[9] PostNL zoekt Postbezorgers (M/V) in eigen wijk in Woerden of omgeving: http://t.co/mndPG6bU #vacature #postbode #baan #bijbaan #werk
EN: PostNL is looking for mailmen (M/F) in own district in Woerden or surroundings (hashtags: vacancy, mailman, job, work)

[10] Sales Representative Retail http://t.co/E9BXmnWZ #werk #werken #banen #baan #vacature #vacatures #MBO #HBO #WO #job #jobs #werkzoeken #in
EN: Sales representative retail
(hashtags: work (noun), work (verb), jobs, job, vacancy, vacancies, professional education, higher professional education, university education, job, jobs, looking for work, in)

[11] Milieuopleiding afgerond? Vind je eerste #baan bij EcoFlexx! http://t.co/PCneSzum #Milieu #Bodem #Water #Civiel #RO #Job #Vacature
EN: Environmental training completed? Find your first job with EcoFlexx!
(hashtags: environment, soil, water, civil, spatial planning, job, vacancy)

Especially with #carriere, #jobs and #productie we find that tweets are composed of multiple units, and the units appear to be ordered according to a fixed pattern (although there is some variation among the various users – MonsterBanen, Megajobs, JobshopNL, ...; exs. [12]-[13]). This suggests the use of some kind of template. Our impression is that it may be well possible that the tweets were generated automatically using a database listing the job title, location, company, and job description.

[12] JobshopNL #Jobs ? Toetdeskundige Wiskunde rekenen: De functie Als toetsdeskundige begeleid... http://t.co/RboGj424tT #vacature #carriere
EN: JobsNL ? exam expert Maths arithmetic: The function As exam expert [you] guide ...
(hashtags: Jobs, vacancy, career)

[13] #vacatures #carriere Engineer Sprinklertechniek: OV-Harderwijk, Als ervaren Engineer Sprinklertechniek en... http://t.co/SDvI4DC9 #banen
EN: Engineer Sprinkler technology: OV-Harderwijk, As experienced Engineer Sprinkler technology and ...
(hashtags: #vacancies, career, jobs)

With #carriere we find some tweets that originate from private users. Typically, these are not about employment, but mostly about the computer game fifa (exs. [14]-[15]).

[14] Wejowejo, ik gooi Bony erin, minuut later staat 't 2-0. #carriere #vitesse
EN: I send in Bony, a minute later it's 2-0.
(hashtags: career, vitesse)

[15] Erg zuur hoor... marcelo en Hulk zijn geblesseerd geraakt #psv #carriere #fifa13
EN: quite a shame ... marcelo and Hulk got injured
(hashtags: psv, career, fifa13)

As we noted above, the overall proportion of clipped units in this cluster is substantial: 20.1%. The proportions for #carriere, #jobs and #productie can be said to be comparable (23.8%, 18.2% and 25.2% resp.), while for #baan it is only 7.8%.
<table><caption>Table 5: Linguistic syntactic structure: Employment cluster. Figures denote the percentage of parse units that were assigned these labels. In each column the highest and lowest percentages are indicated using bold face type (underlined and italics respectively).</caption><thead><tr><th>Hashtag</th><th>S decl)</th><th>S decl,ellip)</th><th>S (int)</th><th>S (imp)</th><th>phrase</th></tr></thead><tbody><tr><td>#baan</td><td>11.0</td><td>6.7</td><td>4.3</td><td>7.9</td><td>58.5</td></tr><tr><td>#carriere</td><td>4.9</td><td>3.8</td><td>1.1</td><td>0.0</td><td>63.0</td></tr><tr><td>#jobs</td><td>1.8</td><td>1.8</td><td>1.1</td><td>0.4</td><td>74.7</td></tr><tr><td>#productie</td><td>3.4</td><td>1.2</td><td>0.0</td><td>0.3</td><td>68.9</td></tr></tbody></table>

**Politics.** In the politics cluster we (perhaps naively) expected to find tweets related to political parties, elections, political issues, etc. For #CDA this is mostly the case. Many tweets appear to originate from professional users (e.g. PartijNieuws, CDA Krant; exs. [16]-[17]).

[16] #CDA D66, VVD en CDA voor extra geld voor Strafhof – Eindhovens Dagblad http://t.co/jQunEwY0

EN: D66, VVD and CDA pro extra funds for Judicial Court — Eindhovens Dagblad<fnref n="14" />
(hashtag: CDA)

[17] #CDA Buma (CDA) sluit samenwerking met PVV uit: DEN HAAG — Het CDA zal na de verkiezingen van 12 september niet ... http://t.co/9ss9gAB3

EN: Buma excludes collaboration with PVV: The Hague -- After the elections of September 12th the CDA will not ... (hashtag: CDA)

However, there are also tweets that are only marginally related to politics. This is the case, for example, for the user VakmensenNL, that uses #CDA merely to be found by search engines. A tweet from VakmensenNL typically has two units, the first of which is an interrogative sentence, the second an imperative, as in examples [18] and [19].<fnref n="15" />

[18] Stem u op #CDA? Kijk dan bij de #Voedingsvoorlichter - #verkiezingen - http://t.co/2Y099s6ZZ1

EN: Are you voting CDA? Look at Nutrition advisor
(hashtags: nutrition advisor, elections)

[19] Stem u op #CDA? Kijk dan bij de #Apotheker - #stemmen - http://t.co/c7EgtTsa

EN: Are you voting CDA? Look at Chemist's
(hashtag: chemist's, vote)

The other hashtags in this cluster are even more noisy:

- 30/100 tweets with #Europe are not about politics at all. They are about a variety of topics: football, traveling, sales, etc.

<footnote n="14">¹⁴ D66, VVD, CDA, and PVV are Dutch political parties. Buma is a Dutch politician. Eindhovens Dagblad is a daily newspaper.</footnote>

<footnote n="15">¹⁵ This may well explain the even for the politics cluster large proportions of interrogative and imperative sentences.</footnote>
<table><caption>Table 6: **Linguistic syntactic structure: Politics cluster.** Figures denote the percentage of parse units that were assigned these labels. In each column the highest and lowest percentages are indicated using bold face type (underlined and italics respectively).</caption><thead><tr><th>Hashtag</th><th>S decl)</th><th>S decl,ellip)</th><th>S (int)</th><th>S (imp)</th><th>phrase</th></tr></thead><tbody><tr><td>#CDA</td><td>26.6</td><td>15.0</td><td>16.4</td><td>11.1</td><td>14.5</td></tr><tr><td>#crisis</td><td>33.0</td><td>15.1</td><td>8.9</td><td>4.5</td><td>16.2</td></tr><tr><td>#Europa</td><td>40.4</td><td>13.2</td><td>9.8</td><td>3.8</td><td>17.4</td></tr><tr><td>#euro</td><td>39.3</td><td>18.5</td><td>5.2</td><td>5.2</td><td>18.5</td></tr></tbody></table>

- about 1 in 5 tweets with #euro are not about the euro as a political issue, but are rather about promotional actions and sales offers (ex. [20]).

[20] #deal #www Doe mee met deze #actie en maak #kans op 150 #euro #tank tegoed http://t.co/nfdiSFZfb7 #prijsvraag.

EN: Participate in this promotion and stand a chance to win 150 euro worth of petrol (hashtags: deal, www, promotion, chance, euro, tank, contest)

- although the majority of tweets with #crisis is about the economic crisis (exs. [21]-[22]), there are also a substantial number of tweets about some 'crisis' in someone's personal life (exs. [23]-[24]):

[21] 'Meer duurzame woningen bouwen om crisis te lijf te gaan' http://t.co/9I5oMm9l #duurzaamheid #crisis #woningbouw

EN: 'Build more sustainable houses to fight crisis' (hashtags: sustainability, crisis, building houses)

[22] #crisis #nieuws: EU: Nederlands tekort 3,6 procent, economie krimpt met 0,6 procent http://t.co/S0hjDocuCw

EN: EU: Dutch deficit 3.6 per cent, economy shrinks with 0.6 per cent (hashtags: crisis, news)

[23] @gijsvofoto Kat naareden uitrechtd voor operatie aan pootje? Dat meen je niet! #crisis

EN: Cat to Utrecht for operation on leg? You're kidding! (hashtag: crisis)

[24] Weeh ik kan me niet meer vinden? #crisis

EN: Sob I can't find my blackberry anymore? (hashtag: crisis)

**School.** The tweets in this cluster reflect the use of the medium as a means for high school kids to interact with their peers. The communication is typically bi-directional as they are sharing information about what they are doing and voicing opinions, but also from time to time asking questions (e.g. about what homework assignments they have got) and inviting responses from their mates. There are many short exchanges, but from time to time there are also longer tweets which then typically are very dense, packed with information, and highly elliptical (cf. exs. [25]-[28]).
<table><caption>Table 7: Linguistic syntactic structure: Politics cluster. Figures denote the percentage of parse units that were assigned these labels. In each column the highest and lowest percentages are indicated using bold face type (underlined and italics respectively).</caption><thead><tr><th>Hashtag</th><th>S decl)</th><th>S decl,ellip)</th><th>S (int)</th><th>S (imp)</th><th>phrase</th></tr></thead><tbody><tr><td>#ak</td><td>23.5</td><td>40.3</td><td>14.3</td><td>0.8</td><td>8.4</td></tr><tr><td>#huiswerk</td><td>28.0</td><td>49.2</td><td>3.8</td><td>0.8</td><td>8.3</td></tr><tr><td>#hw</td><td>13.5</td><td>52.9</td><td>2.6</td><td>0.6</td><td>14.2</td></tr><tr><td>#wrts</td><td>15.3</td><td>59.1</td><td>5.1</td><td>0.0</td><td>8.0</td></tr></tbody></table>

[25] Wiskunde maken, ofso ? #huiswerk
EN: Do maths or something?
(hashtag: homework)

[26] Pff kut techniek #huiswerk
EN: Pff damn technology
(hashtag: homework)

[27] @sannelepoutre ooh oke superleuk! ja dat is minder. is het veel dan? ik moet ook leren in de vakantie anders ken ik 't niet ... #ak
EN: @sannelepoutre oh okay supercool! yes that sucks. is it much then? I also have to study during the holidays otherwise I won't know it ...
(hashtag: geography)

[28] En weer thuis. Lekker gesport vandaag. Woensdag is de dag van vele uurtjes. Zometeen verslag vertalen. Duits & Nederlands #huiswerk
EN: And home again. Great work out today. Wednesday is the day of many hours. In a minute translate report. German & Dutch
(hashtag: homework)

When we look at the variation within the cluster (cf. Table 7), we see that what was already predicted by the U-scores (cf. Section 4) is confirmed: with #huiswerk and #ak (both relatively high U-scores) more traditional structures are found, whereas with #hw and #wrts (relatively low U-scores) there is indeed frequent use of reduced or otherwise deviating structures.

In this cluster the large proportion of interrogative sentences found with #ak and of phrases with #hw beg an explanation. The hashtag #ak is the only one in our selection that represents an actual school subject. The interrogative sentences are mostly of two types, connected to this fact. First there are questions about which material was discussed and what homework needs to be done (exs. [29]-[30]).

[29] moet je de schaal van richter kennen ? #ak #tk2
EN: Do we have to know Richter's scale?
(hashtags: geography, tk2)

[30] Tot hoe ver moet je lezen #ak ?
EN: Up to what point do you have to read ?
(hashtag: geography)

Secondly, there are questions about the material itself, such as the meaning of specific
terms (ex. [31]).

[31] Wat zijn lithosferische platen? #ak
EN: What are lithospheric plates?
(hashtag: geography)

In the case of #hw what we observe is that this hashtag is quite noisy: apart from the
school-related texts we find many news items from a local weekly newspaper (viz. Harener
Weekblad). Many of the phrases (14.2%) are in fact the name of a location, such as Groningen
in the examples below (exs. [32]-[33]).

[32] #hw Politie onderzoekt twee straatroven – GRONINGEN – De politie stelt een onderzoek in naar twee straatroven ... http://ow.ly/1ea52v
EN: Police investigates two street robberies – GRONINGEN – The police are investigating two street robberies...

[33] #hw Man zwaar gewond bij schietincident – GRONINGEN – Een 24-jarige man uit Paterswolde is dinsdagavond op de ... http://ow.ly/1e40KR
EN: Man severely injured during shooting incident – GRONINGEN – Tuesday night a 24-year-old man from Paterswolde was on the

7 Conclusion

In this paper we set out to investigate the language used on Twitter, with a focus on syntax.
We wanted to see to what extent it varies and how it relates to the standard language. The
main findings of the exploratory research reported here can be summarized as follows:

- After clustering a collection of tweets on the basis of users and hashtags, and closer inspection of four of the resulting clusters, we found that each of the four clusters indeed bears characteristics that set it apart from the other clusters. As was already hinted at by the U-scores, the clusters exhibit variation in language use which is also manifest in the linguistic structures that are used. A tentative explanation for the variation *between* the clusters is that with clusters E and P the language use is typically informative, whereas in the A and S clusters it is highly interactional. This, however, needs further investigation.

- There is variation *within* clusters. The variation is often related to the specific topics discussed with the various hashtags and/or to specific users or user groups. Also, part of the variation could be explained through the noisiness of the hashtags, which played a role for some hashtags more than for other ones.

- Language use in terms of syntactic constructions is quite regular: it essentially makes use of the same inventory of constructions that we find in standard language; to the extent that Twitter tribal languages differ from other varieties, the difference is in the frequency and distribution of various constructions.
In this paper the focus has been mainly on the composition of tweets in terms of parse units and their syntactic structure. There is much more to be said about the use of dependent clauses and the structure of phrases. For example, dependent clauses appear to be heavily underused when compared to more conventional text types. To the extent that they do occur, it is mostly as adverbial or direct object. Also, while annotating the parse units, we observed that frequently in noun phrases determiners are omitted and postmodification is much simplified or lacking altogether. These are all indications that the language is being adapted to the situational context, not least the Twitter medium.

As for the feasibility of a (rule-based) syntactic parser for Dutch tweets, based on what we have seen in this exploratory study we believe that the development of such a parser is very well possible. The grammar rules by themselves can be derived directly from existing grammars for standard written language, as long as these include proper treatment of ellipsis. If a probabilistic component is used for disambiguation, probabilities would best be recalculated per tribe, but this is still doable. The main problem for the parsing lies in the steps that precede the syntactic analysis, especially for the more informally and intimately tweeting tribes, namely dealing with the deviations from standard use of punctuation and extensive spelling variation.
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## A Clusters

Below an overview is given of the composition of each of the four clusters that feature in the present study. The selective hashtags that we subjected to further analysis are marked in bold face type.

In figures A.1 to A.4 excerpts of the dendrogram for the cluster structure are shown. Each figure visualizes the composition of one of the four clusters, including only the hashtags selected for annotation. The top node represents the point where the cluster joins another node in the dendrogram. The uninterrupted lines indicate a direct connection; the dashed lines indicate an indirect connection, while the hashtag dominated by a dashed line is one of a group of hashtags that cluster at that point.

<table><caption>Table A.1: Appreciation (cluster of 74 hashtags; Figure A.1).</caption><tr><td>#apart</td><td>#gezellig</td><td>#kuch</td><td>#spannend</td></tr><tr><td>#au</td><td>#gezelligheid</td><td>#lastig</td><td>#stress</td></tr><tr><td>#balen</td><td>#ghehe</td><td>#lekker</td><td>#sweet</td></tr><tr><td>#bedankt</td><td>#goedidee</td><td>#lekkerdan</td><td>#thanks</td></tr><tr><td>#beetjejammer</td><td>#grr</td><td>#leuk</td><td>#triest</td></tr><tr><td>#benbenieuwd</td><td>#grrr</td><td>#lief</td><td>#trots</td></tr><tr><td>#blegh</td><td>#handig</td><td>#liefde</td><td>#vermoeiend</td></tr><tr><td>#blij</td><td>#heerlijk</td><td>#logisch</td><td>#verslaafd</td></tr><tr><td>#blond</td><td>#hehe</td><td>#lovely</td><td>#vervelend</td></tr><tr><td>#brr</td><td>#helaas</td><td>#medelijden</td><td>#vreemd</td></tr><tr><td>#cute</td><td>#huilen</td><td>#nerd</td><td>#vreselijk</td></tr><tr><td>#dilemma</td><td>#jaja</td><td>#nieteerlijk</td><td>#woehoe</td></tr><tr><td>#drama</td><td>#jammer</td><td>#oneerlijk</td><td>#zinin</td></tr><tr><td>#druk</td><td>#jammerdan</td><td>#onzin</td><td>#zucht</td></tr><tr><td>#feit</td><td>#jammie</td><td>#pfoe</td><td></td></tr><tr><td>#gaap</td><td>#jippie</td><td>#pijnlijk</td><td></td></tr><tr><td>#gatver</td><td>#joepie</td><td>#please</td><td></td></tr><tr><td>#geenidee</td><td>#jummie</td><td>#scary</td><td></td></tr><tr><td>#gemeen</td><td>#kansloos</td><td>#schaam</td><td></td></tr><tr><td>#genieten</td><td>#kots</td><td>#snik</td><td></td></tr></table>
<figure><img src="image_46.png" /><figcaption>Fig. A.1: Excerpt of the dendrogram showing the selected hashtags from the ‘appreciation’ cluster.</figcaption></figure>

<table><caption>Table A.2: Employment (cluster of 21 hashtags; Figure A.2).</caption><tr><td>#baan</td><td>#job</td><td>#productie</td><td>#vacature</td></tr><tr><td>#banen</td><td>#Job</td><td>#Sales</td><td>#vacatures</td></tr><tr><td>#carriere</td><td>#Jobs</td><td>#sales</td><td>#VCW</td></tr><tr><td>#CV</td><td>#jobs</td><td>#uitzend</td><td></td></tr><tr><td>#fulltime</td><td>#MBO</td><td>#UZB</td><td></td></tr><tr><td>#HBO</td><td>#Milieu</td><td>#Vacature</td><td></td></tr></table>

<figure><img src="image_47.png" /><figcaption>Fig. A.2: Excerpt of the dendrogram showing the selected hashtags from the ‘employment’ cluster.</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>Fig. A.4: Excerpt of the dendrogram showing the selected hashtags from the ‘school’ cluster.</figcaption></figure>
<table><caption>Table A.3: **Politics** (cluster of 32 hashtags; Figure A.3).</caption><tr><td>#CDA</td><td>#Europa</td><td>#PVDA</td><td>#SP</td></tr><tr><td>#cda</td><td>#FNV</td><td>#PvdA</td><td>#tk2012</td></tr><tr><td>#crisis</td><td>#GL</td><td>#PvdD</td><td>#TK2012</td></tr><tr><td>#CU</td><td>#Griekenland</td><td>#PVV</td><td>#verkiezingen</td></tr><tr><td>#d66</td><td>#groenlinks</td><td>#pvv</td><td>#VVD</td></tr><tr><td>#D66</td><td>#Groenlinks</td><td>#rutte</td><td>#vvd</td></tr><tr><td>#EU</td><td>#pensioen</td><td>#SGP</td><td>#watkiestnl</td></tr><tr><td>#euro</td><td>#pvda</td><td>#sp</td><td>#wilders</td></tr></table>

<figure><img src="image_49.png" /><figcaption>Fig. A.3: Excerpt of the dendrogram showing the selected hashtags from the ‘politics’ cluster.</figcaption></figure>

<table><caption>Table A.4: **School** (cluster of 18 hashtags; Figure A.4).</caption><tr><td>#ak</td><td>#frans</td><td>#leren</td><td>#sms</td></tr><tr><td>#bio</td><td>#gs</td><td>#nederlands</td><td>#wiskunde</td></tr><tr><td>#dm</td><td>#huiswerk</td><td>#nl</td><td>#wrts</td></tr><tr><td>#duits</td><td>#hw</td><td>#pww</td><td></td></tr><tr><td>#engels</td><td>#ipod</td><td>#skype</td><td></td></tr></table>

## B Annotation scheme

Below we repeat the annotation scheme that we presented in Table 2, this time with examples. Most examples were encountered as original tweets; occasionally an example did not constitute a tweet by itself but was identified as one of multiple parse units making up a tweet.
<table><thead><tr><td>label</td><td>denotation</td></tr></thead><tbody><tr><td>coordS</td><td>coordinated sentences<br/>Wow k loop nr buiten en daar staat een fels malibu :$ :p #apart<br/>EN: Wow I walk outside and there is a bottle of Malibu :$ :p #strange</td></tr><tr><td>juxtS</td><td>juxtaposed sentences<br/>ik dnek wanneer begint mn liedje nou, staat mn geluid uit ! #blond ?<br/>EN: I think when will my song start, my sound is muted ! #notverysmart ?</td></tr><tr><td>(decl)</td><td>declarative sentence<br/>#SPnl #Europa - @DdJong: Europese Commissie wil wurgcontracten opleggen aan lidstaten: Phttp://t.co/w6oahBQc60 via @spnl<br/>EN: #SPnl #Europe - @DdJong: European Commission wants to impose killer contracts on member states http://t.co/w6oahBQc60 via @spnl</td></tr><tr><td>S(decl,-su)</td><td>declarative sentence with subject missing<br/>ben echt bang voor morgen #ak #laatste #test<br/>EN: am really afraid for tomorrow #geography #last #test</td></tr><tr><td>S(decl,-su,-aux)</td><td>declarative sentence with subject and auxiliary missing<br/>filmpje over aardbevingen kijken #ak<br/>EN: watch film about earthquakes #geography</td></tr><tr><td>S(decl,-su,-cop)</td><td>declarative sentence with subject and copular verb missing<br/>ng bij me neefje thuis #gezellig<br/>EN: still at my cousin's #enjoyable</td></tr><tr><td>S(decl,-su,-v)</td><td>declarative sentence with subject and verb missing<br/>niks te doen deze zaterdagavond #jammer<br/>EN: nothing to do this Saturday evening #pity</td></tr><tr><td>S(decl,-v)</td><td>declarative sentence with verb missing<br/>Een sp- stemmer en een pvv-er samen op 1 bank, lol<br/>EN: An SP voter and PVV supporter together on one couch, lol</td></tr><tr><td>S(decl,-od)</td><td>declarative sentence with direct object missing<br/>@RedCapMusic Ik weet niet man<br/>EN: @RedCapMusic I don't know man</td></tr><tr><td>S(int)</td><td>interrogative sentence<br/>Wat is er nodig om #positiefjeugdbeleid méér als basis te krijgen voor lokaal beleid en decentralisatie #peerlearning #Europa<br/>EN: What is needed to get #positiveyouthpolicy as basis for local policy and decentralisation #peerlearning #Europe</td></tr><tr><td>S(int,-su,-aux)</td><td>interrogative sentence with subject and auxiliary missing<br/>Misschien gewoon om de beurt in deeltijd?<br/>EN: Maybe just one at a time parttime?</td></tr><tr><td>S(int,-cop)</td><td>interrogative sentence with copular verb missing<br/>@Miekje25 iemand met reservesleutel in de buurt? #blond<br/>EN: @Miekje25 anyone around with a spare key? #notverysmart</td></tr></tbody></table>
<table><thead><tr><th>label</th><th>denotation</th></tr></thead><tbody><tr><td>S decl/int</td><td>declarative or interrogative sentence (undecided)<br>ik heb mijn tv aangezet in mijn slaap?!?!?</td></tr><tr><td>S (excl)</td><td>exclamatory sentence<br>Wat doet/acteert die Peetoom vreselijk overdreven!<br>EN: I have turned on my tv in my sleep?!?!?</td></tr><tr><td>S (imp)</td><td>imperative sentence<br>Lees het gehele artikel trouwens hier: http://t.co/2gSg8yQx #Krugman #euro<br>EN: Read the full article here by the way: http://t.co/2gSg8yQx #Krugman #euro</td></tr><tr><td>CL</td><td>clause<br>@RonKas1 @rjvanhouten @adresonbekend als het programma onder-<br>tussen dan maar niet is wegbezuinigd #crisis<br>EN: @RonKas1 @rjvanhouten @adresonbekend If only the programme<br>meanwhile has not been stricken off because of financial cutbacks<br>#crisis</td></tr><tr><td>NP</td><td>noun phrase<br>Nieuw Blog Bericht<br>EN: New blog post</td></tr><tr><td>AJP</td><td>adjective phrase<br>suupergoooooed!<br>EN: fabulous!</td></tr><tr><td>AVP</td><td>adverb phrase<br>thuis #hw ;s<br>EN: home #homework ;s</td></tr><tr><td>PP</td><td>prepositional phrase<br>Net als die andere: #PvdA<br>EN: Just like the other: #PvdA</td></tr><tr><td>VP</td><td>verb phrase<br>leren #ak #twexit<br>EN: study #geography #twexit</td></tr><tr><td>DISC</td><td>discourse item (word or phrase)<br>Huh?<br>EN: Eh?</td></tr><tr><td>CLIPPED</td><td>clipped unit<br>Ombouwen en afstellen van afv... http://t.co/oVHcCU6Z #techniek<br>#productie<br>EN: Converting and tuning of (incomplete)... http://t.co/oVHcCU6Z<br>#technology #production</td></tr><tr><td>REST</td><td>other (none of the above)<br>hoppa!<br>EN: hoppa!</td></tr></tbody></table>
# *TweetCommeUneFille*: Twitter as giveaway for stereotyping speech acts

Camille Lagarde-Belleville and Michel Otell

Department of Language Sciences

Praxiling UMR 5267-CNRS

Paul Valéry University

Montpellier, France

**Keywords:** Discourse Analysis, Cognitive Semantics, Gender Stereotypes, Categorisation, Digital Utterance.

# 1 Introduction

This work aims to tackle a corpus stemming from Twitter, a social media that is representative of emerging trends in speech. The corpus here to be studied was selected and gathered through a keyword-like tagging device: the hashtag. In 2013, Marie-Anne Paveau referred to hashtags as *technomorphemes* that enable Twitter users to tag their tweets for online use.

As a conventional utterance, “#TweetComme” (“#TweetLike”) creates an overassertion inside the discourse. The latter is host to gender considerations when phrases such as ““#TweetCommeUneFille” (“#TweetLikeAGirl”) or “#TweetCommeUnMec” (“#TweetLikeABoy”) are produced. “Fille” and “Mec” work as the archetypal pair. Such a use of categorising keywords fits into a binary drift rendering either an auto-representation of one’s own gender, or a hetero-representation of the other gender by the enunciating subject. By such a statement, the subject makes a prototype model of general (or virtual) scope out of *une fille* or *un mec*.

We will see how by over-asserting one's tweet with "#TweetComme", one works actively to make the image of a stereotyped speaker -fille (a girl) or mec (a boy, a guy, a dude) likely to produce or to have produced said over-asserted speech- surface into the other Twitter users' minds. General tendencies will then be extracted to show how uttered clichés are associated to paradigms that are presented as gender-dependent: sports, alcohol, sex, etc. A sense of community-building activity comes to sight with Twitter as a social media becoming a hub from which are propagated collections of discourses which reception is oriented by the categorizing selection made through the hashtag.

# 2 About the corpus: why "#TweetCommeUnMec" and "#TweetCommeUneFille"?

By way of overture, it is important to list the parameters that allow defining the outline of the environment in which the studied speeches were produced. It is after all their social and technological bosom in many ways, with all implied constraints and potentialities.
## 2.1 Twitter: a micro-blogging platform where discourse is embedded in a maximum of 140 characters

Twitter is a micro-blogging platform where discourse is embedded in a maximum of 140 characters. It was created in 2006. Its core principle is for users to answer the question *What are you doing?* Just like any other speech, tweets host their author's discursive ethos: one's self-image painted in the way in which one intends to be seen. In 2013, Longhi outlined that said discursive ethos could be echoed by a *technodiscursive* ethos that can be produced y the means of the platform's technical jargon. Longhi also suggested a third dimension for self-image producing: the *pre-technodiscursive* ethos. The latter is produced when background elements ("*des arrière plans*") are embedded into speech with the use of *prédiscours* that expand various elements (logical and semantic) that are preliminary to the utterance. We use the French word *prédiscours* as defined by Marie-Anne Paveau in her 2006 book *Les Prédiscours. Sens, mémoire, cognition*. For clarity purposes, we will not yet try to translate the word so soon in this article, as this endeavour would most probably interfere with previously attested terminology in academic literature in English. In this case, we are referring to pre-established, well spread speeches that hold a cognitive operation of categorization approved by *virtually* all members of the linguistic community from which said speech is produced. In other words a commonplace thought, a stereotype. Hashtags can be designed as tools to enact such pre-technodiscursive ethos in a given speech.

Also, as the social media it is, Twitter is often used to spread community-building injunctions, i.e. to urge members of a given community to collude in some way.

## 2.2 The hashtag: a tool for referencing tweets with keyWord-dependent tags

In Twitter's restrained enunciation context, the speaker's goal is to enhance his tweet's visibility by assigning a hashtag to it that will both tag it (or reference it) and categorise it on the WorlWide Web. As Paveau puts it, the hashtag is a "linguistic item, which function is essentially social, that allows ambient affiliation of users, techno-conversationallity and searchability of discourse" ("*forme langagière dont la fonction est essentiellement sociale, permettant l'affiliation diffuse* (ambient affiliation) *des usagers, la technoconversationnalité et l'investigabilité* (searchability) *du discours* ").

In addition to its referencing role, the hashtag embodies a cognitive operation that brings it epistemologically close to Sacks' *categorisation device* (2000). Auto-representation of one's own gender (ex.: "*C'est sur quelle chaîne la cérémonie des Ballons d'or please ? Ils sont tellement beaux les footballeurs en costards #TweetCommeUneFille*" ("On which channel is the Ballons d'or ceremony broadcasted please? Soccer players in tux' are so handsome #TweetLikeAGirl") – female twitto), hetero-representation of the other gender by the enunciating subject (ex.: "*C'est trop nul, ce soir les garçons vont parler que de foot #tweetcommeunefille*" ("Such a shame, tonight boys will only talk about soccer #TweetLikeAGirl") – male twitto).

## 2.3 #TweetCommeUnMec vs. #TweetCommeUneFille: an archetypal couple

The reason why the *fille/mec* opposition was selected for this study is it appears to be the most representative occurrence of the discursive activity of gender-discriminating categori-
sation in the french-speaking community. In the over-assertion activity materialised in the technomorpheme “#TweetComme + male categorising item”, the tag “mec” (dude, lad) was most productive, more than “garçon” (boy) or “homme” (man). The same goes for the female categorising item “fille”, more often used than “femme” (woman), “nana” (chick) or “meuf” (very common verlan slang version of femme).

This shows users' preference for the opposition fille/mec to embody gender-discriminating categorisation.

In a more semantic view, the pairing of fille with mec operates a semantic setting of both words that gives rise to quite a precise, narrow referential range. The etymological dictionary of French language *Le Robert historique de la langue française* rebuts any historical link between mec and mac (short for *maquereau* (mackerel, “pimp”)), or with mag, that would have linked it to the Latin mega meaning large. Le Robert describes the origins of mec as “obscure”. Nevertheless, it is in the 19th century that mec made its way into French slang (argot). First meaning “a strenuous, vigorous, leader of a man”, it eventually referred to any male individual. Later on, mec was defined by opposition to nana (chick), and its actual synonyms are gars, type or homme (lad, dude, fellow, chap, bloke, man or individual): a broad sense, but with a pejorative twist. As for fille, when paired up with garçon (boy), both fille and garçon are then to understand in their general sense of boy and girl. But paired up with mec, fille takes another colour now holding the sense of prostitute in potentiality.

# 3 A few words on our theoretical background

Here we will try to give a brief outline of what we mean by *stereotype* and perhaps even more about *stereotyping* as a social activity embedded into language use.

## 3.1 The praxis

The theoretical core of our approach is Praxematics. It is in *Introduction à l'analyse textuelle* by Lafont and Gardès-Madray, in 1976, that the word Praxematics (“*la praxématique*”, in French) first arose. It names a branch stemming from Discourse Analysis, in the field of Language Sciences. Praxematic discourse analysis was first developed in the late 1970's, early 1980's by Lafont, Gardès-Madray and Siblot (*Le travail et la langue*, Lafont, 1978; *Pratiques praxématiques*, Lafont, Gardès-Madray, Siblot, 1983), and is today mainly expanded through the journal *Cahiers de praxématique* and in the works of the laboratory *Praxiling* at Paul Valéry University in Montpellier (Détrie et al., 2001).

This sprout in discourse analysis focuses its attention on natural corpora; i.e. naturally produced discourses, spontaneously uttered prior to investigation, rather than induced speech or selected fragments. It puts the subject's experience of the world and his relationship with reality at the basis of the linguistic representation of the world. Praxematics developed a conceptual tool to better depict its model for sense production in discourse: the praxeme. (Lafont, Gardès-Madray 1976/1989, p. 99, in Détrie et al. 2001, p. 263) “Nous appellerons praxèmes les outils de praxis linguistique qui permettent le repérage de l'analyse du réel objectif par l'homme et spécialement le repérage des autres praxis.”
This *praxis*, borrowed from Philosophy and strongly influenced by Phenomenology, represents the cognitive processes that are nourished by past experiences –both perceptive and practical–, and that are motivated by intention-bound relations, and that result in sense (Détrie et al., 2001, p. 266, translated by us). In Détrie’s words (2001), the praxeme is “[l’]unité lexicale [prise] au moment de son actualisation discursive, mais aussi en tant qu’outil linguistique porteur de potentialités signifiantes (capitalisées en langue) qui résultent (et reflètent) des savoirs acquis sur l’univers extralinguistique grâce aux praxis humaines.”

Moreover, and as early as the 1970's, this cluster of linguistic practices made it a major point that the context of production be given an essential role in all analysis.

It also borrowed to Gustave Guillaume a conceptual tool that allows it to look at the processes at stake in meaning production in language. Guillaume referred to said concept as the process of *actualisation*. In this new context, the word refers to the crossing from the *to be said* stage (“l’à-dire”) to the *saying* stage (“le dire”) and finally to the *said* stage (“le dit”). We are talking here of the changes in nature that occur when what the subject intends to say goes from the state of pre-language magma through the enunciation operation and to a speech-product. In other words, actualisation means the crossing from language (“la langue”) to speech (“la parole”).

This contribution allows us to clearly turn away from the diktat of interpretation, and decisively focus our attention on the emergence of meaning.

## 3.2 The dialectic of the same and the other

The Dialectic relationship between *Same* and *Other* hinges on two philosophical concepts, the *Idem* and the *Alliud*, that follow from the binary sorting that operates in the awareness process at stake as the subject partitions the world in order to categorise it more sharply (“[le] tri binaire qui s’opère au niveau du découpage du réel”) (Détrie, Masson, Verine, 1998 : 44-45). That this very movement between Same and Other is reflective of the construction of self-identity — refered to as the *Self* (or *Ipse*) – also makes it an access-point to the speaker’s subjectivity.

To Jeanne-Marie Barbéris (1998 : 30), this linkage between *Ipse*, *Idem* and *Alliud* (*Self*, Same and Other) is as essential anthropologically as it is in terms of systemic representations.

In other words, if the analysis is about the to be said stage, the choosing of the right words, then the speaker’s lexical selection is made through the generic cognitive operation that says *x* is not *y*, and *y* is not *x*: inclusion of Same, exclusion of Other.

## 3.3 About dialogism in nomination

The words we use are all second hand. Others who have proceeded to their own semantic tunings have already said them all. Those tunings themselves have sometimes been endorsed by the linguistic community (French-speaking in our case). We merely recycle others’ discourses for the imperious purpose of being understood. This is the dialogism of nomination. Past uses of words and phrases have each time hosted their speaker’s motivations. Those past uses eventually sediment their meaning programs. Those meaning programs then become available in the language in question, capitalised for other speakers’ later use.
## 3.4 Ready-made in language

The image of a general movement of capitalisation in language is probably what best describes how ready-made can be found embedded in speech. French Discourse Analysis calls this *le préconstruit*. We will venture to use the English word *preconceived* for this article. As shown, what is preconceived in terms of meaning production calls on what has been said, or prior discourses. From now on, we will use the phrase *prior discourses* or virtual prior discourses also as a transposition of the French *prédiscours* (see 2.1.). In language, ready-made works like a massive volume of layers and layers of settled meaning programs that were once hosted by lexicon. Albeit settled, this volume is fluid and shifting: some usages disappear, some develop, all are bound to evolve. The *preconceived* can thus be seen as both the entry and the exit point of lexicalisation processes.

## 3.5 Lexicalisation

In his use of words, the speaker gives them a patina that, after being put through other discourses of other speakers, will potentially disseminate in all the linguistic community. This is lexicalisation. The *preconceived* weighs on the discourse, and the latter, by lexicalising by chunks (phrases), enriches and yet trends the former.

## 3.6 Folk wisdom

The meaning programs that are under sedimentation into the layered volume of the pre-
conceived hold the precursors for what is referred to as *folk wisdom, conventional phrases, buzzwords, clichés, collective unthinking, collective psyche, stereotypes, ...*. They stay in use as
long as they are contained in the *preconceived* layers.

This can happen locally, at the interaction level: the *preconceived* then calls upon the
interlocutors' conversational history. At the level of the linguistic community, the *preconceived*
calls upon collective history.

## 3.7 Praxical patterns (or praxical schemes)

In order to use the praxematical terminology earlier presented in this paper, let us simply
point out that this *preconceived* layering are what we call praxical patterns ("*schémas praxiques*" in Détrie’s work). They are a sum of socio-experiential patterns that are shared
and thus endorsed by a linguistic community, and that underpin their language’s way of
partitioning their world (see 3.2.).

## 3.8 Value-oriented stereotyping systems

This conceptual *millefeuille* that has been unwound notion after notion, leads to the idea that
our chosen corpus of study conveys stereotypes that work in highly value-oriented systems.

This figure works as a model in the sense that it is meant to simplify general observations
as well as to connect them to the conceptual framework earlier exposed.
<figure><img src="image_50.png" /><figcaption>Fig. 1: Value-oriented stereotype systems.</figcaption></figure>

Assuming that men dominate women, men produce positive representations of men and negative representations of women. In reaction, women produce positive representations of women and negative representations of men. Yet, as stated, men dominate women, therefore men-produced stereotypes dominate women-produced stereotypes; so there is no such thing as symmetry here. Men are impervious to women-produced stereotypes whereas women do tend to make men-produced stereotypes their own. As said, men produce positive representations of men and negative representations of women, women are influenced by ambivalent representations of themselves and positive representations of men: $\sigma \to (\sigma^+\sigma^-)$, $\sigma^- \to (\sigma^-\sigma^+)$; $\sigma^+ > \sigma^-$.

# 4 Tendencies from our corpus

In this section, a few graphs show general statistical tendencies out of our observations of 1571 tweets. As said earlier, tweets were collected using both hashtags \#TweetCommeUneFille and \#TweetCommeUnMec, applied to all tweets published since the birth of Twitter in 2006 and still available at the time of the study.

## 4.1 Sexuality and Relationship

It appears that Twitter users put *relationship* status and romance in the girls' dominion. To a lesser extent, motherhood is also in the girl area as is *feminism*. In the guys' dominion are *infidelity*, *boorishness* and *singlehood*.

The global stereotype at stake is that women thrive in relationships – with its corollary romance and motherhood–, whereas men do not care for such questions.

Regarding sexuality, men are most associated with intercourse and sexual attraction, then to *homophobia*, *flirting*, *penis size*, and *street harassment*; whereas girls are less represented by their sexual life.

The global stereotype brings up a hypersexual man and a woman who gives much less importance to her sexual life.
<figure><img src="image_51.png" /><figcaption>Fig. 2: Number of tweets linking gender to couple topics.</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>Fig. 3: Number of tweets linking gender to sexual topics.</figcaption></figure>

## 4.2 Sports and video games

Men are massively represented as sport connoisseurs, whereas women are strongly depicted as ignorant in sport or even with a heinous point of view towards it. The stereotypical man is associated with soccer and workout. Women ignorance first concerns soccer, then rugby and then basketball. It is interesting to see that no tweet actually represents the stereotypical man as a sports ignorant or a sports hater.

The global stereotype at stake is that of a man who is a connoisseur, and a woman who either hates or knows nothing about sports. The sport that is most referred to –all genders
<figure><img src="image_53.png" /><figcaption>Fig. 4: Number of tweets linking gender to sports.</figcaption></figure>

considered– is soccer (*football* in French). For men, the second sport is workout, and for women it is rugby (or the ignorance of rugby!).

<figure><img src="image_54.png" /><figcaption>Fig. 5: Number of tweets linking gender to video games. (Fifa :soccer game, Grand Theft Auto: road violence game, Call of Duty: war game)</figcaption></figure>

Men are more often associated with video games than women. It is very much the case regarding the *Fifa* game (soccer), a little less regarding videogames in general (*Undetermined*), and even less for *GTA* (road violence game). *Call of Duty* –a worldwide best selling video
game- is equally mentioned for men and women. However, the last two games do not count utterances enough to be statistically interpretable.

## 4.3 Culture

<figure><img src="image_55.png" /><figcaption>Fig. 6: Number of tweets linking gender to cultural products.</figcaption></figure>

This time, a clean cut separates the girls from the guys: according to French-speaking Twitter users, the stereotypical man listens to rap, and the stereotypical woman enjoys TV series, reality shows and movies. No tweet links women to rap, and no tweet links men to TV series, reality shows or movies. Those are the only four cultural products represented in our corpus.

## 4.4 Avenues for Reflexion

Overall, among 1571 tweets, 1443 were posted by females, 773 by males, and 26 by Twitter users whose gender was undetermined.

We had 762 #TweetLikeAMan and 809 #TweetLikeAGirl, thus quite a well-balanced ratio.

Men were more likely to use the hashtag #TweetLikeAGirl: 706/67. Girls have a more balanced ratio: 771/672, with a slight tendency towards autostereotyping.

It is very difficult to say if a topic is linked to a gender in a negative or a positive way. For instance, men' infidelity is sometimes valued, sometimes sentenced. Irony and sarcasm are frequent and very easily misconstrued, especially in written speech. It is ever more true with written discourses that are so often decontextualised. This is why it was not possible for us to consider statistically studying positive and negative values linked with gender-oriented stereotypes in this corpus.
"Tranquil contre l'Ukraine, Rio est déjà à nous les gars #TweetCommeUnMec" (♀)

"Demain qui est chaud pour un foot ? #TweetCommeUnMec" (♀)

And if men are gossiping:

"@Simonn_C jtexplique demain gros mdr ca nous occupera en maths #TweetCommeUneFille" (♂)

## 5.2 Hetero- Versus Auto-Representation

In tweets, stereotyping acts are either made in auto-representation or in hetero-representation. The cases we chose to put forward are those that involve the womaniser figure. One tweet is male-produced while the other is female-produced:

"Je t'aime mon cœur :$ Envoyer à vannessa, Mélissa, Caroline, Leila, Lisa, Lucie, Emma.. #TweetCommeUnMec" (♀)

"DemJe t'aime doudou1 doudou2 doudou3 doudou4 #TweetCommeUnMec" (♂)

Whether the Twitter user is female or male, both tweets draw the womaniser as an archetypal representation of the loving man.

## 5.3 Retweets and confrontational conversations

Earlier, we talked about value-oriented stereotyping systems (see 3.8.). Sometimes, other users did detect a pejorative orientation in another user's tweet in our corpus. This led to retweets and commentaries that sometimes developed into confrontational conversations. Interestingly enough, the most angry reactions mostly came from male Twitter users.

"Vous êtes chaud avec #TweetCommeUnMec. Je vais me mettre a faire des #TweetCommeUnePute ma méchanceté atteindra son paroxysme" (♂)

"@____ : #TweetCommeUneMec elle a un boule de ouff mec elle est bonne à niquer ! » Aucun mec ne parle comme sa." (♂)

Reacting male Twitter users are deactivating the stereotype by saying this is one guy talking, not all guys. They are contesting the archetotype.

"Je ne sais pas qui vous fréquentez mais je n'ai jamais dit un seul de vos #TweetCommeUnMec" (♂)

"Depuis tout à l'heure je vois des #TweetCommeUnMec Certain serait plutôt #TweetCommeUnKsos" (♂)
This time, not only is the archetype contested, it is replaced by “ksos” (from “cas social”):
a socially challenged person. It is a recategorisation that is taking place here.

This confrontational reaction is almost exclusively posted by male Twitter users. Female contestation is rather passive in comparison:

“Avec vos hastag « #TweetCommeUneMec » et « #Tweet-CommeUneFille » on en conclus que les gars sont vulgaires et les filles Hypocrite et Chieuse” (♀)

“@MenoJmen : Comme J’suis fat sur cette photo #Tweet-CommeUneFille pic.twitter.com/lqlvYKIT » Salaaaaud ahahaa” (♀)

(Pastiched conflict.)

This illustrates the conclusions of Figure 1, that show how a dominated group tends to abide by the negative stereotypes society produces about it.

“Mddr la meuf elle dit « les bg venez » alors qu’elle ressemble a rien mdr si elle veut des bg faut être un minimum potable #TweetCommeUnMec” (♀)

This tweet states the obligation for female to be good-looking according to male (stereo-typical) desire. Nevertheless, by using the hashtag #TweetLikeAMan it also states male hegemony over women.

In a similar double-talk statement, this last tweet exposes woman servility, and yet one could argue that the (female) Twitter user is most probably speaking her own mind, and then trying to show she is taking some distance from her statement by marking it with this hashtag.

“C’est M D R les filles qui se plaigent h24 sur fb/twitter prsq un mec l’a paqué.. T’avais qu’a être mieux pour lui hyn #TweetCommeUnMec :p” (♀)

## 5.4 Associated Hashtags

There is no doubt that *fille* and *mec* work like an archetypal pair thus making us depict an opposition between a servile archi-woman and a macho archi-man. Yet, although the former is often present in our corpus, the latter only appears once:

“Pouuuaaaaah [Surnom] clash trop ! Vengeance #TweetCommeUnMec ma femme jlui dis fait moi un café elle le FAIT point #Machovaaa” (♀)

Other tweets do depict a misogynistic archi-man, but female endorsement of negative female stereotypes always surfaces in some way:
“J’ai besoin d’une femme la sa devient chaud chez moi qui va
cuisiner le menage tout sa faut jme pose la #TweetComme-
UnMec” (♀)

'Ma femme doit savoir cuisiner. #TweetCommeUnMec La femme en question sait même pas faire des pâtes. (je fais partie de celles là LL )" (♀)

In this last tweet, the female Twitter user is sorry not to fulfill her male partner's cooking expectations.

The housewife figure also comes in other forms:

“J’ai besoin d’une femme la sa devient chaud chez moi qui va
cuisiner le menage tout sa faut jme pose la #TweetComme-
UnMec” (♀)

We see that users do not feel the need to say the words misogynistic or macho. The opposition between #TweetLikeAGuy and #TweetLikeAGirl, after being used and sedimented by the community, is already host to misogynistic and macho views that precipitate into praxical patterns (see 3.7.). This means that the decontextualisation at stake (see 4.4.) comes less from a will to take some distance from the discourse (“This could have been said by x.”), and more from an active generalisation (i.e. stereotyping) movement (“All men are misogynists.”).

“Une fille en jupe n’est pas une pute messieurs mais vous des daleux #TweetCommeUneFille #feministeoulaferte #safedanslarue” (♂)

Referring to the Same and Other dialectical pair (see 3.2.), one can see here that even if this man feels concerned by female harassment, his use of the hashtag #TweetLikeAGirl no matter how compassionate, says that gender oppression remains a girl's concern.

5.5 Metadiscursive tweets and gender supremacy

As we showed, categorisation by these hashtags can start conflicts among Twitter users.
Moreover, we have shown how little general social problems are discussed. The opposition
between girls and boys is often represented in gender-dependent topics, incompatible views
on relationships, and jokes. These very last tweets show a male Twitter user antagonising
other male users over their use of the hashtag #TweetCommeUneFille in rather a violent
way:

“«#TweetCommeUneFille» VOILA UN HASHTAG QUI PERMETTRA AUX MECS FRAGILES DE NOS #TL DE S’EXPRIMER A L’AISE !!!! BANDE DE BOLOSS SODOMISES” (♂)

This is a metadiscursive hashtag in the sense that it is discursively looping towards itself:
“I am talking about the hashtag I am using.”
> “Pour certains garçon Le hashtag #TweetCommeUneFille leurs permet de dire vraiment ce qu’ils pensent des garçons !!” (♂)

What both tweets say is guys shouldn't tweet like girls.

## 6 Conclusion

The study of tweets that are categorised by the hashtags #TweetCommeUneFille and #Tweet-CommeUnMec forces us to think over the question of decontextualisation-recontextualisation of reported speech. The use of those hashtags and the conventional utterances #TweetComme does mean an activity of over-assertion for the user is clearly saying “I am tweeting” (“and I am tweeting that I am tweeting”). Fille and garçon add to this a stereotyping act directed towards the gender opposition between male and female. This generalising movement of thought paired with this over-assertion and produced in a decontextualised mode has been qualified in 2010 as unsubstanciated reported discourse by Bertrand Verine (“discours rapporté non avéré”). Verine later said (2011): “la situation d’énonciation enchâssée est donnée pour effective et définie, mais [...] l’acte d’énonciation rapporté est représenté comme seulement plausible”.

Twitter users use the preconceived --or, as Authier-Revuz puts it “an inter-speaker dialogism of grater range” (““un dialogisme interlocutif large”) (1984)— when they specify that they have chosen words that have been already used (by a specific gender). This is how they end up producing, endorsing and spreading gender-dependent stereotypes. This extreme overlapping of gender-dependent topics is a fundamental pillar of stereotypical structuration. The allusions to words from a virtual speaker compel the interlocutor because the speech needs to be understood to be powerful (KomurThilloy, 2010) despite the dissolution of one speaker into another (Authier-Revuez, 1984).
Bibliography

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[2] Authier-Revuz J. (1995). *Ces mots qui ne vont pas de soi. Boucles réflexives et non-coïncidence du dire, Tome 1*. Paris : Larousse.

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[4] Détrie C., Masson M., Verine B., (1998), *Pratiques textuelles*, Montpellier: Presses de l'Université Paul Valéry.

[5] Détrie C. Siblot P., Verine B. (2001). *Termes et concepts pour l'analyse du discours. Une approche praxématique*. Paris : Honoré Champion.

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[9] Verine B. (2010), « Construire une connivence dans la disjonction : l'emploi extensif de tu et le discours autre non avéré dans les commentaires rugbystiques d'Herrero et Cazeneuve », Dans C. Détrie, B. Verine (dir.), *L'actualisation de l'intersubjectivité de la langue en discours* (p. 89-104), Limoges : Lambert-Lucas.
Managing your online identity through Twitter within
business-to-business (B2B) organisations

Lucill J. Curtis

Essex Business School, University of Essex
Wivenhoe Park, Essex CO4 3SQ
UK
lcurti@essex.ac.uk

**Abstract.** The purpose of this research is to examine the day-to-day management of organisational Twitter sites from the perspective of UK marketing professionals, within five B2B case-study organisations. Specifically this study examines how marketing professionals as 'organisational storytellers' engage in a process to communicate the official organisational story to customers, while maintaining an element of their personal identities, due to the less formal and brief (140 character) nature of tweets. This results in a fusion of organisational and self-narratives through conversational dialogue, appearing as 'narrative public voice' within the tweets. Adopting an abductive, interpretivist research design, this study initially analysed the content of tweets from five B2B Twitter sites over a three-month period, utilising a corporate web identity framework (Elliott & Robinson, 2013). The tweet examination informed the qualitative interviews with 15 marketing professionals across the five case-study organisations. The emerging results suggest a purposeful unification of personal/organisational identities as more marketing professionals are tweeting in a conversational voice designed to increase accessibility and interactivity with customers. Despite this most of those interviewed consider Twitter to be an awareness raising communications channel in the B2B sector, rather than a means of building long-term relationships with customers.

**Keywords:** B2B, corporate web identity, narrative public voice

# 1 Introduction: Aims

This study will provide a better understanding of the opportunities, challenges and barriers to utilising Twitter within business-to-business (B2B) organisations. In order to evaluate the potential of Twitter it is important to better understand the role, identity and behavioural traits of the ‘organisational storyteller(s)’, which for the purposes of this study are identified as marketing communications professionals working for B2B organisations. This is because it is the organisational storyteller who constructs the narrative that is projected via Twitter and which becomes the ‘organisational story’ and ‘public voice’ of the organisation. In the current literature the role of employees as online network facilitators is largely overlooked (Aula, 2010), so a better understanding of their role should also make an influential contribution to future marketing communications strategies, encompassing corporate identity.
By drawing upon the ‘lived experiences’ of the marketing communications professional(s) as the organisational storyteller within the virtual space, an opportunity arises to see how communication is developed and maintained in organisational Twitter feeds by those working day to day within the sector “in their own words and from their own perspective” (Tyler, 2011: 1483). This has been hitherto missing in the emerging literature relating to Twitter and online identity, which primarily reviews the experience from the perspective of the customer, rather than the ‘organisational storyteller’, within the Business to Consumer (B2C) environment, as opposed to the Business to Business (B2B) context, (Cover, 2012, van Dijck, 2013, Bullingham and Vasconcelos, 2013).

## 2 Constructing identity – can it ever be unified into a single narrative voice?

Social psychology literature (Harre, 1998) has described identity as predominantly singular and static, only liable to alteration gradually over many years. However, this theoretical approach has been disparaged in favour of identity as socially formed and open to change depending on the societal situation (Tajfel and Turner, 1979, Gergen, 1991). According to Karreman and Alvesson (2001: 63) “identity in contemporary social science… is seen as multiple, fragmented, processual and situational, rather than coherent, fixed and stable.” Rosenberg (1997) draws upon James’s (1890: 294) concept of the “empirical self” – where a person “has as many social selves as there are individuals who recognise him,” to describe multiplicity. Based on social psychological practice this meaning derives from the suggestion that a multiplicity of selves has “a variety of conceptual partitioning’s of self” (Rosenberg, 1997:23). Sassen (2000:11) explains this concept in relation to spaces for identity construction:

> “Each one of us has multiple identities and multiple sites for action; worker and workplace are but one type… community, household cultural practices, our bodies – these are all sites for identity and for action.” Sassen (2000: 11, as cited in Valle and Torres, 2000).

Arguably, this view reiterates the multiplicity of individual identities, espoused in modern society. Notwithstanding some of these identity constructs may co-exist in conflict, such as professional, (work roles) domestic (wife/husband, mother/father, friend) and group identities. This may be considered problematic. In fact Lawler (2008:3) suggests the main risk to stability and the consistency of self, is in what she describes as its “internal homogeneity,” – the understanding that the consistency of one identity is simply not sustainable for anyone. Perhaps though there may be situations for professional identities where consistency and a singular personification of self may be desirable. Perhaps this would require one or more aspects of an individual’s identity to reach a point of conscious compromise, or for one or more identity constructs to be omitted in favour of the ‘dominant other’. For example the young female social worker, recently enjoying the birth of her first child, asked to remove a young child from their family due to suspected abuse would have to distance herself from her identity as a mother in order to comply with professional bureaucratic process. There may
though be professional roles that require less distinction between professional and personal identities. For example individuals running their own businesses, working freelance or within industries where their values are reflected within organisational values, may be able to unify elements of their multiple selves more easily. Scripted roles such as those people working in job centres and within customer service centres who are trained to respond in a united, consistent manner may also feel less tension between aspects of their identities (Hopfl, 2002). There is also the possibility individuals may find a way of managing their multiple selves into a single voice. Either they are performing their identity and therefore they speak with the unified voice of an actor, or they find a way to combine the qualities and ideals from their personal identity construct(s) with their professional identity. This may be ratified individually or through social interaction. Arguably this is where the online space may provide a less rigid, controlled place, where personal and professional identity may be seen to more comfortably co-exist or potentially unify. This potential co-joining of personal and professional identities online provides a slightly different perspective on more traditional identity work theories and one that differs from the earlier theories on online communication, where identity is seen almost exclusively in terms of its multiplicity, as is outlined in the next section.

## 3 Existing online identity theories as multiple constructs

It may be considered that each participant in Twitter is trying to condense what could be termed his or her ‘multiple selves’. However, many contemporary social theorists, Hayles (1999) and Braidotti (2013) in particular, have enquired whether our basic understandings of identity are changing as communication flows through computer mediated networks. Rather, are the Twitter generation more comfortable with multiple, even contradictory performativities, not resolved into a single, narratively stabilised identity. Within her seminal work ‘Life on the Screen’ (1995) Sherry Turkle explains the appeal of creating multiple identities, within virtual spaces, known as Multi-User Dungeons (MUDs). These sites, originating from “role-playing games such as Dungeons and Dragons let people take on fictional personae and play out complex adventures,” (Turkle, 1995: 180). From the interviews included within the book, it is suggested young people overcoming shyness, loneliness and desiring escapism from the routine nature of their everyday lives, predominantly accessed these sites during the 1990’s. It gave them the opportunity to ‘live out’ multiple existences, distinct from their own offline identities. She explains the nature of the communication within the online environment, as it was then:

> "In the story of constructing identity in the culture of simulation, experiences on the Internet figure prominently, but these experiences can only be understood as part of a larger cultural context. That context is the story of the eroding boundaries between the real and the virtual, the animate and the inanimate, the unitary and the multiple self..." (Turkle, 1995:10).

The creation of multiple identities in this scenario gave the participants freedom of expression without fear of reprisal, as their online identities during this period were predominantly
anonymous. For a link to be found between their identities online and their actual identity was very unlikely. Usually it was their choice when or if to reveal their 'true selves'. However, this is not the case today and most online identities are clearly visible. On a Twitter account for example an organisation uses extensive branding to actively promote their identity online. Organisations also often include their 'real' name and a photograph, usually including a logo to visually announce who they are.

This is a contemporary phenomenon since the Internet's only enabled virtual communication of this kind recently. However, earlier communication methods may be described as using a virtual medium, such as the written letter, semaphore, beacon-lighting or phone calls. They are considered virtual in nature as the communicators cannot physically see each other and so the recipients are reliant on an exchange of messages. Therefore it is important to recognise the newness of virtuality enabled by the Internet, but also that it has a precedent in other forms of absent-present communication.

What is evident from existing interpretations and applications of narrative identity constructs are the problems associated with the current definitions of identity both within the physical and specifically the virtual environment. McPherson (2000) for example suggests the time has come to move away from considering identity as play-acting online, instead an analysis of politics and participation is urged to form an awareness of authentic virtual world interactions. Therefore, an understanding as to how the organisations' identity is communicated through the organisational storyteller is intended, as well as an awareness of how this impacts on the self-identity of the individual employee(s). Initially an analysis of corporate identity constructs is discussed below, so as to consider the impact of organisational identity on the self identity of the organisational storytellers.

## 4 Constructing identity – can it ever be unified into a single narrative voice?

The study of corporate or business identity has focussed on those aspects of an organisation that make it distinct, and therefore identifiable to customers and employees alike (Balmer, 1998, 2001, 2008, Christensen and Askegaard, 2001). This is aligned with the concept: "...that the effective management of an organisation's identity results in the acquisition of a favourable corporate image and, over time, of a favourable corporate reputation, which leads an organisation's key stakeholders and stakeholder groups to be favourably disposed toward it." As an aspirational concept corporate identity has been explored principally through formal communications literature and policies (Balmer and Wilson, 1998). Recently, however marketing (Abdullah, Nordin and Aziz, 2013) and organisational (Sillince and Brown, 2009) scholars have started to investigate the processes and challenges of maintaining a consistent and appealing corporate identity through online communications, specifically corporate websites. Abdullah, Nordin and Aziz, (2013) approach the study of corporate identity through an analysis of the mission and vision statements on the websites of 860 Malaysian and 642 Singaporean companies, utilising Aaker's (1986) Big Five brand personality dimensions (sincerity, excitement, competence, sophistication and ruggedness). The paper concluded
however that both sets of organisations were poor at positioning themselves distinctly through brand personality dimensions online, potentially leaving them at risk of failing to achieve competitive advantage and of maintaining their reputation. Nonetheless, little is known currently about the routine, day-to-day lived experiences of marketing professionals that support, produce and represent the brand.

Within this study the brand as constructed and embodied within the organisational story interpreted and communicated through the marketing professional's voice within the tweets is considered. Therefore an analysis of the day-to-day interaction and communication of brands, by producers and customers within an online Twitter environment is sought. In this sense an investigation of how corporate and self-identity unite within communications practice and emerge “*through self-aware reflections about whom one is and through everyday practices of doing work*” (Wieland, 2010) will be clarified. Such a sense of consistent unification may for example be achieved through the adoption and reconstruction of brand values. For example recent papers more explicitly review the relationship between brands and employee identity within contemporary organisations (Brannan, Parsons and Priola, 2015; Vasquez, Sergi and Cordelier, 2013). This paper will expand upon both papers to explore how marketing professionals engage in identity work when communicating with customers, through conversational discourse within the tweets, including the process of reconstructing and reaffirming the organisation's brand, as well as their own narrative identity as discussed below.

# 5 Narrative identity

Narrative is described by Webster (1966:1,503) as “discourse... designed to represent a connected succession of happenings.” This reading of narrative alludes to the importance of vocalising experiences as a means of making sense of them (Weick, 1995). Although as early studies show narrative is also understood as written text. For example the study of narrative text is located in Aristotle's Poetics and initial hermeneutic investigations of the Bible and Koran (Czarniawska, 2004). However, it wasn't until the publication of Vladimir Propp's Morphology of the Folktale in 1928, which examined narrative structures within fairytales that it reached broader recognition as a method of textual analysis, principally. Its popularity grew as a means of communication and by the 1970's narratives were considered as ubiquitous, with an ability to adapt their shape and form to suit different social environments as Barthe's outlines below:

> "Narrative is first and foremost a prodigious variety of genres... Able to be carried by articulated language, spoken or written, fixed or moving images, gestures, and the ordered mixture of all these substances; narrative is present in myth, legend, fable, tale, novella, epic, history, tragedy, drama, comedy, mime, painting... stained glass windows, cinema, comics, news item, conversation. Moreover, under this almost infinite diversity of forms, narrative is present in every age, in every place, in every society... it is simply there like life itself." Roland Barthes (1977: 79).
Interestingly this description suggests narrative is understood to be more than just the spoken and written word, it is present within images, gesticulations and theatrical performance. To show how individuals may be said to perform identity narratives I will discuss relevant theories (Goffman, 1959, Butler, 1990 and Boje, 1991) later in this section. The idea of narrative being visual as well as textual is a relatively recent conceptual development and is noteworthy here. In 1995 Julia Murray described narrative illustration as the “pictorial representation of or reference to one or more events that occur in a sequence of time and that bring about a change in the condition of at least one character” (Murray, 1995:17). Of those examples provided above by Barthes (1977) the stained glass window and painting suggest visual narratives may be viewed in isolation of text and still provide adequate meaning. However within film, comics and televised news reports, narrative and visual narratives are mutually complimentary, with one often helping to explain the other. To outline the challenges of understanding and producing narratives I will now explain the difference between a story and a narrative. This is important as this study examines how the organisational storyteller communicates the official organisational story through narrative.

McAdams and McLean (2013) describe narrative identity as “a person’s internalised and evolving life story, integrating the reconstructed past and imagined future to provide life with some degree of unity and purpose.” The idea of narrative identity as an evolving process is useful here in several ways. Firstly it suggests the incomplete ongoing and developing nature of narrative, which would work in an SMN environment, as the ‘list’ of tweets moves at a fast pace and is conversational in nature as outlined in the introduction to this chapter. The concept of the ‘imagined future’ also alludes to a certain degree of creative flexibility with the use of language. While the internalisation of the story also highlights something of an internal struggle possibly for the narrator who has to unify his or her multiple identity facets with elements of the organisation’s identity through narrative public voice.

# 6 Introducing the new concept of ‘Narrative Public Voice’

As outlined above there are various different competing demands on the organisational storyteller when they are communicating via Twitter, one element is the challenge of maintaining the consistency of one organisational identity narrative. This is what current marketing literature (Nandan, 2005) suggests the organisational storyteller should be striving for to secure brand consistency. One of the contributions to research this study offers is to develop a new theoretical construct focussing on the concept of a single unitary identity expressed through voice within the online environment. Voice in this context (specifically narrative public voice) is a different theoretical construct to previous interpretations of voice within organisational studies (Boje, 1995; Vickers, 2005 and Purcell, 2012), feminist theory (Belenky et al, 1997); and previous employee relations’ papers (Budd, Gollan and Wilkinson, 2010). Voice is being investigated as the principle way for organisational storytellers to combine their self-narrative and the official organisational story. This enables the organisational storyteller to effectively ‘tell’ the official story, in a way that encourages them to retain elements of their self-identity, while also effectively communicating the organisational narrative in a way
that can be understood by customers. This model is being developed during the fieldwork
process for this study and the findings are in the process of being analysed.

# 7 Method

## 7.1 Data collection

The first stage of the data collection included the collection of tweet data from Twitter sites of the five case-study organisations over a period of three months. The tweet data was then analysed for key themes, which determined the content of the questions for the next phase of the research. The second stage of data collection included qualitative semi-structured interviews with those responsible for organisational storytelling, including 15 marketing professionals, within each of the five case-study organisations. Once the interviews were transcribed the content as individual quotes, has also been grouped under the five core headings from Elliott and Robinson's (2013) framework. A summary of the characteristics for each of the organisations is included in Figure one below:

These organisations were chosen as they represent a diverse spread of B2B organisations offering an expansive range of products and services. This supports the validity of the data, as it provides an opportunity to explore the lived experiences of organisational storytellers in a variety of organisational contexts. In this sense each organisation is unique and may be treated as a stand-alone unit of analysis (Eisenhardt and Graebner, 2007).

# 8 Research design

## 8.1 New advances in qualitative research methods

The aim of this study is interpretive in nature, with a focus on the social constructivist
paradigm, as it is focused on “how people craft their identities through interaction or how
they weave ‘narratives of self’ in concert with others and out of the contextual resources
within their reach,” (Alvesson, Ashcraft and Thomas, 2008:8). A two-stage qualitative
approach has been undertaken that focuses initially on a thematic narrative analysis of the
tweets, followed by semi-structured interviews with organisational storytellers (marketing
professionals responsible for writing tweets and managing the organisational Twitter site).
Denzin and Lincoln (2005) summarise the essence of qualitative analysis as relevant to this
study empathetically:

> "Qualitative research is a situated activity that locates the observer in the world. It consists of a set of interpretive, material practices that make the world visible... attempting to make sense of or interpret, phenomena in terms of the meanings people bring to them." (Denzin and Lincoln (2005: 3).

Only a qualitative analysis, with its emphasis on reflexivity (Hammersley and Atkinson, 1995) has ensured an explicit emphasis on the detail of individual lived experiences of the organisational storytellers, especially as a lot of the activity, such as the twitter creation
<table><caption>Table 1: A summary of the characteristics for each of the B2B case study organisations.</caption><thead><tr><th>Industry Sector</th><th>No. of Employees</th><th>B2B Customers</th><th>Markets</th><th>Primary Products/Services</th><th>Number of Tweets</th></tr></thead><tbody><tr><td>Telecommunications</td><td>19,800 globally</td><td>Large corporate multi-national companies (MNCs), small, medium enterprises (SMEs) and start-up companies (entrepreneurs) including freelance workers</td><td>More than 170 countries globally</td><td>Broadband and internet support services, IT support and security, phones and phone line management.</td><td>1,437</td></tr><tr><td>Financial services</td><td>38,500 globally</td><td>Large corporate multi-national companies (MNCs), small, medium enterprises (SMEs) and start-up companies (entrepreneurs)</td><td>120 countries globally</td><td>Independent assurance, tax and advisory services</td><td>460</td></tr><tr><td>Healthcare</td><td>8,000 approx. in the UK</td><td>Local Government, specifically County Councils who are responsible for outsourcing healthcare contracts (state-funded elderly care homes) locally</td><td>UK only</td><td>Care homes, homecare and day care clubs</td><td>623</td></tr><tr><td>Business support</td><td>7,100 globally</td><td>Principally, SMEs, MNCs and, freelancers and entrepreneurs</td><td>2,000 business centres in 100 countries globally</td><td>Office space, meeting facilities, virtual offices, video conferencing and support services</td><td>382</td></tr><tr><td>Manufacturing</td><td>2,150 globally</td><td>Commissioners, Facilities Managers, Mechanical design engineers and building services experts</td><td>15 factories in 7 countries globally</td><td>Industrialised fans and ventilation systems</td><td>54</td></tr></tbody></table>
process, is intrinsic and thus not necessarily observable. Of particular importance here is a
search for meaning through these experiences that sheds new light on emerging fields such as
online identity through the observation and collection of organisational tweets. Interviews with
the organisational storytellers, who create the tweets, has also encouraged an understanding
of the process/role of the narrator and the practices they go through in order to create
and respond to the content on organisational Twitter sites. This is pertinent as to date the
emphasis within studies of Twitter has been on quantitative analysis (Krishnamurthy, Gill
and Arlitt, 2008; Java et al, 2007). The regularity of features such as hashtags, retweets and
followers lends itself to statistical analysis, arguably at the expense of a richer examination
of understanding from the organisational storytellers themselves. Such insights from the
storytellers will help to provide details of the processual nature of their ‘identity work’
(Watson, 2008) and the influences on their online ‘identity construction’ (Alvesson, Ashcraft
and Thomas, 2008) as contained within the tweets and their interview narrative. To this
extent this study proposes a deeper understanding of Twitter data that’s different from
simplifying similarities (Marwick, 2013). The adoption of a qualitative approach also allows a
richer analysis of the storyteller’s subjective interpretations of their situation through living
story, evident within the tweets.

## 8.2 Collecting data from ‘Living Story Spaces’

Given that this study has its origins in narrative tradition and is influenced specifically by Boje's concept of 'living story spaces' (Boje, 2011) it is appropriate that this framework is utilised to shape the methodological approach and to categorise organisational sensemaking, a key outcome from this data analysis. The constant fluidity of the online Twitter environment may be considered a place for 'networking in the unfolding present' and therefore a 'Living story space', worthy of further investigation as initially depicted within an adaptation of Boje's (2011) Storytelling Triad Model, Figure Two below:

Another important concept included in the triad model is ‘antenarrative’, which is
considered by Boje (2004) as a ‘bet’ on the future of a past story, which has the potential
to become active again and alter standard narrative constructs. As Boje (2011: 3) suggests
antenarratives may appear as “fragments that seem to cling to other fragments, and form
interesting patterns of assemblage relationships to context and one another.” Given their
brief, unordered, often, fragmented conversational nature tweets may be considered as a
form of antenarrative interplaying in a living online story space. They may also be described
as offering a link to historical narratives based on the organisational story, whilst interacting
with developing living stories (Boje, 2011). In this regard such a living story space should also
lend itself to the concept of a sensemaking narrative. As Brown, Stacey and Nandhakumar
(2013: 1036) summarises:

> "...theorists with an interest in sensemaking have argued that narrative is a
> primary cognitive instrument (Mink, 1978; 131; Polkinghorne, 1988) which constitutes
> the basic organising principle of human cognition" (Boland and Tenkasi, 1995).

The importance of combining narrative and sensemaking within the same theoretical
framework for analysis has lead to the utilisation and adaptation of Rosile et al's (2013: 3)
## Storytelling Triad Model

### Narrative Places

Narrative history of the organisation - framed by notions of 'old war stories' and the 'good old days.'

Successes and failures - past achievements and humorous stories of organisational failures, which may be attributed to the failings of past employees.

Previous key messages from printed corporate literature, including mission statements and annual reports.

### Living story spaces

Twitter sites (tweets) and other social media networks/blogs.

Fragments of the organisational story communicated through the organisational storytellers and appearing as tweets.

PAST

FUTURE

### Antenarrative Frontiers

Given their brief, unordered, often fragmented conversational nature, tweets may be considered as a form of antenarrative interplaying in an online story space.

They offer a link to historical narratives based on the organisational story, whilst interacting with developing living stories, such as events, product launches and staff appointments.

<caption>Fig. 1: Storytelling triad model – Adapted from Boje (2011).</caption>

storytelling diamond, as the overall means of shaping the methodological approach, which includes the tweet analysis and the qualitative interviews. Figure three, below provides an illustration of the storytelling diamond. Of particular interest is the ‘living story’ category and the arrows, which are the antenarrative processes moving in-between the paradigm (Rosile et al, 2013: 559).

Before application of the storytelling diamond is possible, Rosile et al asks researchers to consider their motives from epistemological, ontological and methodological perspectives as illustrated in Table 2 (Rosile et al, 2013).

<table><caption>Table 2: Epistemological, ontological and methodological perspectives (Rosile et al, 2013)</caption><tr><td>1</td><td>What is the epistemological value of storytelling? Is it a series of unique and generalisable accounts of reality, or is it a phenomenon that explains identity and rationality?</td></tr><tr><td>2</td><td>Does storytelling give a better understanding of an experienced truth of Being-in-the-world, or does it reflect a reinterpretation of lived experiences?</td></tr><tr><td>3</td><td>What methods motivate the researcher? Is the goal finding empirical evidence (both qualitative and quantitative) that leads to the testing hypotheses, or is it adding multiple perspectives so that a greater depth of understanding is possible?</td></tr></table>
<figure><img src="image_56.png" /><figcaption>Fig. 2: The storytelling diamond (Rosile et al, 2013: 559).</figcaption></figure>

Of particular relevance to this study is the second question, which relates to lived experiences. The combination of tweet analysis and qualitative interviews is intended to provide greater insights into the day-to-day experiences of marketing professionals within their role as organisational storytellers. Arguably the online dimension to these experiences provides a means of reinterpreting lived experiences, which is different to previous studies (Jacobi, Freund and Araujo, 2015) however the notion of “an experienced truth” (Rosile, 2013: 560) is also sought through the living story within the tweets and the narrative from the qualitative interviews. While Elliott and Robinson’s (2013) web identity framework, discussed in the later sections (specifically in relation to the initial tweet analysis), where narrative and voice are initially linked to sensemaking.

Elements of the storytelling diamond model are highlighted throughout the rest of this paper to illustrate how it has shaped the data collection process, with links also to how it will inform the analysis. In order to make this work cohesively, Rosile et al (2013) suggests the researcher needs to be part of the process of living story, with an ability to recognise when antenarratives are interjecting into the present. Such a process of understanding is suggested through the analysis of the tweets by the researcher, but also through speaking with the organisational storytellers. This has supported understanding of the identity of the five case-study organisations and also their stories, past and present, so as to recognise the occurrence of antenarratives within the tweets.

As Rosile explains:

> "If the audience with whom the scholar wishes to converse is in need of abstractions in order to value the findings, then the deep descriptions of living story should be turned into abstracted categories" (Rosile et al, 2013: 570).
Such ‘abstracted categories’ of living story were identified from the tweet analysis utilising
the broad categories as identified by Elliott and Robinson (2013) as part of their work making
sense of websites and corporate identity thematics as detailed within the next section.

8.3 Understanding the context of the ‘Living Story Space’ - The collection of tweet data

The first stage of the data collection included the collection of tweet data from Twitter sites of the five case-study organisations over a period of three months. The tweet data was then analysed for key themes, which determined the content of the questions for the next phase of the research. The second stage of data collection included qualitative semi-structured interviews with those responsible for organisational storytelling, through the adoption of the narrativist paradigm (Rosile, 2013). This approach encouraged understanding as to how they make sense of their work/organisational experiences, within each of the case-study organisations.

An explanation of why these methods and case-study analysis are the most appropriate to
meet the aims of this study and how they were undertaken, is outlined within the following
sections.

9 Phase one – Antenarrative processes

9.1 Living Story Spaces analysis - Netnographic observations of Twitter sites from five case-study B2B organisations

Tweet data including text with links, images and video content was collected every three
days for three months, from online Twitter sites (for each organisation), managed by the
five B2B organisations. Every three days was a practical collection time for the tweets as
the organisations wrote between 2-10 tweets on average most days. Therefore collecting
every day would have meant a very large number of small files, whereas every three days
provided fewer files with more data on each screen-shot. When collecting the tweet data a
technique known as ‘screen-grabbing’ was used, which is similar to ‘cutting and pasting’ in
Word documents. As illustrated below I was able to collect approximately 3-6 tweets for
each organisation in one ‘screen grab’, Figure Six. However if images or video are included in
the data capture there are slightly fewer tweets per ‘screen grab’. This is because the images
and video clip links take up a lot of space on the page as they are much larger than space
required for 140 characters of text.

As Kozinets (2010: 56) explains, the observation of "*online community conversations and other internet discourse combines options that are both naturalistic and unobtrusive.*"
Analysing tweet data through netnography allows the researcher to meet the principles of traditional ethnography, telling a believable, detailed and genuine narrative (Fetterman, 2010). Observing the tweets as they appeared in 'real-time' contributed to the sense of seeing a naturalistic narrative (living story) unfold, without the organisation or the followers of each Twitter site being conscious of the observation, as the researcher did not make any
<figure><img src="image_57.png" /><figcaption>Fig. 3: Example of tweet data 'screen grab' – the method of collecting tweet data during phase one. This Twitter page includes primarily text-based tweets, produced by Grant Thornton, [one of the five B2B case-study organisations], by the organisational storyteller over a four day period.</figcaption></figure>

interventions on the site. This is important, as the researcher wants to observe the twitter site as if they are a 'customer' initially learning and making sense of the organisational identity
and that of the narrator, for the first time, through the type of language and images contained within the tweets. As a potential or pseudo customer there is the potential to observe what there is to glean in terms of data and clues about the identity of the organisation. The idea here is that the tweets highlight to customers those issues of most interest or significance to the organisation and therefore are where researchers should look for data/clues about how the organisation wishes to portray itself. As the content of the tweets was primarily informative, the messages are described as “closed netnographic texts” (Kozinets, 2010: 170).

## 10 Living story – Organisational and personal identity through story

The researcher views each tweet in detail while also trying to understand the broader themes and considerations relating to the organisation's and the organisational storyteller's identity. The process as Markham (2005:794) concurs was largely “iterative; one's attention shifts alternately between close examination of texts to larger sensemaking frameworks.” As Davies (2008) elaborates: “‘There are two primary ways in which ethnographic research on the internet can be contextualised. In the first place, attention should be given to the offline context in which internet usage occurs.’” In this sense the organisational setting where the tweets are written, as well as the identity of the organisational storyteller is of principal interest. This is because of varying levels of interaction by the customer accessing the organisation via online platforms (Sawhney, Verona and Prandelli, 2005).

Due to the volume and variety of tweets, this first phase of data collection provided a detailed understanding of the organisation's style of tweets (such as product promotion, new initiatives, events etc) their followers, hashtags used, retweets included and complaints experienced. The tweets also gave the first insight into sensemaking of the organisational narrative by the organisational storyteller – as encapsulated in the analysis of the narrative public voice. As Garton, Haythornthwaite & Wellman, (1995: 75, as cited in Kozinets, 2010: 50) explain, this type of analysis provides a means of understanding sequences of language formation and customer responses and silences (who speaks, who does not communicate and what is said proactively or in response to whom?):

> “Social networking analysts seek to describe networks of relations as fully as possible, tease out the prominent patterns in such networks, trace the flow of information (and other resources) through them, and discover what effects these relations and networks have on people and organisations.” (Garton, Haythornthwaite & Wellman, 1995:75).

The main purpose of this stage of data collection was to understand the information contained within the organisational tweets. As Brown, Stacey & Nandhakumar (2008) outlines the essence of narrative (and sensemaking) analysis is focused on three main elements, context, content and form. The contextual aspect of Twitter relates principally to who tells what to whom; the frequency of organisational tweets as opposed to re-tweets and the identity of the key spokespeople is therefore important. While content requires more specific analysis
of language – in terms of what is spoken and how frequently; and form relates to how it is said including the tone, words and the links and images used in conjunction with the text. An analysis of the content of the tweets initially collected as living story, was then used to identify the nature of the questions to be included within the interview scripts for the organisational storytellers.

# 11 Abductive interpretivist approach - Data analysis, interpretation and presentation

## 11.1 Using coding to identify themes from the tweet data

Following the identification of Rosile et al's (2013) storytelling diamond model as a lens with which to frame the two processes of data collection, the specific leitmotifs as originating from the tweet analysis are considered. An established qualitative coding model has been used to categorise the downloaded text, image and video files. Given the relatively small sample per organisation the researcher was able to start the process of interpretivist extrapolation of emergent living story themes into a draft framework, comprising slight modifications and additional aspects of the corporate website identity model. The main themes in the original framework for abstracting corporate web identity were: "mobility, accessibility, visuality, interactivity and customisation" (Elliott and Robinson, 2013: 276) and these are developed within the framework for analysis of the tweets below:

<table><tr><td>Mobility</td><td>What it is within the tweets that encourage movement/action?<ul><li>types of revolving text</li><li>repeated tweets</li><li>links, moving the reader away from the central text</li></ul></td></tr><tr><td>Accessibility</td><td>How customers make sense of the identity of the storyteller and the mass of information that is included<ul><li>identification of the audience, who is the organisational storyteller speaking to?</li><li>complaints</li></ul></td></tr><tr><td>Interactivity</td><td>The interplay between self and organisation<ul><li>attempts via tweets to persuade customers to interact with the organisation/organisational storyteller positively</li><li>personal twitter feeds interjecting into organisational conversations</li><li>cross-over with followers</li></ul></td></tr><tr><td>Visuality</td><td>Influence and occurrence of videos, images and hashtags.<br>Content of images and how they contribute to organisational/organisational storyteller identity and narratives.</td></tr><tr><td>Customisation</td><td>Key messages and repeated phrases relating to the identity of the organisation and its story.<br>Appearance of branding and consistency.</td></tr></table>
This framework has been utilised to structure the development of the abductive (Mantere and Ketokivi, 2013; Martela, 2011) coding process from the tweet data (Bryman and Bell, 2007). An example is included to illustrate how this process has been implemented to analyse the tweet data including the coding manually by hand as depicted below in Figure 4.

<figure><img src="image_58.png" /><figcaption>Fig. 4: This is an example of where the tweet data from one of the case study organisations has been manually coded using the headings and descriptions from the Elliott and Robinson, (2013) framework.</figcaption></figure>

The next stage of the process was to use these headings (Elliott and Robinson, 2013) and their descriptions to develop a list of codes for each of the case study organisations (Kozinets, 2013), which led to the drafting of specific questions utilised within the interviews.

Arguably, the identity of the customer is inferred by the content, tone and style of the organisational tweet. Even if the tweets do not always promote a response it is suggested that each one is intended to engage existing and potential new followers. Engagement is encouraged by linking online content about the organisation, for example the links in tweets frequently refer the reader back to the company website. Tweets may also be originated in response to other online communities and help with facilitating networking between followers, or between the organisation and existing or potential new followers - helping to develop online and offline relationships through dialogue (Weber, 2009). The content of the tweets identifies causes the organisation supports such as national commemorative days i.e. ‘Dignity Action Day’ (Healthcare company), as well as new products/services. Individuals, such as employees are also introduced through the inclusion of links to interviews with senior members of staff, including the chief executive and the HR director (Healthcare company).
The focus of the next section is on the second stage of data collection, through the case-studies, semi-structured interviews with organisational storytellers. This section will also discuss sampling, access and participation. In contrast the objectives for the second stage of data collection, particularly focus on making sense of the online narrative through the process of narrative fusion (where elements of personal identity, and organisational identity are combined through narrative by the organisational storyteller and represented in their tweets as ‘narrative public voice’). Therefore this stage requires a different methodological approach because it is the richness of the organisational storyteller’s experience through narration of the process of writing the tweets that is required here.

# 12 Phase two – Antenarrative processes: Narrator – (Narrativist) paradigm

## 12.1 Qualitative semi-structured interviews

Following the identification of key themes from the tweet analysis, the second phase of the data collection was semi-structured interviews with the organisational storytellers from the five case-study organisations.

Given the relatively embryonic field of Twitter research from the perspective of those writing the tweets, particularly within the organisational context, this study's focus is a contemporary inquiry. Additionally, how individuals interpret and make sense of the online narrative in order to produce the tweets, may be described as a “complex social phenomena” (Yin, 2014:4) and so would benefit from further understanding, specifically in relation to online organisational narrative. Further details as to phase two of the data collection via semi-structured interviews is included below.

## 12.2 Characteristics of the participants

The semi-structured interviews were undertaken with 15 employees in total, who are involved in writing and responding to tweets on the organisation's Twitter site. The sample included eight men (53%) and seven women (47%). Within the five case-study organisations social media management is considered a specialism and often part of, though distinct from the marketing communications' function. Small marketing communications' teams are more common in B2B companies due to the emphasis on a more specific customer base and therefore more stringent targeted communication methods (Jensen, 2006).

## 12.3 Interviews: Sampling method, access and participation/purposiveness

Two to five employees were interviewed per organisation, the majority of these worked across management levels. The participants were interviewed once and their responses audio recorded and transcribed. A breakdown of interviewees per case-study organisation by gender and job title is included below in Figure 3.

The researcher's prior knowledge and experience of working within the social media function in the B2B sector enabled her to use established contacts to gain access into the
organisations initially. Introductions were then made from established contacts to those
individuals working on updating the organisational Twitter site. A purposive sampling
approach was therefore adopted as the individuals being interviewed were the organisational
storytellers, or they have input into the Twitter creation process. It is only in this instance
that these individuals can provide data on the sensemaking processes involved in identity
construction – self and organisational – as well as provide insight into the influence of
technological channels on creation of a narrative public voice. The approach to sampling
undertaken follows Glaser’s (1978:37) description of selective sampling as the:

> *“Calculated decision to sample a specific locale according to a preconceived but reasonable initial set of dimensions (such as time, space, identity or power) which are worked out in advance...”*

This informed approach to sampling allowed a structured interviewing timetable, where
individuals were seen on the same day, so as to maximise the momentum immediately
preceding the collection of tweet data. Each interviewee was asked to recommend others in
the organisation that s/he felt the researcher should talk to. Brown & Thompson (2013) in
their research on sensemaking, also draws attention to the necessity of talking to several
members of the same team in order to understand the discrepancies and how these are
translated into individual and team action. Different interpretations and responses to the
same or similar processes is certainly a key element of the sensemaking process in this study,
which is why at least two people per team are being included within the interviews. It is
therefore the role of the researcher to draw these interpretations together from the tweets
and also from the organisational storyteller’s qualitative interview narrative.

As summarised by Dolores and Tongco, (2007) this is a non-randomised method where
the researcher understands some of the information to be investigated, and then they choose
the appropriate contacts based on their expertise in this field of study (Lewis & Sheppard
2006). Criticisms of sampling strategies in qualitative research are common, as Coyne (1997)
summarises due to poorly explained assumptions, blurring of methods and theoretical
standpoints. These criticisms do not apply directly to this study as the identification of roles
and key contacts is based on experience and informed research of the selected case study
organisations. An interpretivist qualitative approach is the most suitable approach as the
depth of day-to-day experiences from those updating and monitoring Twitter sites within
organisations is sought.

The main reason for conducting the interviews is to increase the depth of meaning from the
tweet content, specifically around the process of identity construction and voice. In particular
this part of empirical data collection will help to answer the second and third objectives
for this study. These focus on making sense of the online narrative from the organisational
storyteller’s perspective and to understand the implications for them in producing the tweets,
and how this positions the customer. Based on what they say about their day-to-day jobs,
the researcher through the methodology is asking the organisational storyteller to make
sense of how they interpret and communicate the organisational story. How the researcher
interprets the ‘story spaces’ (or antenarratives) and how they in turn shape organisational
and individual (organisational storyteller) identity is the next stage of the process. Followed
by how this blended narrative identity (narrative fusion, a mix of organisational and personal identity through narrative) is communicated through voice.

## 12.4 Exploring themes from the tweets written by the interviewees to inform the interviews

The aim of this stage of the research was therefore to explore the meaning behind the concepts inferred from the tweet data. To this effect the questions and tweet data are included as prompts – see also Figure eight for examples, to elicit further detail, giving interviewees the chance to explicate and expand upon the sensemaking of their specific phenomena (Saunders et al, 2009). It is though worth noting for the purposes of this study that preparation has been central to ensure the validity of the data gathered. This is particularly relevant here as individual organisational tweets, images and video clip ‘stills’ were downloaded and printed out (prepared) on pages to be shown to the interviewee, in order to elicit detail about the process of managing tweet content. In addition, a list of key headings, questions and tweet data has been collated in advance to suit the specific case-study organisation each time. The fact that there are only small teams working closely together on social media functions, including Twitter, within organisations meant that shared meanings and experiences were common among interviewees, which also helped to strengthen the value and reality of their accounts. Reality or lived experience in this sense, relates to the constructionist tradition of an agreement of opinions, as to what can be understood as truth (Guba and Lincoln, 1989) which is elaborated upon in the next section which focuses specifically on the emerging themes from the data.

# 13 Emergent thematic identification and reasoning – interpretive interview data Analysis

The analysis of the semi-structured interview data was also undertaken through an abductive approach. The process included the practice of reviewing the existing literature in conjunction with the empirical data, so as to explicate the importance of identity, narrative and voice in this research and to expand current thinking in this field (Van Maanen et al, 2007). In particular the aim of the analysis is to understand the narrative journey of the organisational storyteller through a process model that explores the stages and influences within tweet creation (Langley et al, 2013) from their experiences. The emphasis within this paper (Langley et al, 2013) is an examination of phenomena over duration and specifically how it evolves, alters and develops, there isn’t quite the same emphasis on turning points and how you can identify these from the data within this study. Rather here the emphasis is on process research focuses on sequential developments of actions as the means of more easily making sense of them. For example the process of writing the tweets is an emerging phenomena that is examined here from the basis of the tweets themselves, as well as what the authors (organisational storytellers) say about the process of writing them. Some of the interview data contributes to this notion of organisational stories re-emerging as antenarratives through
the tweets, which provides specific insights into ‘how the narrator makes sense of the past, work/organisational experiences.

## 13.1 Coding the interview data

Having identified the core interview questions through the initial tweet analysis, these formed the basis of the qualitative semi-structured interviews with the organisational storytellers (narrators). The emphasis of the abductive interpretive approach, requires “a constant movement back and forth between theory and empirical data” (Wodak, 2004:188, as cited in Mantere and Ketokivi, 2013). In this way, awareness and interpretation is seen as an ‘ongoing conversation’ between the existing theories and the researcher’s previous experience. The process of referring back to the theory (relating specifically to identity narrative and voice) and looking ahead at the empirical data, should therefore inform a better understanding of the phenomena being studied, as well as providing new insights/knowledge (Mantere and Ketokivi, 2013; Martela, 2011). The interviews were therefore pertinent to understand how the organizational storytellers (narrators) make sense of their work/organisational experiences, in a manner described below by Butler (2005):

> "...it is possible to explore how narrators make themselves subject to their new media narratives (Butler, 2005), allowing insight into the ways in which the possibilities and expectations of new media existence are internalised and with what implications for self and social relations."

It is this understanding of internalisation that is important here, because as previously discussed, just observing the writing of tweets will not provide sufficient depth to understand the implications of the organisational storyteller's identity construction. Also, part of the interview process was to test out a new concept of voice over the internet, initially conceptualised as 'narrative public voice'. This is because there are various different competing demands on the organisational storyteller when they are communicating via Twitter, one element is the challenge of maintaining the consistency of one organisational identity narrative. This is what current marketing literature (Nandan, 2005) suggests the organisational storyteller should be striving for to secure brand consistency.

In order to illustrate the manual coding of interview data, following initial thematic analysis a short extract from an interview with the marketing communications manager at Grant Thornton (business support) is included below as Figure 5.

As previously discussed the researcher is still in the process of analysing the data, so the initial findings and discussion below are focused on one case study only at this stage.

## 14 Emergent findings and initial discussion

Initial analysis focuses on an understanding of how many of the tweets fit into each of the categories: *Mobility, Accessibility, Interactivity, Visuality and Customisation*, as outlined by Elliott and Robinson (2013). The data from the 15 semi-structured interviews with marketing
<figure><img src="image_59.png" /><figcaption>Fig. 5: The manual coding of interview data, following initial thematic analysis.</figcaption></figure>

professionals has also been grouped into these categories and is incorporated to explain the intention of the organisation and the individual storytellers. As the author is at the early stages of data analysis, an initial review of the findings from one case study (manufacturing) is provided below.

Over a three-month period 54 tweets were collected from Forsdyke Industrial's Twitter site, a UK based manufacturer of air-technology and ventilation systems for buildings. The majority of the tweets, 15 (28%) were focussed on facilitating mobility and accessibility 15 (28%). An example of a tweet that specifically encourages the mobility of the customer from the main Twitter site to other linked articles were predominantly PR initiated stories relating to new product launches, for example:

> Tweet: 'Check out the front page of @MBS_magazine #ahu #sheffieldhallam #recooler #afreshapproach edition.pagesuite-professional.co.uk//launch.aspx?e...'

While tweets that facilitate accessibility were principally those that advertised new vacancies and therefore were inviting the customers to become part of the internal team. In this sense access is being facilitated to the organisation itself, where customers have the opportunity to become part of its core identity, as staff members. Customers are informed within the tweet, which positions are currently vacant, and provided with a link to the job specification and the application form, also as detailed below:

> Tweet: 'Forsdyke Industrial UK currently has a vacancy for a Presentation
Specialist & e-Learning Coordinator #jobs forsdyke.co.uk/newsarchive24/...’’

By narrating the story of the organisation positively, i.e. 'this is a good place to work' the marketing professional is also narrating their self, to an extent, 'I am proud to work here' but in a precarious context where reception is uncertain, as it is dialogic, at some level, and also potentially dependent upon the reception of an audience for its validation. Accessibility with the customer is sought in this context through validation where customers viewing the tweets may also want to work for the organisation and therefore will apply for a position within it. The organisational storytellers are also prepared to adapt their 'voice' in order to allow the organisation and themselves to seem more accessible. As Rebecca Williams, Marketing Assistant, highlights:

> "When I write I aim to be the voice of 'the guy next door' because I think that's what our customers need to hear and what they recognise and I imagine you get better accessibility when you do that and because of the types of things that we use Twitter for, you know like product information, recruitment - so I don't think that if it was really the 'voice' of a marketing person it would have much legibility with people."

In this sense the presentation of self is performed to create the specific voice of an individual the customer will identify with. The guy next door is therefore considered more accessible than a marketing person in the conversational context of a tweet. This perhaps explains why there is less emphasis on tweeting key corporate messages. Only five out of the 54 tweets (9%) were categorised as 'Customisation and communication of strategic messages' which was the lowest category. Visuals and rhetorical questions are more frequently utilised to increase accessibility and to an extent interactivity, though this is very rarely reciprocated by customers as minimal 'retweets' and 'favourites' are noted. The visuals are principally focused on new products, visits from local dignitaries, such as MPs to the factory, 'Thanks for the retweets' messages and coming soon teaser campaigns. Sometimes these images are also accompanied by rhetorical questions such as: "My best RTs this week came from: @AFE_Dist_UK #thanksall Who were yours? Sumall.com/thankyou. Arguably though engagement with customers is not the principal aim of using Twitter in the B2B sector, as raising awareness may be a more achievable outcome, as John Griggs, Regional Operational Marketing Manager, explains:

> "I see it (Twitter) as a way of raising awareness of the brand. The problem with B2B social media or any sort of digital marketing is really just about generating leads, you'll never actually get anyone to say: 'You're great - can we buy your vans, or stuff like that.' It's just to make sure people are aware as to who you are."

Perhaps one of the main reasons also that awareness of the brand seems a more likely aim than engagement, is because the organisation does not know who their followers are as John outlines:

> "But we do try to convey the same sort of message online, in theory it is the same sort of people, at least we think it is, as we don't know exactly who our followers are.
> We have an idea of the groups from the retweets. So, I will go round looking for more of the right sort of people and follow them and hope they return the favour – they don't always, but I'll keep trying.

In this instance reiterating the identity of the brand consistently via Twitter is therefore more achievable. It also contributes to the notion of corporate identity as focussed on those aspects of an organisation that make it distinct, and therefore identifiable to customers and employees alike (Balmer, 1998, 2001, 2008, Christensen and Askegaard, 2001).

Although far more work is required to gain a more detailed understanding of the subject position of the organisational storyteller and the customer within B2B Twitter accounts, these very early findings suggest there is an emphasis on raising awareness of the brand, rather than engagement. A brief analysis of a manufacturing case study based in the east of England highlights an emphasis on moving customers away from the Twitter site through links is preferred to directly encouraging customer interaction. This is partly due to the nature of the identity of the organisation and also the content of the tweets, which is primarily factual and information-based rather than provocative and/or stimulating of discussions.

## 15 Contribution of this study, a new theoretical construct

One of the contributions to research this study offers is to develop a new theoretical construct focussing on the concept of a single unitary identity expressed through voice within the online organisational environment. Voice is being investigated as the principle way for organisational storytellers to combine their self-narrative and the official organisational story. This enables the organisational storyteller to effectively ‘tell’ the official story, in a way that encourages them to retain elements of their self-identity, while also effectively communicating the organisational narrative in a way that can be understood by customers. The ‘narrative public voice’ model has been re-examined during the fieldwork process for this study and the findings are in the process of being analysed and the model finalised.
<table><caption>Table 3: Breakdown of Interviewees by organisation, job title and gender.</caption><thead><tr><th>Case Study</th><th>Organisation</th><th>Job Titles</th><th>Ages (in order of job titles)</th><th>Male/Female</th><th>Education Level</th></tr></thead><tbody><tr><td>1</td><td>Telecommunications</td><td>Head of Customer, Insight and Futures, Head of online adoption and social media, Customer experience consultant, Social media lead and Marketing executive</td><td>50, 38, 35, 47, 27</td><td>4 Women, 1 Man</td><td>Doctorate, Degree, Degree, A-levels, Degree</td></tr><tr><td>2</td><td>Financial Services</td><td>Senior manager, national communications, Social media manager</td><td>38, 46</td><td>2 Men</td><td>Degree, Degree</td></tr><tr><td>3</td><td>Healthcare</td><td>Head of Digital, Communications manager, Group Communications Manager, PR manager and</td><td>34, 49, 31</td><td>2 Men, 1 Woman</td><td>Degree, A-levels, Degree</td></tr><tr><td>4</td><td>Business Support</td><td>Social media manager and Customer services manager</td><td>48, 45</td><td>1 Man, 1 Woman</td><td>Degree, A-levels</td></tr><tr><td>5</td><td>Manufacturing</td><td>Regional operational marketing manager, PR manager and Marketing assistant</td><td>49, 42, 28</td><td>2 Men & 1 Woman</td><td>Apprenticeship, post-school, Degree, Degree</td></tr></tbody></table>
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# Twitter data for urban policy making: an analysis on four European cities

Marta Severo¹, Timothée Giraud², and Hugues Pecout³

¹ Laboratoire Geriico GIS-CIST
Université de Lille 3
Villeneuve-d'Ascq, France
marta.severo@univ-lille3.fr

² CNRS, UMS Riate
timothee.giraud@ums-riate.fr

³ CNRS, GIS-CIST
hugues.pecout@gis-cist.fr

**Abstract.** This paper presents the results of a project (called “Big data”) carried out in the context of the European programme ESPON. In this project, we test the interest of using Web 2.0 data for studying issues related to territorial development and cohesion. We focus on the possible uses of large sets of Twitter data for studying European cities. These data are expected to improve the study of the impact of the brand of a city at international and at local level. The objective of the project is to define a methodology and not to provide final evidence. For this reason, we test three different types of samples and methods in order to evaluate advantages and drawbacks of each technique: (first method) tweets mentioning the city; (second method) tweets geo-tagged in the city; (third method) tweets of a qualitative sample of influencers from a city.

**Keywords:** Twitter, city, brand, visualisation, communication, public policy.

## 1 Introduction

This paper presents the results of a project (called “Big data”) carried out in the context of the European programme ESPON<fnref n="4" />. In this project, we test the interest of using Web 2.0 data for studying issues related to territorial development and cohesion. We focus on the possible uses of large sets of Twitter data for studying European cities. Twitter is an online social networking and micro-blogging service that enables users to send and read short messages called "tweets". Today this platform has more than 200 millions active users and more than 500 millions tweets per day. There are three features that make tweets very interesting for our analysis: (i) tweets are short (ii) they are generally public and can be easily collected through the API (iii) they are or can be geo-tagged. These data are expected to improve the study of the impact of the brand of a city at a local and international level.

<footnote n="4">⁴ http://www.espon.eu/main/Menu_Calls/Menu_Procurements/MenuimbPreviousProcurements/Feasibility_Study_Analytical_Tools_Big-Data.html</footnote>
The objective of the project is **to define a methodology and not to provide final evidence**. For this reason, we test three different types of samples and methods in order to evaluate advantages and backwards of each technique: (first method) tweets mentioning the city; (second method) tweets geo-tagged in the city; (third method) tweets of a qualitative sample of city's influencers. We analyse tweets related to four European cities (Marseille, Brussels, Edinburgh and Bologna) for a period of four weeks. In the first part, we will present the methodology that we have designed for this case study. In the second part we will describe the results of the analysis and in the third part, we will summarize our conclusions based on the project.

## 2 Method

We analyse tweets concerning four cities (Marseille, Brussels, Edinburgh and Bologna). Data available concern the period from 21st May to 6th July<fnref n="5" />. We focus especially on a period of four weeks (2-29 June 2014). We propose to select these metropolitan areas because they: guarantee a sufficient coverage of the ESPON area; constitute very good examples of metropolitan projects; have similar size (between 1 and 3 millions habitants); guarantee a good and feasible linguistic and cultural variety.

As said, the objective of this study is to define a methodology and not to provide final evidence. For this reason, we decide to test three different methods of using Twitter in urban studies and we evaluate the advantages and drawbacks of each method. In the first method, by analysing tweets including the name of the city, we expect to identify the items that constitute the image of a city. In the second method, by analysing tweets geotagged from the four cities, we will give insights on the intra-urban activities of the city. Then, in the third method, by identifying a sample of influencers on Twitter for each city and by analysing their tweets, we expect to observe in real-time the themes related to the metropolitan area.

Yet, before presenting the analysis's results, there are two main drawbacks related to Twitter data that have to be taken into account: (1) the variety of uses of tweets and (2) the small amount of geo-tagged tweets.

### 2.1 The variety of uses of Twitter

Twitter is a tool with several different uses. On the one hand, people write tweets for expressing emotions, for communicating or organizing their activities, for spreading their ideas. On the other hand, Twitter can be used as a promotional tool for diffusing events, products or buzz. Moreover, several websites or social network accounts (Tumblr etc.) can use tweets for syndicating their content.

Another important issue to be considered is the impact of some events on the normal Twitter activity. If we consider tweets from the 2nd of June to the 29th of June 2014, some important peaks can be identified in time series of each city (except for Bologna). For example,

<footnote n="5">⁵ Tweets have been collected thanks to the TCAT tool developed by the University of Amsterdam - Digital Method initiative (Rieder & Borra, 2014).</footnote>
in Brussels, a peak on the 2nd of June is due to a demonstration related to the abdication of Juan Carlos (#ReferéndumYA, #IIIRépública, #ElReyAbdica), followed by the peak of the 3-5 June related to the G7 summit. In Edinburgh, there is a peak at the beginning of June related to the concert of the band "One Direction" (#NiallsNotes, #WWATour, #1DA). Different is the case of Marseille where there is a peak on the 21st of June that is not related to a few specific hashtags, but is a result of the intensification of the general activity on Twitter because of a cultural local event ("fête de la musique") that pushed people to tweet. Such phenomenon can create important biases, especially for aggregated analysis across time. Yet, it is very difficult to define a general rule for treating it.

As we will see in the following pages, it appears difficult to understand whether a tweet really concerns the city (even if the city is mentioned) and even more difficult to understand which content of tweets is related to the image of a city. No doubt, it is impossible to develop a standardized and automatic method for distinguishing pertinent and non-pertinent information.

## 2.2 The small amount of geo-tagged Tweets

Another important limit that has to be taken into account is the scarcity of geo-tagged tweets, usually no more than 1% of the total tweets (Gerlitz & Rieder, 2013). The geo-tagged tweets we will use in this study are those that contain an actual latitude/longitude coordinate pair, derived from the GPS sensor on a smartphone or through cell tower triangulation. In our study, tweets' geographical distribution is normalised with the distribution of a 1% random extraction from all tweets. Through this normalisation, we can focus on the over-quotation of cities instead of on the simple quotation.

Several other methods have been experienced to infer user's location on Twitter based on user profile descriptions, user profile languages or following-follower relationships (Davis et al, 2011; Crampton et al, 2013), yet these methods are still experimental and are really time-consuming if applied on larger corpora. For this reason, we decided to exclude them in this first experimental phase of the study.

# 3 Results

## 3.1 Frist method: tweets about the city

As a first method, we propose to analyse tweets mentioning the name of the four cities in all European languages. Through this technique, we expect to study the recognition of a city at the international level and to identify the items which are acknowledged as part of the brand of a city. On this sample we performed two types of analysis. First, we used geo-tagged tweets in order to analyse the geographical distribution of people mentioning the city. Second, we focus on the topics related to a city through different content analysis techniques, by combining quantitative and qualitative methods.
**Spatial analysis: where are located the users mentioning the city?** In a first phase, we focus on geo-tagged tweets to study where are located people speaking about a city. As said, we have to keep in mind that these tweets represent only a small percentage of all tweets mentioning the city: between 5% of Bologna and 2% of Edinburgh. We can observe two facts:

(1) Tweets that mention a city are more likely to be geo-tagged. They are from 2 to 5% in our sample whereas the general ratio is more around 1 or 1.5%.

(2) The bigger sample, the lower is the percentage of geo-tagged tweets (Table 1).

<table><caption>Table 1: Tweets and geo-tagged tweets mentioning the four cities from the 21st May and the 6th July.</caption><thead><tr><th>City</th><th>Total Tweets</th><th>Geo-tagged Tweets</th><th>Ration</th></tr></thead><tbody><tr><td>Bologna</td><td>233,899</td><td>12,089</td><td>5.17%</td></tr><tr><td>Brussels</td><td>739,150</td><td>20,183</td><td>2.73%</td></tr><tr><td>Edinburgh</td><td>1,064,735</td><td>21,583</td><td>2.03%</td></tr><tr><td>Marseille</td><td>430,824</td><td>18,344</td><td>4.26%</td></tr></tbody></table>

We analyse the geographical distribution of tweets mentioning a city. By comparing the city's quotation density around the world to the general density of tweets, we can easily see where the city is over-quoted in relation to the normal Twitter activity (Figure 1). Our hypothesis is that the geographical distribution of over-quotation could be used as an indicator that reflects the level of internationalisation of a city.

The geographical distribution of tweets mentioning the four European cities exhibits some regularities. Generally, we can observe that tweets that mention a city are mostly emitted from the city's country and even more from within the city and its suburban area. We can identify some exceptions:

- The over-quotations of Bologna in USA due to the fact that there is a city called Bologna also in USA and in a similar way in Spain where there is zone called Bolonia;

- The over-quotations of Brussels that are dispersed in several European countries. This is probably due to the European role of this city (in several case the term "Brussels" can become a synonym for "European Union");

- the over-quotations of Marseille in North Africa probably due to the geographical and cultural proximity with other Mediterranean cities.

So as a conclusion, the analysis of over-quotations' distribution can be useful as an initial exploratory technique for two goals. First, over-quotations' distribution appears quite a good indicator of the international recognition of a city. The different international role of Brussels compared to the other cities clearly emerges by using this indicator. Second, the identification of peaks of over-quotation outside of the city's country can help in detecting
trivial errors of homonymy that could be verified by content analysis such as in the case of Bologna in our sample.

<figure><img src="image_60.png" /><figcaption>Fig. 1: Geographical distribution of over-quotations of the Bologna and Brussels at global level.</figcaption></figure>

**Content analysis: which are the topics associated to the city?** In a second phase, we consider all tweets mentioning a city (not only the geo-tagged) in order to identify topics addressed by Twitter users in relation to the city. This technique is meant to identify the items of the brand of the city that are recognised all around the world. Considering the large amount of tweets for each city, only automatic techniques of analysis are feasible. Yet, it is important to underline that in content analysis of tweets, even when we are using an automatic technique, qualitative interpretative work is equally necessary. Often users employ abbreviations and special expressions that can be not so immediate for the researcher.

We start by considering the most frequent hashtags<fnref n="6" /> for each city (Table 2). In order to evaluate the representativeness of hashtags, it is very important to consider how many distinct users mention them. In this project, we establish that when the ratio between the number of tweets mentioning the hashtag and the number of distinct users sending tweets

<footnote n="6">⁶ Hashtag analysis is supposed to be more effective than simple word analysis for identifying the topic of a tweet. Indeed, tweets users employ hashtags to connect their tweets to a general discussion.</footnote>
is higher than 10, we can exclude the hashtag because it can be considered as a form of
advertisement and it does not reflect a shared discussion.

Once excluded non-representative hashtags, results are quite generic. If we take the first 20 hashtags in each city, we find several hashtags generated by special events and only some general tags related to the city. There are some common topics such as job and travel that emerge in every city, then for Bologna and Marseille, football clearly emerges as the main topic related to the city, for Brussels, the link with Europe is surely the main characteristic that we can identify. Conversely, for Edinburgh, there is no information except for the One Direction concert and the referendum in Scotland (#indyref) that can be detected with an aggregated analysis of tweets over a one-month period.

In order to avoid this problem of few large peaks across the aggregate data, we try to disaggregate by analysing the frequency of hashtags at the level of a week. Indeed, this technique is very useful to identify hashtags related to specific events through time. For example in the case of Bologna, we can detect the impact of the audition of the XFactor and the IT fair SMAU the first week, a convention of a political party the second week, the projection of the Lego Movie film the third week and the gay pride last week. Yet, this technique doesn't help us in our mission of finding items of the image of the city. There are very few contents that are constantly mentioned over the four weeks. We find again job, travel, football, Europe... These are still really generic.

Another possible way to carry out a thematic analysis consists in analysing the list of hashtags through a qualitative content analysis. As a first step, non-pertinent (for homonymy) and non-representative (ratio tweets/user) hashtags have to be excluded. Then, hashtags mentioned a sufficient number of times (in this project 50 times over the period of observation) can be classified in different categories composing the brand of a city. We propose to use 14 categories: crime, culture, economics, politics (national/local), international politics, social affairs, transport, urban planning and life, sport, tourism, food, fun, weather and places (other places mentioned in the same tweet with the city). This analysis can be partially automatised by building a dictionary that connects hashtags to categories.

The advantage of this technique is that it can provide a more detailed view of the city's topics that draw people attention on Twitter. Then, through a treemap, it is possible to easily visualise which hashtags are more used inside each category (Figure 3). This tool can be very useful for decision makers that can have a general point of view on topics related to the city and that can quickly identify hot topics that affect the image of the city at national and international level. If we consider the example of our four case studies, we can note that this technique can produce some interesting insight.

As an example, we will present the case of Bologna. In this corpus, we have a total of 299 hashtags used at least 50 times in 92,797 tweets. We exclude 32 hashtags because non-representative and 26 because non-pertinent.<fnref n="7" /> Figure 2 shows the distribution of hashtags related to Bologna by category and Figure 3 shows the distribution of tweets by hashtag and hashtags' category through a treemap.

<footnote n="7">⁷ We also excluded name of the city and the city's country.</footnote>
<table><caption>Table 2: Most frequent hashtags in tweets mentioning a city (2-29 June). Red = possible errors; grey = non-pertinent; white = generic; green= events; yellow; = city related; blue = places.</caption><caption>BOLOGNA</caption><thead><tr><th>hashtag</th><th># of tweets distinct users ratio</th></tr></thead><tbody><tr><td>bologna</td><td>31,720 10,056 3</td></tr><tr><td>bolonia</td><td>2,702 2,493 1</td></tr><tr><td>news</td><td>2,553 150 17</td></tr><tr><td>tarifa</td><td>2,351 2,261 1</td></tr><tr><td>andalucía</td><td>2,244 2,177 1</td></tr><tr><td>campogibraltar</td><td>2,231 2,162 1</td></tr><tr><td>cádiz</td><td>2,116 2,074 1</td></tr><tr><td>italy</td><td>1,256 817 2</td></tr><tr><td>xf8</td><td>976 554 2</td></tr><tr><td>lavoro</td><td>779 134 6</td></tr><tr><td>meteo</td><td>752 71 11</td></tr><tr><td>thelegomovie</td><td>743 741 1</td></tr><tr><td>ss16</td><td>740 14 53</td></tr><tr><td>smau</td><td>726 337 2</td></tr><tr><td>eritrea</td><td>575 133 4</td></tr><tr><td>italia</td><td>562 393 1</td></tr><tr><td>bolognafc</td><td>560 82 7</td></tr><tr><td>roma</td><td>553 253 2</td></tr><tr><td>calciomercato</td><td>528 123 4</td></tr><tr><td>seriea</td><td>493 155 3</td></tr></tbody></table>

<table><caption>BRUSSELS</caption><thead><tr><th>hashtag</th><th># of tweets distinct users ratio</th></tr></thead><tbody><tr><td>Brussels</td><td>19,010 10,671 2</td></tr><tr><td>Bruxelles</td><td>12,285 6,903 2</td></tr><tr><td>Belgium</td><td>5,43 2,751 2</td></tr><tr><td>Bruselas</td><td>3,190 2,084 2</td></tr><tr><td>g7</td><td>3,039 2,271 1</td></tr><tr><td>Job</td><td>2,951 312 9</td></tr><tr><td>Jobs</td><td>2,791 344 8</td></tr><tr><td>Eu</td><td>2,621 1,927 1</td></tr><tr><td>catalanswanttovote</td><td>2,314 1,704 1</td></tr><tr><td>Ukraine</td><td>2,049 1,580 1</td></tr><tr><td>Inclusive</td><td>1,567 1,563 1</td></tr><tr><td>Innovative</td><td>1,554 1,550 1</td></tr><tr><td>News</td><td>1,508 385 4</td></tr><tr><td>Bel</td><td>1,262 1,181 1</td></tr><tr><td>Europe</td><td>1,186 813 1</td></tr><tr><td>Belgique</td><td>1,178 553 2</td></tr><tr><td>referendumya</td><td>1,086 930 1</td></tr><tr><td>lt</td><td>1,065 49 22</td></tr><tr><td>Afp</td><td>1,058 877 1</td></tr><tr><td>Travel</td><td>952 577 2</td></tr></tbody></table>
<table><caption>Table 2: (Continued). Most frequent hashtags in tweets mentioning a city (2-29 June). Red = possible errors; grey = non-pertinent; white = generic; green= events; yellow; = city related; blue = places.</caption><thead><tr><th colspan="4">EDINBURGH</th></tr><tr><th>hashtag</th><th># of tweets distinct</th><th>users ratio</th><th></th></tr></thead><tbody><tr><td>edinburgh</td><td>82,200</td><td>28,735</td><td>3</td></tr><tr><td>new</td><td>19,955</td><td>5,364</td><td>4</td></tr><tr><td>wwa</td><td>11,339</td><td>11,141</td><td>1</td></tr><tr><td>wwatour</td><td>9,600</td><td>8,368</td><td>1</td></tr><tr><td>job</td><td>9,294</td><td>374</td><td>25</td></tr><tr><td>jobs</td><td>8,713</td><td>552</td><td>16</td></tr><tr><td>scotland</td><td>8,626</td><td>3,991</td><td>2</td></tr><tr><td>niallsnotes</td><td>7,870</td><td>1,752</td><td>4</td></tr><tr><td>lda</td><td>7,005</td><td>1,938</td><td>4</td></tr><tr><td>hq</td><td>6,126</td><td>2,435</td><td>3</td></tr><tr><td>nn</td><td>4,494</td><td>1,842</td><td>2</td></tr><tr><td>indyref</td><td>3,394</td><td>1,557</td><td>2</td></tr><tr><td>ns</td><td>2,764</td><td>1,443</td><td>2</td></tr><tr><td>gumball3000</td><td>2,516</td><td>1,161</td><td>2</td></tr><tr><td>onedirection</td><td>2,116</td><td>888</td><td>2</td></tr><tr><td>glasgow</td><td>2,047</td><td>822</td><td>2</td></tr><tr><td>niallspotato</td><td>1,867</td><td>567</td><td>3</td></tr><tr><td>london</td><td>1,774</td><td>814</td><td>2</td></tr><tr><td>travel</td><td>1,578</td><td>605</td><td>3</td></tr><tr><td>notasstyles</td><td>1,538</td><td>458</td><td>3</td></tr></tbody></table><table><thead><tr><th colspan="4">MARSEILLE</th></tr><tr><th>hashtag</th><th># of tweets distinct</th><th>users ratio</th><th></th></tr></thead><tbody><tr><td>Marseille</td><td>33,756</td><td>13,049</td><td>3</td></tr><tr><td>Om</td><td>4,242</td><td>2,575</td><td>2</td></tr><tr><td>marseillelife</td><td>3,676</td><td>3,1225</td><td></td></tr><tr><td>Teamom</td><td>3,663</td><td>2,327</td><td>2</td></tr><tr><td>France</td><td>3,507</td><td>1,179</td><td>3</td></tr><tr><td>Alg</td><td>1,735</td><td>1,612</td><td>1</td></tr><tr><td>cdm2014</td><td>1,529</td><td>1,285</td><td>1</td></tr><tr><td>olympique</td><td>1,498</td><td>1,145</td><td>1</td></tr><tr><td>Worldcup</td><td>1,397</td><td>1,339</td><td>1</td></tr><tr><td>Algrus</td><td>1,373</td><td>1,102</td><td>1</td></tr><tr><td>Football</td><td>1,104</td><td>878</td><td>1</td></tr><tr><td>Algerie</td><td>947</td><td>841</td><td>1</td></tr><tr><td>Paris</td><td>945</td><td>617</td><td>2</td></tr><tr><td>shake14</td><td>910</td><td>383</td><td>2</td></tr><tr><td>Lfc</td><td>807</td><td>769</td><td>1</td></tr><tr><td>Travel</td><td>796</td><td>548</td><td>1</td></tr><tr><td>Mufc</td><td>728</td><td>712</td><td>1</td></tr><tr><td>Marsella</td><td>676</td><td>448</td><td>2</td></tr><tr><td>Fcb</td><td>665</td><td>653</td><td>1</td></tr><tr><td>Mercato</td><td>648</td><td>536</td><td>1</td></tr></tbody></table>
<figure><img src="image_61.png" /><figcaption>Fig. 2: Distribution of hashtag in tweets mentioning Bologna between the 2nd and the 29th June 2014.</figcaption></figure>

Through this method, we can clearly confirm that culture and sport are the main topics related to Bologna. As regards “culture”, most frequent hashtags are related to national and international events happening in the city (such as the audition of the musical TV program XF and the release of the Lego Movie). Yet, there are numerous local events (festival, concerts, cinema and art events) that are frequently mentioned. As regards “sport”, football is a key topic, yet we can also see the effect of the World Cup.

If we focus on aspects related to spatial analysis and urban planning, several insights that could be useful to decision makers can be listed:

- Few *places inside the city* are mentioned: particular places and streets (#piazzamaggiore), cultural venues (#cassero), commercial spaces (#interporto), monuments and historic building (#sanluca). It is really interesting that the Portici (arcades), which are considered as one of the main symbols of Bologna, do not appear in the list of hashtags.

- Some *urban questions* emerge. For example, #Nopianocasa hashtag is related to a national social movement for the right of habitation against violent evictions.

- Items of the *economic sector* mentioned at international level. The SMAU, IT fair, is mentioned in several tweets. Then, we find the role of the university through several hashtags. Also the fashion sector is cited (in particular the local retailer yoox) and the motor sector with the hashtag #ducati.

- Very few *content related to international politics*. We find only some tweets related to a public mobilisation against the European meeting of the 11 July 2014 (#civediamolundici).

- People communicate about different types of transport, especially highways (#a1, #a14) and trains (#trenitalia), but also bicycles (#bici), flights (#aereo) and bus. What is interesting is the hashtag #viabilibo that allows to monitor the impact of road works.

- The hashtag #mybologna is used on Twitter to indicate what people like of Bologna. This hashtag can be a very precious source of information about the perception of the
<figure><img src="image_62.png" /><figcaption>Fig. 3: Treemap of hashtags in tweets mentioning Bologna between the 2nd and the 29th June 2014.</figcaption></figure>
city. 139 different users have used it 266 times over the four weeks. Co-hashtag analysis<fnref n="8" /> is a useful quick technique to see the context of use of a hashtag (see Figure 4).

- Co-citation with other places: in numerous tweets (13,182), Bologna is cited with other places, notably cities such as Roma, Florence and Milan and several others. Yet, only Italian places are mentioned.

<figure><img src="image_63.png" /><figcaption>Fig. 4: Co-hashtag analysis of the hashtag #mybologna in tweets mentioning Bologna (2-29 June 2014). Size of nodes is proportional to word frequency</figcaption></figure>

# 4 Tweets inside the city

Through the first method, we have two main findings: (1) geo-tagged tweets seem to contain more space-related and city-related content; (2) the majority of geo-tagged tweets are located inside and around the city. Given that, as a second method, we propose to focus exclusively on tweets geo-tagged in the metropolitan areas of the four cities.<fnref n="9" /> Doing this, we expect to reduce the noise related to occasional events and find more spatial-related content. Moreover, the analysis of discourses of users located in city is expected to give insights of Twitter communication at local level. As in the previous case, first we focused on spatial analysis, then on content analysis.

**Spatial analysis: where are located in the city the users tweeting?** The sample is constituted of 105,188 tweets from 7,357 users for Bologna; 146,040 tweets from 8,941 users for Brussels; 193,547 tweets from 11,713 users for Edinburgh; 288,453 tweets from 25,730 users for Marseille. So, once again, it is necessary to underline that this is just a very small part of the global Twitter activity. As a first step, we can analyse the distribution of tweets in the city in order to see if there are some places where people tweet more than other. The

<footnote n="8">⁸ The analysis of network of co-citation of hashtags in the same tweet.</footnote>

<footnote n="9">⁹ City areas have been selected according ESPON standards NUTS3 coordinates.</footnote>
comparison with the population density helps us to identify urban places where Twitter activity is concentrated. Globally, we observe that the distribution of tweets is similar to the distribution of population inside all four cities, yet some places where Twitter activity is particularly intensive can be identified.

Tweets per capita by municipality in Marseille

<figure><img src="image_64.png" /><figcaption>Fig. 5: Geographical distribution of tweets per capita in Marseille.</figcaption></figure>

In general, we can see that tweets are spread out all around the city quite proportionally to the population density. Yet, in every city there are same places that emerge. For example, in Marseille (Figure 5) tweets are distributed in every part of the city, yet we can locate notably the old port, the stadium and the train station. At the borough level, we see a concentration (200-280 tweets per 1000 inh) in the 2nd borough were we can find a lot of touristic and cultural attractions as the new museum MUCEM, a new mall or the city hall. We can note that these sectors, like the 16th (North), concentrate a lot of young people. In Bologna (Figure fig:ch8-fig6) the situation is quite different. Tweets are concentrated especially in the downtown, in the area around the main square, Piazza Maggiore and along the main route that connects the city from North to South (via ugo Bassi – via Rizzoli). Moreover, we can identify some places where there is a strong Twitter activity, such as the airport, the fair, the hospitals and the stadium. Also university buildings clearly appear as tweeting places.

**Content analysis: which are the topics associated to the city?** As a second step, we try to identify main topics that are debated inside the city. Also with this corpus, most
Tweets per capita by census area in Bologna

<figure><img src="image_65.png" /><figcaption>Fig. 6: Geographical distribution of tweets per capita in Bologna.</figcaption></figure>

frequent hashtags are not useful to catch the diversity of the discussion. So, considering the good results of the first corpus, we use the same technique, quantitative analysis of hashtag, on the corpus of geo-tagged tweets inside the city. We focus again on the example of Bologna.

We have a total of 282 hashtags used at least 20 times<fnref n="10" /> in 19,194 tweets. We excluded 69 hashtags because non-representative.<fnref n="11" /> The results of this analysis are very unexpected. Even if tweets are geo-tagged, they often don't speak about space and even less about cities. In our corpus, 44% of tweets including hashtags talk about sport and notably about the world cup. The coincidence with this big event makes it very difficult to know if it is an accidental effect or it is usual that geo-tagged tweets don't speak of the city. Nevertheless, we can note that 25% of tweets are tagged as “fun”: they are used principally to share emotions, photos or organise fun activities (parties, hang-out etc.). So, these tweets are surely related to space yet the communication often is just expressive and phatic. Conversely, there are very few tweets related to urban questions and tweets related to transport and international politics are completely absent. Figure 7 shows the repartition of tweets by category. We use the same categories as presented above.

<footnote n="10">¹⁰ Considering that tweets geo-tagged in the city are less than tweets mentioning the city we take a lower level of hashtag frequency in order to have a comparable amount of hashtags.</footnote>

<footnote n="11">¹¹ We also excluded name of the city and the city's country.</footnote>
Based on this case study, we can make the hypothesis that geo-tagged tweets are not so useful for studying the city. Even if located in the city, they generally are very few (not representative of the general discussion) and don't concern urban life, but rather private life. The only possible exploitation that we see concerns tweets that mention photos. As an example, we verify the content of tweets including the following hashtags: #picoftheday, #photooftheday, #photo, #igers, #instamood, #igersbologna and #Instagood (the last four are related to the photo sharing application Instagram). In this way, we can build a corpus of 518 tweets including images of the city that can give us insight of the perception of some places. Figure 8 represents the word cloud generated by these tweets can provide an overview of the type of content of these messages.

## 4.1 City's influencers on Twitter

We have tested two methods so far. As a first method, we analysed tweets mentioning the name of the four cities in all European languages. This technique was useful to have an indicator of the recognition of a city at international level and to have a general point of view on topics related to the city. As a second method, we analysed tweets geo-tagged in the city. This method should help us to study more precisely the discussion related to the city, yet actually this corpus proved to be very general. People inside the city rarely speak about the city, most often their communication is emotional in nature.

In order to bypass this issue and to find more precise insights concerning urban life, we propose a third method. By identifying about 40 influencers on Twitter for each city (for example members of the city council, chamber of commerce, big local enterprises, local political leaders, local ONG leaders, blog stars...) and by analysing their tweets, we expect to observe in real-time the themes related to the city and the metropolitan area. This analysis will provide a representation of the public debate in the digital sphere, especially with a focus on the metropolitan issues that are emerging.

Selecting a specific sample of Twitter users is meant to avoid the noise related to sport and fun that we found in the previous corpora. As influencers, we privilege public people and institutions rather than media. For this test, influencers of each city have been chosen by an expert in urban planning that have lived in the city. If this experiment is successful, in a project at larger scale, influencers have to be chosen by a panel of decision makers and experts working in the city.

Considering that this sample of users is smaller than the two previous ones, it was possible to collect tweets on a longer period of one year from the 1 July 2013 to the 30 June 2014. For Bologna the corpus consists of 60,173 tweets sent by 44 influencers, for Brussels 32,592 tweets by 39 influencers, for Edinburgh 53,182 tweets by 42 influencers, for Marseille 46,208 tweets by 50 influencers. These tweets are not equally distributed along the period. By considering the time series of tweets in the four cities, two phenomena may be observed: (1) tweets are distributed according a weekly cycle: influencers usually tweet less the weekend. Moreover, in all cities there are some less active periods during August 2013 and Christmas holidays. This fact can be considered a good indicator of the link between the professional life of
<figure><img src="image_66.png" /><figcaption>Fig. 7: Treemap of the hashtags used in tweets geo-tagged in Bologna between the 2nd and the 29th June 2014.</figcaption></figure>
the influencer and the communication conveyed on Twitter. (2) Along the year, tweets are increasing.

We study this corpus principally through content analysis.<fnref n="12" /> Once again, quantitative techniques are preferable considering the amount of data to study. In order to be effective and useful for decision makers, we propose a methodology organised in four phases:

1. General overview of the corpus. As a first step, it is possible to have a global idea of the contents of the discussions through the analysis of 50 most frequent hashtags.

2. Diachronic overview of the corpus. Then, visualising the most frequent hashtags per month through a stream graph will allow observing the evolution of the discussion. Decision makers can easily identify themes that occupy public debate and follow their development along time.

3. Focus on specific themes. Once identified an interesting theme, decision makers can obtain a summary of the discussion through three techniques:

(a) Most retweeted messages is an quick technique to know the most popular contents that occupy the discussion;

(b) Co-hashtag analysis allows to visualise how the theme is connected to others;

(c) Word-frequency analysis (and the visualisation through word clouds) allows identifying the main elements that are mentioned in the debate concerning the selected theme.

4. Qualitative analysis of tweets. As a final step, decision markers may need to perform a close reading of the tweets themselves in order to have a complete view of the debate.

In the next paragraphs, we present some examples of this analysis workflow. Once again, we will focus on Bologna.

Even at a first look, the content of this corpus appears strongly different from the previous ones. By considering the most frequent hashtags along the year of observation (Annex 14), Sport remains a very important topic (first hashtags are #bolognafc the football team and #granarolo #virtus, the basket team), yet “fun” is practically inexistent. “Unibo”, the hashtag of the University of Bologna, is largely used and then there are several keywords related to politics (#ilgiornodopononsuccedemaiuncazzo related to political scandals, the main political parties #pd #ms5 and a few mentions for the right party #pdl) and social life, notably social movements (#19o for the strike of 19 October 2013, #occupymensa for the student strike, #sollevazione and #assedio generally related to social demonstrations).

If we analyse the discussion across time, we observe a great variety of topics. Stream graph visualisation (Figure 9) provides a diachronic view of most frequent hashtags per month. The hashtags that are constantly present are very few: #Unibo for the university, #openconsiglio_bo related to the city council activity, #bambini (children) and #scuola (school), political parties and the prime minister #renzi and finally #viabilità (mobility). Then, contents change across time. In the first months, keywords related to social movements and strikes are more frequent: #omophobia in August and September 2013; #socialstrike, #sollevazione, #assedio and #occupymensa in October and November. Then, in 2014, discussion is principally political until the peak of May 2014 with the European elections.

<footnote n="12">¹² On these samples, geo-tagged tweets are practically inexistent.</footnote>
<figure><img src="image_67.png" /><figcaption>Fig. 8: Word clouds of the terms included in tweets mentioning photos in tweets geo-tagged in Bologna between the 2nd and the 29th June 2014. General terms are excluded. Max 150 words shown.</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>Fig. 9: Stream graph of top hashtags per month in tweets of Bologna's influencers (July 2013 – June 2014). Sport hashtags and "#Bologna" are excluded.</figcaption></figure>
If we focus on aspects related to spatial analysis and urban planning, several insights can be useful to decision makers:

- **Discussion about the city brand.** In last months, Bologna council organised an important international competition for changing the brand of the city. Twitter has been a good terrain for the promotion of this project (in our corpus 140 tweets sent by 22 users mention "brand"). As an example, we use co-hash tag network analysis to have an overview of the discussion about brand. Through this graph (Figure 10), we can easily identify the principal themes related to the competition such as food, art, tourism and social.

<figure><img src="image_69.png" /><figcaption>Fig. 10: Co-hash tag analysis in tweets of Bologna's influencers mentioning "brand".</figcaption></figure>

- **Discussion about city politics.** Several hashtags are related to city politics, notably #openconsiglio_bo and #iperbole.

- **Discussion about energy.** Energy is also a key topic especially through the hashtag #PAES (Patto dei Sindaci - Piani d'azione per l'energia sostenibile, Covenant of Mayors - Action plans for sustainable energy) that is present along all the period. Bioarchitecture and energy-sustainable housing are frequently discussed.

- **Discussion about transports.** It is interesting to know that main topics are bicycles (#bici, #ciclabile) and buses (#bus).
- Discussion about school. As said #scuola (school) and #bambini (children) are constantly present in the Twitter communication, yet these hashtags are mentioned in very varied contexts, from crimes to political declarations to the promotion of events.

- Discussion about enterprises of the metropolitan area. Several industries are mentioned, #coop and #coopservice, but also the boycott actions against Granarolo and the social demonstrations at Ikea.

- Discussion about the environment. Several hashtags are related to the environment, notably #bolognasostenibile, but also #ambiente (environment), #alberi (trees), #waterbologna...

- Discussion about the smart city. The impact of digital technologies on the urban life also appears: #BolognaSmartCity, #Smartcity, #agendadigitale, #bolognaagendadigitale.

- Discussion about San Luca, a church on the hills of Bologna. The crowdfunding campaign for the arcade of San Luca is diffused in several tweets such as "Cosa rappresenta il Portico di #SanLuca per #Bologna?Potete raccontarlo compilando un questionario online @Twiperbole http://t.co/mZw6GUqK5z" or "#SanLuca: parte la raccolta fondi per il portico http://t.co/v7S4iY4cGC 10 Ottobre il primo evento #Bologna #sostienilportico @twiperbole". San Luca can be considered as one of the main symbols of the city.

- Discussion about change. Several hashtags related to change are used: #cambiamento, #cambiamotutto, #cambiareverso, #cambiaverso.

- Discussion about crisis. Hashtags like #crisi (crisis) and #austerity are often used. We have 362 tweets sent by 30 users that mention them. Through word frequency analysis of this corpus (Figure 11), we can identify the main content related to the crisis. The discussion generally involves harsh terms with words like "sollevazione", "assedio" and "ipocrisia" related to social demonstrations. The strong connection with Christmas is also interesting. Finally, we can also identify a positive attitude through some words: "ribaltiamolausterity", "anticrisi", "opportunita", "autoriduzione".

<figure><img src="image_70.png" /><figcaption>Fig. 11: Word clouds of tweets from Bologna influencers mentioning “crisi” and “austerity”. The terms “crisi”, “austerity” and “bologna” have been excluded from the representation.</figcaption></figure>
Also European questions are touched upon in the discussions of influencers. Most of the mentions are related to the European elections (#EP2014, #Europee), yet there are also other mentions of Europe (#Europa) in other contexts such as a conference on the value of the arcade of Bologna in an European context and then some social movements such as #Civediamolundici. We can also note the strong attention for the visit of Alexis Tsipras. (RT @tsipras_eu: @LidiaSenra "we are going to build the Europe of workers bread and dignity" #AGEenEuropa #ELChangeEurope http://t.co/NqZXx...). What is interesting is that the most retweeted message of this corpus does not relate to European elections but to the possible effect of Renzi election on the European policy "RT @matteorenzi: Un risultato storico. Commosso e determinato adesso al lavoro per un'Italia che cambi l'Europa. Grazie #unoxuno. @pdnetwor...".

In the corpus, there are 1,919 tweets sent by 36 users that mention “euro*”. We generate a co-hash network and a word cloud from this sample (Figure 12 and Figure 13). The first graph is very useful to identify different items of the discussion: the parties, links to the crisis and social demonstration, the Italian policy of Renzi (80euro) and also the presence of several tweets related to sport. Conversely, the word cloud helps us to underline the different weight of these items. After excluding contents related to sport, we see that political campaign “cominciatu” clearly emerges as the main content.

<figure><img src="image_71.png" /><figcaption>Fig. 12: Co-hash analysis in tweets of Bologna's influencers mentioning “euro” and “ep2014”.</figcaption></figure>

Given all that, Bologna’s influencer’s discussion is clearly centred on the life of the city. By building a good sample, decision makers can be updated about the key questions related
to their work. Through Twitter data, they can have a multiple viewpoint on their present and future actions and on the needs of citizens and they can follow them in real-time.

# 5 Conclusion

Through this case study, we add important insights about possible uses of Twitter data for public policy. As said, particular attention has been paid to methodological issues. We focus on the phases of the data collection and on the tagging process. As a final result, we could validate the interest of Twitter data for studying the city brand.

As regards to the selection of the data sample, first of all we can observe that methods based on the use of Web 2.0 data are grounded on a delicate balance between advantages and drawbacks of these data. On the one hand, these data promise to provide very new information about social life. On the other hand, the use of these data raises several methodological issues. In this project, we note two main issues: (a) the different uses of Twitter, notably its ephemeral and expressive use (Bruns & Moe, 2014). Yet, if we consider tweets mentioning a city, the noise related to fun and emotions does not prevent to identify key topics related to the city. At the local level, the problem of the different uses of Twitter can be effectively avoided by analysing a selection of tweets sent by a restricted number of users identified as city's influencers. This project clearly demonstrates that a pertinent choice of Twitter accounts will reduce the noise found in the first two samples. (b) The small amount of geo-tagged tweets. Actually, geo-tagged tweets constitute just a very small percentage of global Twitter activity (Gerlitz & Rieder, 2013). Even if this is surely a very big issue for the selection of data, two observations may be made on the basis of the experience of this project. First, if geo-tagged tweets are not so useful as absolute values they can be used in a comparative way. In the first method, we see that the analysis of the over-quotation distribution of city's mentions can be considered a good indicator of the international recognition of a city. Moreover, in the second method, we see that the content of geo-tagged tweets is not so related to space, so geo-tagged tweets are not necessarily the good entry for studying spatial questions. We agree with Crampton et al (2013) on the importance of going beyond geotagging.

As regards to how a big data sample can be tagged, we can note that tagging data was necessary in order to carry out content analysis of tweets and it is not evident to choose between quantitative and qualitative methods. Considering the size of Twitter datasets, automatic tagging procedures are surely more desirable because less time-consuming. Yet, the use of Twitter data raises the opposition between the necessity of automatic procedures of tagging and the high possibility of tagging errors. Besides the initial fears, results of automatic techniques for analysing tweets mentioning a city are encouraging. The analysis of most frequent hashtags can be a very efficient technique for identifying public events happening in the city (by week). Moreover, word clouds of geo-tagged tweets can have two main objectives: (1) finding errors due to homonymy; (2) identifying the main elements of the image of a city in different countries. So, we can say that automatic methods can be a profitable strategy, even if a preliminary effort of disambiguation is necessary in order to anticipate main possible errors (other meanings of the name of the city, other places with the same name).
Then, more qualitative methods can provide a more complete overview of the themes discussed about the city. We propose to manually categorise tweets and to represent their distribution in categories through a treemap. This visualisation tool can be very useful for decision makers that can have a general point of view on topics related to the city and that can quickly identify hot topics that affect the image of the city at national and international level. This technique can be partially automatized by building a database of correspondences between hashtags and categories. Yet, it is important to underline that in content analysis of tweets, even when we are using an automatic technique, qualitative interpretative work is equally necessary. The style usually employed for writing tweets can be not so immediate for the researcher or the decision maker.

As a conclusion, we can note that, before the emergence of the Web 2.0, decision-makers could obtain insights about public opinion principally by media or by more expensive techniques such as interviews, focus groups and public surveys. Obviously, Twitter users cannot represent statistically all population of a city. Yet, tweets are expected to give innovative insights on social life useful for public policy. This project shows that, through Twitter data, it is possible to identify new and more varied sources of information about the brand of the city such as the interests of people living in the city or speaking about the city, the attractiveness of some special events happening in the city, or the connections of the city at the international level. These sources can be varied and uncontrolled as in the first method and they can provide quantitative overall measures of the international recognition of the city. Or, by selecting a specific sample of Twitter accounts (called influencers), decision-markers can have a real-time and on-going feedback about the key questions related to the city. Decision-makers can distinguish permanent topics and emerging topics by following the general use of hashtags and they can focus on specific hashtags related to the political agenda. Yet, based on these first positive insights, future research has to been carried out in order to validate these results on a longer period.
<figure><img src="image_72.png" /><figcaption>Fig. 13: Word cloud of tweets of Bologna's influencers mentioning “euro” and “ep2014” excluding tweets containing “euroleague” and “eurochallenge”. In the representation the following words have been excluded: Bologna, Emilia, Euro*, EP.</figcaption></figure>
Bibliography

[1] Borra, E. & Rieder, B. (2014). Programmed method: developing a toolset for capturing and analyzing tweets. *Aslib Journal of Information Management*, 66, 3, 262-278.

[2] Bruns, A. & Moe, H. (2014). Structural Layers of Communication on Twitter. In Weller, K. et al. (Eds) *Twitter and Society* (pp. 15-28). New York: Peter Lang.

[3] Crampton, J. W., Graham, M., Poorthuis, A., Shelton, T., Stephens, M., Wilson, M. W., & Zook, M. (2013). Beyond the geotag: situating ‘big data’and leveraging the potential of the geoweb. *Cartography and Geographic Information Science*, 40, 2, 130-139.

[4] Davis Jr., C. A., Pappa, G. L., de Oliveira, D. R. R. & de L. Arcanjo, F. (2011), Inferring the Location of Twitter Messages Based on User Relationships. *Transactions in GIS*, 15: 735-751. DOI: 10.1111/j.1467-9671.2011.01297.x

[5] Gerlitz C. & Rieder B. (2013). Mining One Percent of Twitter: Collections, Baselines, Sampling. *M/C Journal*, 16, 2.

[6] Sakaki T., Okazaki M. & Matsuo Y. (2010). Earthquake shakes Twitter users: real-time event detection by social sensors. *Proceedings of the 19th International Conference on World Wide Web*, April 26-30, Raleigh, North Carolina, USA.
# Building a recommender system using multilingual multiscript tweets

Ritesh Shah¹², Christian Boitet<fnref n="1" />, and Pushpak Bhattacharyya<fnref n="2" />

**Abstract.** We describe an ongoing work aimed at building a Recommender System by applying some innovative Natural Language Processing (NLP) techniques on multilingual, multiscript Twitter data (tweets). Large quantities of tweets in some Indian languages, French and English will be used for processing. Multilingual tweets often exhibit some degree of code-mixing, style variations and disfluencies. Therefore, we rely on an interlingual content extraction approach and apply pertinent NLP techniques on tweets in a modular way. The modules of the multilingual Recommender System are preprocessing, multilingual morphological and presyntactic analysis, named entities identification, interlingual lexical disambiguation (using UWs<fnref n="3" />, UNL<fnref n="4" /> interlingual lexemes), content extraction (with reference to a very simple ontology or knowledge base) and polarity determination.

**Keywords:** SRecom, recommender system, twitter, tweet, multilingual, multiscript, code-mixing, UNL, UW, NLP, French, Indian languages.

## 1 Introduction

Recommender systems (SRecom(s)) are useful when built upon services like Twitter where social information exchanges are characterised by both speed and volume. Recommendations based on such large amounts of information reportedly enable users to make quick and better decisions. In mining this information, nevertheless the magnitude and the inherent diverse characteristics of tweets may pose unique problems. Also, the design of SRecom(s) needs to be driven by specific tasks, information needs, and item domains [6]. It is then essential to identify, accommodate and address these concerns while building a SRecom based on tweets. Presently, the approaches mostly used in building SRecom(s) are collaborative filtering [20] and content-based approaches [11]. Furthermore, the strengths and limitations of each of these approaches have also led to hybrid methodologies, such as those of [2, 3, 13].

<footnote n="1">¹ GETALP-LIG, Université Grenoble-Alpes,
Grenoble, France</footnote>

<footnote>ritesh.shah,christian.boitet@imag.fr</footnote>

<footnote n="2">² CFILT, Indian Institute of Technology,
Mumbai, India</footnote>

<footnote>pb@cse.iitb.ac.in</footnote>

<footnote n="3">³ Universal Words</footnote>

<footnote n="4">⁴ Universal Networking Language (http://www.undl.org)</footnote>
In this paper, we propose an implementation of a *multilingual Recommender System* (m_SRecom) based on tweets while keeping in mind the relevant concerns. Our motivations in building a m_SRecom are as follows.

1. A significant potential demand for tourist-oriented recommendations as exhibited by numerous tweets.

2. Availability of NLP resources and competences for several languages in our lab including some Indian languages, French and English.

3. Availability of large Twitter datasets (through the Twitter API) concerning the same sub-domain of the tourism domain in several languages.

In our work, we plan to apply NLP techniques on multilingual tweets and to make use of an interlingual content extraction approach. We select some Indian languages, French and English as the linguistic base and confine our m_SRecom to provide tourism related recommendations. Our approach demands careful investigation of the underlying multilingual issues and, more importantly allows us to assess the applicability of NLP in implementing an effective m_SRecom. Altogether, we aim at handling several scientific and technical challenges set forth by the micro-blog characteristics, the specific recommendation needs and the proposed multilinguality.

In the subsequent sections, we begin with a short background on Twitter and tweets. This is followed by a description of the tweet collection methodology and a preliminary study related to code-mixing and geo-information in tweets. The concluding section contains a description of each of the NLP modules proposed for the m_SRecom.

# 2 Background

## 2.1 Twitter

*Twitter* is a publicly available rich source of social data in the form of short status updates called *tweets*. Twitter produces 45 Giga<fnref n="5" />(G) tweets per quarter as of early 2015. This roughly translates to 500 Mega<fnref n="6" />(M) tweets per day. Since its public launch in 2006, the increase in tweets produced worldwide has been phenomenal as shown in Table 1. It is notable that regional distribution of tweets is difficult to estimate because the tweets are usually devoid of reliable geotagged information.

Twitter provides access to tweet data primarily through the REST<fnref n="7" /> APIs and the Streaming APIs. The REST APIs are well suited for running searches against the index of recent tweets, whereas a Streaming API allows near real-time access to a subset of tweets by random sampling, for instance. Access requests from the developers are authorised through an OAuth<fnref n="8" /> service. The API calls are limited per API method and also depend on the authentication mechanism explained in the Twitter documentation.

<footnote n="5">⁵ 1 Giga = $10^9$</footnote>

<footnote n="6">⁶ 1 Mega = $10^6$</footnote>

<footnote n="7">⁷ Representational State Transfer</footnote>

<footnote n="8">⁸ Open standard for Authorization</footnote>
<table><caption>Table 1: Growth rate: Number of tweets generated worldwide.</caption><thead><tr><th>Year</th><th>2006</th><th>2007</th><th>2008</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td># tweets per quarter</td><td>60K</td><td>400K</td><td>100M</td><td>225M</td><td>5.85G</td><td>18G</td><td>30.6G</td><td>36G</td><td>45G</td></tr><tr><td># tweets per month</td><td>20K</td><td>133.3K</td><td>33.3M</td><td>75M</td><td>1.95G</td><td>6G</td><td>10.2G</td><td>12G</td><td>15G</td></tr><tr><td># tweets per day</td><td>667</td><td>4.44K</td><td>1.11M</td><td>2.5M</td><td>65M</td><td>200M</td><td>340M</td><td>400M</td><td>500M</td></tr></tbody></table>

<figure><img src="image_73.png" /><figcaption>Fig. 1: An example of a tweet. Source: https://twitter.com</figcaption></figure>

## 2.2 Tweets

A 'Tweet' is any message posted to Twitter which may contain images, videos, links and up to 140 characters of text.

Underlying a tweet is a rich set of metadata (typically 5 KB in size) in the form of embedded attribute-value pairs. It is essential to understand these metadata to effectively use the Twitter APIs. Some fields are briefly described as follows.

<table><tr><td>id:</td><td>integer representation of unique identifier for this tweet</td></tr><tr><td>text:</td><td>contains the actual UTF-8 text of the status update</td></tr><tr><td>retweet_count:</td><td>indicates the number of times this tweet has been retweeted</td></tr><tr><td>retweeted_status:</td><td>contains a representation of the original tweet</td></tr><tr><td>place:</td><td>indicates that the tweet is associated with a place</td></tr><tr><td>geo_enabled:</td><td>indicates whether geo-tagging is enabled by the user</td></tr><tr><td>lang:</td><td>indicates user's self-declared user interface language</td></tr></table>

# 3 Preliminary experiments

## 3.1 Data collection

We use an interactive Python environment for programming and development, and the Twitter search API to extract tweets. The search API (non-Streaming API) allows the developer to obtain a maximum of 1.73M tweets/day through the *Application-user authentication* (AuA) and a maximum of 4.32M tweets/day through the *Application-only authentication* (AoA).
These API constraints necessitate deployment of a large number of computer resources if the goal is to collect all 500M tweets/day produced worldwide as in Table 1. More than 115 distinctly authenticated processes in parallel would be needed to run than is theoretically required.

But, if the goal is only to collect tweet samples based on search queries with filters (for instance, language or location related filters) then the task seems to be manageable. For instance, [10] reports that during the electoral polls of 2014 in India, *election* related tweets were produced in the range of 0.54M/day and 0.82M/day from April 1 to May 12.

Even if we assume that the production of *tourism* related tweets per day in India is ten times more, they can be collected with less than 5 distinctly authenticated processes using suitable APIs. The search API returns a collection of tweets corresponding to the requested query and the specified query filters.

To begin with, we looked at some multilingual tweets obtained by submitting different queries through the Twitter web interface. We then selected the queries that yielded the most suitable results and used them in programming scripts to access the search API. As a preliminary study, in the following sub-sections we describe two experimentations on the tweet results obtained by the search query Q1.

Q1: 'lang:hi OR ("indian music" OR "indian festival" OR "indian restaurant")'

We use the search API under the AuA mode to acquire a sample of 100K tweets using Q1. The result is a set of all tweets filtered on the field 'lang:hi' (with Hindi as the user-indicated language) or tweets matching either of the 3 consecutive terms.

## 3.2 Code-mixing: a preliminary study

In the context of tweets, the text is often full of spam, incorrectly spelt words, disfluencies, slang words and other anomalies, as shown by [14]. Moreover, even though these texts are quite short, they exhibit some degree of code-mixing. So, we tried to estimate the proportion of code-mixed text in the 100K tweet sample.

The step-wise formulation of the experiment is as follows.

1. For a query Q, we obtain a set of tweet responses denoted by $T(Q) = \{R_1, R_2, R_3, ...\}$. Each $R_i$ is a response in JSON<fnref n="9" /> format representing metadata of each tweet.

2. For each $R_i$, we retrieve the values from the 'text' field containing the user's tweet message, $s_i$. Let $S(Q) = \{s_1, s_2, s_3, ...\}$ be a set of all tweet message(s) obtained from query Q.

3. For each $s_i$, we collect whitespace-separated tokens (words) in a set denoted by $W(Q) = \{w_1, w_2, w_3, ...\}$.

4. We now use regular expressions (regex) to group all the words in $W(Q)$ into the following collections.

<footnote n="9">⁹ JavaScript Object Notation</footnote>
<table><tr><td>ALL:</td><td>The set W(Q) containing all the words.</td></tr><tr><td>FILTERED:</td><td>Set of words excluding hashtags, screennames, URLs and 'RT' (retweet) words. Words that do not begin with '@' or '#' or 'http'. The word 'RT' is also excluded.</td></tr><tr><td>ALPHANUM:</td><td>Set of words that only match the alphanumeric characters, regex:'[A-Za-z0-9]+'.</td></tr><tr><td>NON ALPHANUM:</td><td>Set of words that only match the non-alphanumeric characters.</td></tr><tr><td>HASH TAGS:</td><td>Set of hashtags. Words that begin with '#'</td></tr><tr><td>SCREEN NAMES:</td><td>Set of screennames. Words that begin with '@'</td></tr><tr><td>MIXED:</td><td>Contains words having atleast one alphanumeric character and one non-alphanumeric character.</td></tr></table>

A large fraction of tweets in the 100K sample was in Devanagari script because of the 'lang:hi' attribute in the query. So, in Table 2 we calculate code-mixing as a ratio of *tokens in ALPHANUM* to the *tokens in FILTERED*. In these data, the average code-mixing percentage at the word-level over 100K tweets is 3.28%. That is certainly a very small proportion, but it nevertheless corresponds to a very large number of tweets everyday (approx. 15K tweets/day assuming a 500K tweets/day originating from India, [10]) and they may presumably contain very interesting information (produced by visitors, for example).

## 3.3 Geo-information: a preliminary study

Third-party information services built on social media platforms often provide statistics that help to perspectivise and research social media behaviour. A similar study on worldwide distribution of Twitter users by Statista<fnref n="10" /> shows an increase of Twitter users from 29% in 2012 to 35% in 2015, especially in the Asia-Pacific region. Geo-information contained within tweets is one way of determining the origins of tweets and Twitter users.

However, this information is unreliable and needs investigation as undertaken in a recent work by [12]. We are interested in acquiring multilingual tweets based on related scripts or based on originating locations. So, we do a basic check of the extent of geo-information present in the tweets using the 100K tweet sample. The location information, other than for geo-enabled tweets, are updated by users and could be unreliable. We parse the tweet JSON structure to collect non-empty values from the located related attributes in the tweet.

The numbers in Table 3 indicate the presence of location information in the tweets. From the 100K sample, we observe that a total of 48458 (48.5%) tweets are geo-enabled.

## 4 Modules and methodology

There are 6 main modules in our proposed pipeline.

1. Twitter data: Preprocessing, Extraction, Analysis and Storage (PEAS)

<footnote n="10">¹⁰ http://www.statista.com/statistics/303684/regional-twitter-user-distribution/</footnote>
<table><caption>Table 2: (a) Proportion of word-level code-mixing over 100K tweets. (b) Rate limited API allows a maximum of 18000 tweets per request</caption><thead><tr><th rowspan="2">#tweets</th><th colspan="6">Collection Types</th></tr><tr><th>ALL</th><th>FILTERED</th><th>ALPHA NUM</th><th>HASH TAGS SCREEN NAMES</th><th>MIXED</th><th>Code mixing %</th></tr></thead><tbody><tr><td>16000</td><td>309384</td><td>262594</td><td>8471</td><td>7376</td><td>19411</td><td>6775</td><td><strong>3.23</strong></td></tr><tr><td>16000</td><td>312700</td><td>267851</td><td>6888</td><td>5688</td><td>19646</td><td>5790</td><td><strong>2.57</strong></td></tr><tr><td>16000</td><td>317013</td><td>272523</td><td>7024</td><td>4967</td><td>19355</td><td>5908</td><td><strong>2.58</strong></td></tr><tr><td>16000</td><td>310550</td><td>267945</td><td>10761</td><td>4754</td><td>18910</td><td>6643</td><td><strong>4.02</strong></td></tr><tr><td>16000</td><td>304331</td><td>259485</td><td>8264</td><td>4862</td><td>19867</td><td>6141</td><td><strong>3.18</strong></td></tr><tr><td>16000</td><td>293409</td><td>250810</td><td>8270</td><td>4735</td><td>19274</td><td>5735</td><td><strong>3.3</strong></td></tr><tr><td>4000</td><td>72974</td><td>62280</td><td>2528</td><td>1457</td><td>4313</td><td>1573</td><td><strong>4.06</strong></td></tr></tbody></table>