<table><caption>Table 3: Distribution of non-empty geo-related fields.</caption><thead><tr><td>#tweets</td><td>place</td><td>coordinates</td><td>location</td><td>time_zone</td><td>geo_enabled</td></tr></thead><tbody><tr><td>16000</td><td>172</td><td>19</td><td>10175</td><td>4491</td><td>7444</td></tr><tr><td>16000</td><td>244</td><td>64</td><td>10845</td><td>5546</td><td>8028</td></tr><tr><td>16000</td><td>204</td><td>74</td><td>10795</td><td>5957</td><td>8234</td></tr><tr><td>16000</td><td>223</td><td>66</td><td>10564</td><td>6359</td><td>7900</td></tr><tr><td>16000</td><td>311</td><td>51</td><td>10636</td><td>6637</td><td>7898</td></tr><tr><td>16000</td><td>256</td><td>54</td><td>10664</td><td>7013</td><td>7481</td></tr><tr><td>4000</td><td>42</td><td>7</td><td>2212</td><td>1549</td><td>1473</td></tr></tbody></table>

2. Morphology-based Processes and Presyntactic Analysis (MPPA)

3. Named Entities identification (NEi)

4. Interlingual Lexical Disambiguation (WSD)

5. Content Extraction (CE)

6. Polarity Determination (PD)

## 4.1 Twitter data: Preprocessing, Extraction, Analysis and Storage (PEAS)

Our preliminary observations on code-mixing and plans in doing a detailed study of the same are supported by the following published work. [5] discuss code-mixing rules and constraints in English-Hindi mixed language data, while contrasting code-mixing in tweets as opposed to free-form text. [17] shows increasing trends of code-mixing in the past decades.

As seen in the preliminary observations, an average of more than 3% of the Hindi tweets are code-mixed that can possibly be significant for further analysis. An example of code-mixed tweet in Marathi (another Indian language) sharing the same script as Hindi can be seen in Fig. 2.

As further necessary steps in processing code-mixed data, we plan to:

1. build or assemble tools for collecting the largest possible set of tweets in the concerned languages,

2. perform multiple and elaborate analysis of their languages and scripts,

3. normalize them to a standard encoding (UTF-8) and standard scripts.

The preprocessed data will then be stored in a multilingual corpus management system such as SECTra<fnref n="11" />.

## 4.2 Morphology-based Processes and Presyntactic Analysis (MPPA)

The goal is to build compatible morphological analysers (producing the same kind of output) for all languages, and to use the results to identify named entities (names of places, events,

<footnote n="11">¹¹ See [9], [1], [15].</footnote>
<figure><img src="image_74.png" /><figcaption>Fig. 2: Example of a code-mixed tweet</figcaption></figure>

(A) tweet 1: with typos and contracted terms in English (Roman script)

(B) tweet 2: in English (Roman script) and Marathi (Devanagari script)

(C) Marathi transliteration: « gOrava maHArAXTacyA aMtima soHaLA »
« pride of_Maharashtra last event »

people, etc.) and finally to annotate the simple or compound lexemes found by their possible
word senses, expressed as UNL interlingual lexemes (UWs). An interesting and quite new
perspective is the possibility to build a unique morphological analyzer for the set of languages
(rather, sublanguages) and for the various writing conventions at hand. For this, we plan to
use a new version of ATEF, a tool built on an extended NDFST model by [4]<fnref n="12" />.

4.3 Named Entities identification (NEi)

Identification of named entities shall follow a simple syntactic analysis based on "local grammars" or "local automata", where the goal is only to find small linguistic fragments, or "chunks", such as elementary noun phrases (that are often named entities or terminological units), and then structure them in small multilevel structures or small UNL graphs [19].

A favorable circumstance in the chosen domain (tourism) is that quite a large number
of named entities are available and updated in external free resources (persons, places and
above all, various types of events).

## 4.4 Interlingual Lexical Disambiguation (WSD)

These processes usually involve numerical or statistical computation, sometimes even a degree
of Machine Learning. They concern lexical disambiguation (WSD, leading to score assignment

<footnote n="12">¹² See also [8].</footnote>
to possible UWs), semantic disambiguation (at the level of small groups/phrases), and
typically use conceptual vectors, ant algorithms, and similarity or semi-distance computations
[18].

## 4.5 Content Extraction (CE) and Polarity Determination (PD)

[16] conclude that building SRecom(s) using ontologies and knowledge bases (KBs) reduce
several specific problems related to:

1. inter-operability of resources and homogeneity of representation,

2. dynamic contexualization of user preferences,

3. semantic extension of descriptions using other contextual factors.

Following them, and building on previous work in the OMNIA project [7], we will use
Semantic Web technology for defining the "knowledge", that is, we will use one or more
small KBs or small domain specific ontologies. For the proposed m_SRecom, the content
extraction and polarity determination operation has to be done relative to each knowledge
base (e.g., a relational database, or an ontology) in which one wants to index each text
(tweet in this case). It will work in a language-independent way, using the disambiguated
word senses (UWs) and their scores, and relating to each KB by using a UW-KB alignment.

## Acknowledgments

We would like to thank UJF (Université Joseph Fourier), IRD (Institut de Recherche pour le Développement) and IITB, (Indian Institute of Technology, Mumbai) for supporting this research.
Bibliography

[1] Christian Boitet, Mathieu Mangeot, and Gilles Sérasset. The PAPILLON project: Cooperatively building a multilingual lexical data-base to derive open source dictionaries and lexicons. In "Proceedings of the 2nd Workshop on NLP and XML at COLING'02", pages 93-96, Taipei, 2002.

[2] Svetlin Bostandjiev, John O Donovan, and Tobias Höllerer. TasteWeights: A Visual Interactive Hybrid Recommender System. In "Proceedings of the 6th ACM conference on Recommender systems - RecSys '12", pages 35-42, 2012.

[3] Robin Burke. Hybrid web recommender systems. *The Adaptive Web*, pages 377-408, 2007.

[4] Jacques Chauché. The ATEF and CETA systems. In David Hays, editor, *American Journal of Computational Linguistics (2, microfiche 17)*, chapter 1, pages 21-40. Association for Computational Linguistics, 1975.

[5] Anik Dey and Pascale Fung. A Hindi-English Code-Switching Corpus Code-Switching in Indian Culture. In "Proceedings of the 9th Language Resources and Evaluation Conference", pages 2410-2413, 2014.

[6] Michael D. Ekstrand, John T. Riedl, and Joseph A. Konstan. Collaborative Filtering Recommender Systems. *Foundations and Trends in Human-Computer Interaction*, 4(2):81-173, 2010.

[7] Achille Falaise, David Rouquet, Didier Schwab, Hervé Blanchon, and Christian Boitet. Ontology driven content extraction using interlingual annotation of texts in the OMNIA project. In "Proceedings of the 4th International Workshop on Cross Lingual Information Access at COLING'10", pages 52-60, Beijing, August 2010.

[8] Jean-Philippe Guilbaud, Christian Boitet, and Vincent Berment. Un analyseur morphologique étendu de l'allemand traitant les formes verbales à particule séparée. In "Proceedings of TALN-RÉCITAL", volume 9, pages 755-763, Les Sables d'Olonne, June 2013.

[9] Cong-Phap Huynh, Christian Boitet, and Hervé Blanchon. SECTra_w.1: an Online Collaborative System for Evaluating, Post-editing and Presenting MT Translation Corpora. In "Proceedings of the Language Resources and Evaluation Conference", volume 8, pages 27-31, 2008.

[10] Raheel Khursheed. India's 2014 #TwitterElection. http://blog.twitter.com/2014/indias-2014-twitterelection, November 2008.

[11] John Bruntse Larsen. *Content-based Recommender Systems*. PhD thesis, Technical University of Denmark, 2013.

[12] Jalal Mahmud, Jeffrey Nichols, and Clemens Drews. Home Location Identification of Twitter Users. "ACM Transactions on Intelligent Systems and Technology", 5(3):47-69, 2014.

[13] Prem Melville, Raymond J Mooney, and Ramadass Nagarajan. Content-boosted collaborative filtering for improved recommendations. In "Proceedings of the 18th National Conference on Artificial Intelligence (AAAI)", pages 187-192, Edmonton, July 2002.
[14] Subhabrata Mukherjee, Akshat Malu, A R Balamurali, and Pushpak Bhattacharyya. TwiSent: A Multistage System for Analyzing Sentiment. In "Proceedings of the Conference on Information and Knowledge Management (CIKM)", pages 2531–2534, 2012.

[15] Hong-Thai Nguyen, Christian Boitet, and Gilles Sérasset. PIVAX, an online contributive lexical database for heterogeneous MT systems using a lexical pivot. In "Proceedings of the International Symposium on Natural Language Processing", Bangkok, 2007.

[16] Eduardo Peis, JM Morales del Castillo, and JA Delgado-López. Semantic recommender systems. An analysis of the state of affairs. Hipertext.net, Spain, 6:1–5, 2008.

[17] A. Si. A diachronic investigation of Hindi-English code-switching, using Bollywood film scripts. "International Journal of Bilingualism", 15(4):388–407, 2011.

[18] Andon Tchechmedjiev, Jérôme Goulian, Didier Schwab, and Gilles Sérasset. Parameter estimation under uncertainty with Simulated Annealing applied to an ant colony based probabilistic WSD algorithm. In "Proceedings of the First International Workshop on Optimization Techniques for Human Language Technology", pages 109–124, 2012.

[19] Bernard Vauquois and Christian Boitet. Automated translation at Grenoble University. "Computational Linguistics", 11(1):28–36, 1985.

[20] Xiwang Yang, Yang Guo, Yong Liu, and Harald Steck. A survey of collaborative filtering based social recommender systems. Computer Communications, 41:1–10, 2014.
# Colors of the World Cup: visualizations of images shared on Twitter during the 2014 World Cup

Fábio Goveia, Lia Carreira, Lucas Cypriano, Tasso Gasparini, Johanna Honorato, Veronica Haacke, and Willian Lopes

Department of Communications

University of Espírito Santo

Espírito Santo, Brazil

fabiogv@gmail.com, liacarreira@gmail.com

**Abstract.** The 2014 World Cup, held in Brazil from June 12 to July 13, had great impact on social networking sites. Seeking to better understand content sharing flow and to explore methodological possibilities for studies with large volumes of images data, the Image and Cyberculture Studies Lab (Labic), of the Federal University of Espírito Santo, created the project Colors of the World Cup - a project that presents interactive image visualizations of contents shared on Twitter during the football tournament. Throughout this paper, we will present the project's methodological process and propose a brief analysis of these images as a way of documenting the social and cultural movements and events that appeared on Twitter during the World Cup.

**Keywords:** Image, Visualization, Twitter, Color

## 1 Introduction

The large amount of information produced by each individual through mobile devices can be considered one of the most significant marks of contemporary society. On the one hand we are producing these data, on the other hand we are also sharing, circulating and consuming constantly these information. Digital social networks are among the most commonly used ways to consume and circulate personal and public news. These platforms have become privileged interaction zones, with more and more exchanges of information and experiences.

This new universe opens up numerous opportunities for knowledge and relationships - that once seemed nearly impossible to happen – and also makes it possible to develop research analyzing these interactions and weaving more precise understandings of the social behavior of individuals.

In this scenario new experiences among users emerge and important tools of activism and change get created. In addition to the significant role of social networks in the political field, it should be also be highlighted the significant amount of information produced in relation to other large social events (sports, cultural, artistic). Data from these social networks related to these events can contribute significantly to different studies, such as consumer behavior.
From this overview, the analysis of the ways in which such content spread through the networks is necessary for a better understanding of how these social movements develop and behave, in and out of these digital environments. Furthermore this analysis can help to identify potential patterns or future occurrences. Thus the big data studies appear as a field of study that has been highlighted in recent years, especially due to the expansion of this large amount of information circulating in the environment of digital social networking, and that has been used for several studies, as the one presented here.

Seeking investigate new methodological frameworks, the Laboratory of Research on Image and Cyberculture (Labic), started the World Cup Colors project, which sought to analyze the production of images that occur in social digital networks during the 2014 World Cup held in Brazil. The event took place between June 12 and July 13, 2014, with a great impact on social networks. On this occasion, we have seen emerging a large number of images that were critical or made fun of the event, giving way later to a more favorable outlook. This change was attributed to the network called "Boleira" that is, the network of those fan profiles that accompany the real-time games, the backstage and the memes about the World Cup. They significantly contributed to the expansion of the online debate to a more positive and less critical bias.

Before the Cup a strong tension was expressed by various popular protests in several Brazilian cities. The first analysis of the images on social networks revealed that this tension ultimately was displaced by the accounts of the football fans which dominated the sharing of images on Twitter. No other perspective occupied the media spaces network in the same way as the football fans profiles. At one point, even some activists that were complaining about the FIFA impositions to the country, and corruptions with the event, started communicating about the games and became part of this football narrative.

In the following, we present the methods that guided the development of this research, as well as web application development, launched on October 6, 2014 in order to provide viewing modes for large sets of images collected during the World Cup.

## 2 Methodology

Manovich and Hochman (2013); Manovich et. al. (2011); Crandall et. al. (2009 e 2015) are some of the authors that work in this area, from social networks data mining to development of big data image visualizations. The challenges proposed by them were also part of this research. In this research were collected images shared on Twitter. In this case, data was collected through World Cup<fnref n="1" /> related terms such as specific keywords, hashflags<fnref n="2" /> of each country, terms of each match (for example, #BRAXGER used for the game between Brazil and Germany), and the names of the major soccer players, such as "Neymar," "Messi" and

<footnote n="1">¹ Terms were used that made reference to the main event, as "world cup", "copa2014", "brasil2014", "worldcup"; the side events directly related to WordCup as "Funfest", "wordcup opening"; and the organizations responsible for the event, such as "fifa" and "cbf".</footnote>

<footnote n="2">² Hashflags were a special kind of hashtag used on Twitter during the World Cup. Were hashtags related to the countries participating in the World Cup.</footnote>
"Podolski". Here, the aim was to collect, from June 12 to July 13 of 2014, the largest number of tweets related to the event, using precise terms and related keywords.

The tweets published during this period were collected from a script called Marcus, developed in Python by Labic in partnership with the computer scientist André Panisson. This tool searches the tweets containing text terms previously selected and stored in a remote server database (MongoDB<fnref n="3" />). After this first mining, every 15 minutes of collect by Marcus script another script called Crawler<fnref n="4" />, also developed by Labia, accessed the database, captured the tweets of that period containing links (possible images), eliminates those images links resulting from external sites, considering in this way just the images that was posted directly in the tweet. After this the Marcus script selected the 100⁵ most shared images during the selected period - this mean, the images links most repeated during the selected period, in this case each 15 minutes. Thus the script accessed each link collected, saved the image attached and generated a .csv table listing the link of the image and the saved file for further analysis.

With the image dataset ready in hand, it was then possible to proceed with the data processing and image analysis, done in two simple steps: first the extraction of chromatic data and, second, image comparison in order to eliminate repeated ones. Therefore, at the end of each day's data mining process, a third script also developed by the lab called AISI (Automatic Identifier of Similar Images) swept the dataset, extracted and compared the image's colors metadata (such as hue, brightness and saturation, as well as each image's histogram), and identified those that were similar to each other, avoiding repetition. When similarities were identified, these similar images are considered as single ones, adding their frequencies (i.e., the amount of time they were posted) as to give them their correct weight in the dataset. Without this procedure, each one of these similar images, published in different tweets, wouldn't have the impact they actually have on these networks through online sharing - a very important characteristic of social media content.

With this extraction method, we obtained a total of 30 million tweets, of which 2 million contained links with images, and a total of 42,000 images posted directly on Twitter. Among these, there were 17,000 unique images that were then used to create the visualizations. With these data, it was possible to launch a web application called Colors Of the World Cup that facilitates reading this diversity of content by organizing it according to previously collected data, such as their chromatic characteristics and frequency of sharing.

The online interface of the application was developed using the JavaScript programming language, and the D3⁶ library. From the .csv files generated by the Crawler and AISI, the

<footnote n="3">³ Open source database, developed in C ++ programming language. The system generates an information store in JSON documents, a data file type that works independent of any specific programming languages. More information available at: http://www.mongodb.org/</footnote>

<footnote n="4">⁴ Script developed in Java, that accesses the selected links and downloads of these the images found. Available code: https://github.com/ufeslabic/crawler.</footnote>

<footnote n="5">⁵ For to do the extraction of all images published would require a long time, it would preclude the 15-minute time window.</footnote>

<footnote n="6">⁶ D3 (Data Driven Documents) is a JavaScript library designed to handle large data files for online visualizations. More information available at: http://d3js.org/.</footnote>
engine retrieves information to generate the views and display them in the web application. With this data four different visualizations are generated: chromatic calendar<sup>7</sup>, chromatic timeline<sup>8</sup>, chromatic mosaic<sup>9</sup> and images mosaic<sup>10</sup>.

The chromatic calendar (Figure 1) brings daily charts, containing circles as representations of each image from its predominant color. In this view they are counted only the shares obtained by the image in each day. The circle (that represent a unique image) position on the X axis is defined by the predominant hue of the image that its represents, while the position in the Y axis is defined according to the number of shares that the image obtained during that period; the radius of each circle brings the total number of shares that the image accumulated over time; and the color of the circle is defined by the predominant hue of the image. Hovering the mouse over each circle shows the image, and click in the view starts an animation that shows the behavior (sharing number) of images throughout the day in 15-minute intervals.

The chromatic timeline (Figure 2) is similar to chromatic calendar, but now shows on the same graph the total volume of the images during all days of the extraction period, and with this allows to be observed the behavior of each image along all the days. Same as the chromatic calendar the X axis position and the circle color is defined by the predominant hue of the image that its represents. But now the Y axis position is defined according to the number of shares that the image obtained during all one day, and the radius of each circle brings the total number of shares that the image accumulated over the days. Clicking the graph shows an animation with the behavior of images in each day. You can also pause or fast forward the animation to view the images more closely every day, and skip to specific days.

The chromatic mosaic (Figure 3) is a more straightforward view that brings the images represented by smaller circles with their respective predominant color and organized by its color hue. And hovering the mouse over each circle shows the original image.

Finally, the image mosaic (Figure 4) is the last visualization, in this is displayed the own images organized by their predominant hue color value, with an option of zoom in and out to have a more overview of all images or a more close visualization for a specific image. It is important to note that this is the only visualization of the application that uses the stored images<sup>11</sup> from the dataset, while the others visualizations access the original link of the image to show those in real time. This means that images in these others views will gradually fail to appear, since their source links may in time cease to exist. Thus, these views reflect the vital movements in networks.

Likewise, downloading and storing the images is also a way to keep these vivid images as memories of a movement or major event. Working with these large datasets is to work with

<footnote n="7"><sup>7</sup> Available at: http://labic.net/coresdacopa/calendariocromatico/</footnote>

<footnote n="8"><sup>8</sup> Available at: http://labic.net/coresdacopa/timelinecromatica/</footnote>

<footnote n="9"><sup>9</sup> Available at: http://labic.net/coresdacopa/mosaicocromatico/</footnote>

<footnote n="10"><sup>10</sup> Available at: http://labic.net/coresdacopa/mosaicoimagens/</footnote>

<footnote n="11"><sup>11</sup> Because of the large size of the image and to possibility zoom the view, the ZoomIt tool, developed by Microsoft for viewing very large size images on the Internet has been used. More information available at: http://zoom.it/.</footnote>
their transitions and movements over a period of time, and create views is one way to realize
these processes and make them visible.

3 Analysis

The Colors of the World Cup visualizations aims to highlight certain features of the shared
content on the network during the 2014 World Cup. Through them, it is possible to highlight,
from a wide range of content, key aspects about the way of images sharing in social network
during the collection period. By analyzing these views, an intense chromatic variation of the
images can be observed. A large volume of green tone images is noticeable, partly because
the green color of the grass (since most of the photos with this predominant color tone
are scenes that happened on the field) and partly because the presence of some Brazilians
flags. There is also a considerable number of images in blue shades composed of uniform
images such as the Argentina selection uniform with predominant blue colors, and a large
number flags and advertising materials. In contrast, the more yellowish region, there is a
large volume of images of materials related to the Brazilian national team, notably the yellow
shirt traditionally used in games.

In the area with more red tones, we see mostly pictures of people, due to skin tones
approaching beige and red. There is still a small part with pink predominant tone images,
composed largely of images published with filters (like the ones used in the Instagram
application), which pull the image tone to pink.

Beyond this evident chromatic variation, shared oscillations by day can be observed
during the World Cup. This shows that the impact of the event on the networks did not
occur in a homogeneous way. On the contrary, in a single day there was a very large amount
of images with radically different levels in popularity on the network. The high frequency
of an image in a day did not imply a similar level of popularity the next day, as happened
with the eagle image (Figure 5) with the US flag on 22 June12, being the third most shared
image of the entire Cup, obtaining total 22,098 posts just on the first day of sharing. This
eagle image represented the discussions taking place on Twitter during the US game with
Portugal, highlighting the broad participation of the North American and other supporters
throughout this day. This demonstrates, therefore, that the image sharing modes during the
World Cup were related to the specificity of the events of each day.

The most shared image throughout the period appeared on the network a few days before
the end of the World Cup, on 5 July. It is an image of Neymar (famous soccer player) (Figure
6) kissing the ball, with a yellow mount in the background. The image was shared too by
player David Luiz after Neymar suffered a spinal injury and cumulated a total number of
38,490 shares. This represents the support of fans and players in relation to their concerns for
Neymar and was a very present image from the days of the event, showing the sentimental
side of the players. Similar images in which players appear crying or celebrating a victory or
defeat reinforce the emotional tone that the shared images had during the World Cup.

Thus, these images underline the existence of anemotional outlook: images shared by
the network reflect the feelings of social networking users in relation to the championship
and the participating teams. Images with scenes of the competition, as photo of players
being emotional in the field, were used to express the feelings of users. This is most clear in the images most shared between the 8th and 9th of July, which brought scenes of sadness resulting from the defeat of Brazil to Germany by 7-1, and in the second shared image (Figure 7) between the 12th and 13th June.

The wordcup shared images also have strong memetic content, ie, were created to become memes (images with the potential to multiply and propagate quickly, and can be given new meanings in the sharing process). These memes dominated the images on the networks that have formed around topics related to the World Cup, especially during the event first week, and remained largely shared together with pictures of other events that drew public attention (such as the elimination of teams considered "strong" or the Brazilian defeat to Germany, for example).

Thus, these visualizations made possible a rich analysis with the identification among other characteristics, of the existence of a chromatic rhythm. The images pulses and dances, even if at times, out of step and rarely able to maintain a constant frequency. This rhythm is symptomatic of the incessant and extremely fast content sharing process, characteristic of our time, that makes images suddenly appear, rise and quickly fall, preventing them to remain on the spotlight for too long. Thus, we can say that these social network images compete with each other for a spot to be seen, to be liked and to be shared - before they lose their sparks. And thus we can conclude, by watching these images through the visualizations created for this projects, that the images on Twitter posted during the World Cup were, above all, short-lived devices fighting for visibility.

# 4 Final Considerations

Our studies with large quantities of images still have a long way to go. With the current research, Labic has tried to bring question as well as contribute with new and already existing image visualizations methods. In a time where these data are easily produced and shared on every moment, initiatives that try to record these MOVEMENTS are even more needed, so narrative paths and rhythms can be discussed, despite the quick and uncertain life of the artifacts under consideration. Through Colors of the World Cup, we can observe the traces of images published in a network and try to determine its behavior, and by way of its vestiges comprehend its movements, apparitions and reapparations, and longevity after all.

However, there are still obstacles that tend to discourage this kind of research. The processing power needed for the conceptualization and execution of projects of this magnitude is very large: many files are used, and the large volume of images can lead machines to have a very low performance. Astable and high speed internet connection is also an important issue to insure that the image crawling can proceed without interruption.

Another constant problem faced in this kind of study is "bots", robot-profiles that automatically share content and almost always in a massive way. This kind of automatic process can make specific network's threads or subjects seem to have a high relevance, figuring between the most shared ones, though really their organic circulation is low: they are not shared by the network's "common" users. For this reason it is crucial to have a watchful eye for bots, so they don't taint the analysis.
<figure><img src="image_75.png" /><figcaption>Fig. 1: Chromatic calendar: days 12-20 of June. Best viewed through the site.</figcaption></figure>

<figure><img src="image_76.png" /><figcaption>Fig. 2: Chromatic timeline. Best viewed through the site.</figcaption></figure>

Colors of the World Cup provides an experimental method for data extraction in real time and introducing new image visualizations possibilities, offering a contribution to the cyberculture and image study fields and presenting novel ways to analyze image data.
<figure><img src="image_77.png" /><figcaption>Fig. 3: Chromatic Mosaic. Best viewed through the site.</figcaption></figure>
<figure><img src="image_78.png" /><figcaption>Fig. 4: Images Mosaic. Best viewed through the site.</figcaption></figure>
<figure><img src="image_79.png" /><figcaption>Fig. 5: Most shared image of June 22. Author (s) unknown (s).</figcaption></figure>

<figure><img src="image_80.png" /><figcaption>Fig. 6: Image composition with Neymar player, was the most shared in the period of the World Cup.
Author (s) unknown (s).</figcaption></figure>
<figure><img src="image_81.png" /><figcaption>Fig. 7: David Luiz (player) photo being consoled was the second most shared image in the day after the brazilian defeat to Germany. Author (s) unknown (s).</figcaption></figure>
Bibliography

[1] Crandall, D. J., Backstrom, L., Huttenlocher, D., & Kleinberg, J. (2009, April). Mapping the world's photos. In Proceedings of the 18th international conference on World wide web (pp. 761-770). ACM. Lee, S., Maisonneuve, N., Crandall, D., Efros, A. A., & Sivic, J. Linking Past to Present: Discovering Style in Two Centuries of Architecture. In *IEEE International Conference on Computational Photography*.

[2] Haacke, V., Ribeiro, A. et. al. Do “Não Vai Ter Copa” para a “Copa dos Memes”: Uma análise das imagens memes mais compartilhadas durante a Copa do Mundo FIFA 2014. Brazil, *Foz do Iguaçu: Intercom*, 2014.

[3] Hochman, N., & Manovich, L. (2013). Zooming into an Instagram City: Reading the local through social media. *First Monday*, 18(7).

[4] Manovich, L. (2012). How to compare one million images. *Understanding Digital Humanities*, 249-278.
# Why Larry is different than Narry: A linguistic study of shipping communities in the One Direction fandom

Grace A. Ruiter

Calvin College

Grand Rapids, Michigan

graceruiter@gmail.com

**Abstract.** This study uses corpus linguistics, sentiment analysis, and discourse analysis to test the hypothesis that the Larry shippers—fans of the relationship between Louis and Harry from One Direction—behave differently than other shipping communities within the One Direction fandom. The study not only finds support for its hypothesis, but also reveals that the Larry shippers, who are suppressed for their support of a homosexual relationship, have themselves become oppressors through their treatment of another shipping community: the Elounor shippers. The Larry shippers dominate Twitter discussions about Louis Tomlinson and Eleanor Calder's relationship with attacks on the ship's validity. In their domination, the Larry shippers arguably disempower the official One Direction narrative about Elounor and replace it with their own interpretation of the relationship, an incredible feat for a fan community—one that would not be possible without social media avenues like Twitter

**Keywords:** fandom culture, corpus linguistics, sentiment analysis, discourse analysis

## 1 Introduction

Social media has given the general public an unprecedented ability to respond to important events in politics, entertainment, and business. With this ability comes the potential for groups to develop and perpetuate their own counter-narratives that reject mainstream ideas (Dahlberg, 2007). This democratization of the public sphere proves particularly significant for fandom communities. In fact, by enabling fans to respond to, and even reject “official” narratives, social media has fundamentally changed the relationship between cultural texts and their fans (Galuszka, 2014). Now more than ever, fans are not only consumers, but also creators, promoters, and critics in their own right.

One of the most popular ways fans interact with texts is through the practice of shipping, supporting a hypothetical or real romantic relationship between two characters or people. From Drarry (Draco Malfoy and Harry Potter), to JohnLock (Sherlock Holmes and John Watson), the social web plays host to a plethora of ships—many of which represent counter-discourses to the cultural texts that inspired them. However, because social media's emergence is recent, no previous linguistic research on fandom shipping communities and social media exists. Using the One Direction fandom as a case study, this study aims to set the groundwork for future scholarly work on the subject.
Few fandoms boast shipping communities as intense as the One Direction fandom's. And few fandoms can compare to the One Direction fandom in size and online activity. The most popular ship in that fandom, and the second most popular pairing for stories on the fan fiction site Ao3: Larry Stylinson (Harry Styles and Louis Tomlinson). Larry Stylinson is an unusual ship in that, officially, both Harry and Louis are heterosexual, yet almost everyone who ships Larry believes Harry and Louis are really in a secret relationship. The relationship is not public, shippers assert, because One Direction's management is forcing Harry and Louis to stay in the closet.

Larry shippers want their ship to be recognized as more than a fantasy. This study hypothesizes that, because of this desire, discussions about Larry follow different linguistic patterns than other discussions about ships within the One Direction fandom. In particular, the study anticipates that Larry shippers will employ more intentional rhetoric aimed at proving their ship is real than other shippers. The study tests this hypothesis by using corpus linguistics and discourse analysis tools to compare the content of Twitter discussions about Larry with discussions about five other ships within the One Direction fandom: Elounor (Louis Tomlinson and Eleanor Calder), Sophiam (Liam Payne and Sophia Smith), Zerrie (Zayn Malik and Perrie Edwards), Ziam (Zayn Malik and Liam Payne), and Narry (Harry Styles and Niall Horan).

# 2 Methodology

## 2.1 Data collection procedures

**Materials.** At the time of the study, three One Direction members were in public relationships with women: Zayn Malik, Liam Payne, and Louis Tomlinson. I included the ships centered on these relationships—Zerrie, Sophiam, and Elounor—in the study to represent the One Direction fandom's treatment of band members' publicly romantic relationships. The study compares these official heterosexual pairings with three ships centered on the publicly platonic relationships between band members. I selected Larry, Ziam, and Narry as the “bromance” ships for the study because they were the three most frequently discussed ships between One Direction band members on Twitter during December 2014.

**Data collection procedure.** To build corpora for the six ships in the study, I collected a random sample of 100 tweets—one tweet from each day between October 1, 2014 and January 8, 2015—for each of the ships included in the experiment. I used Topsy, a Twitter analytics tool, to search for the tweets that contained the names of the bromances and romances being studied. Topsy was set to display search results beginning with the tweets posted closest to the time of day at which I collected data. Collection took place between 12 and 9 p.m. Eastern Standard Time. I added the tweet at the top of the results list for each day to the corpus. I did not exclude or treat retweets differently in this process.
## 2.2 Data analysis procedures

**Preparation for analysis.** To build corpora for the six ships in the study, I collected a random sample of 100 tweets—one tweet from each day between October 1, 2014 and January 8, 2015—for each of the ships included in the experiment. I used Topsy, a Twitter analytics tool, to search for the tweets that contained the names of the bromances and romances being studied. Topsy was set to display search results beginning with the tweets posted closest to the time of day at which I collected data. Collection took place between 12 and 9 p.m. Eastern Standard Time. I added the tweet at the top of the results list for each day to the corpus. I did not exclude or treat retweets differently in this process.

**Sentiment analysis.** I classified each tweet in the six corpora as positive, neutral, or negative in its sentiments toward the ship or ships it discussed. I realize no language is truly neutral. However, I included a neutral category in my classification system because people in a fandom often have generally positive feelings toward ships within that fandom without actively shipping them. To get an accurate picture of attitudes toward each individual ship, I needed to divide those mildly positive sentiments from clearly positive attitudes. And in order to account for context, fandom-specific language, pictures, videos, GIFs, and emojis, I did all of the sentiment analysis without the use of computational systems. I made my classifications based on the definitions of positive, neutral, and negative provided below.

- **Positive sentiment:** Tweets that use clearly positive language, emoticons, or images to characterize the ship being discussed or those that count themselves fans of that ship.

- **Neutral sentiment:** Tweets that do not explicitly express positive or negative feelings about the discussed ship or those that ship it.

- **Negative sentiment:** Tweets that use negative language, images or rhetoric to describe a ship or those that ship that particular ship.

### Tweet categories by sample

#### Organizational notes

The Elounor and Zerrie samples had a significant number of negative tweets. I broke the negative tweets for both of those samples down into individual types of criticism. However, none of the other four samples had more than 10 negative tweets. Because these samples had so few tweets with negative content, categorizing the tweets reveals little. Thus, although a study with a larger sample might be able to draw more conclusions about the rhetoric employed in negative tweets about those ships, for the purposes of this study, I chose to display the negative tweets in these samples as a single category.

The Elounor sample's general tweet data is also organized differently than the other samples. Because Larry shippers represented such a significant portion of that sample, the large volume of Larry shipper tweets skewed the data from other categories within the sample. To get an accurate representation of how each type of tweeter in the sample discussed Elounor, I broke down the Pro-Larry, Pro-Elounor, and Neutral tweet categories individually, in addition to the "By Category" results for the entire sample.
## General tweet type categories:

- **Coming Out Discussion (Larry only)**: Discusses a hypothetical coming out for a shipped pair, implying belief in the ship's reality.

- **Creative Activity**: Displays creative interaction with a ship, but does not construct a fantasy based on the ship.

- **Defense of Ship**: Defends a ship and/or the act of shipping it.

- **Fangirl Behavior**: Uses hyperbolic, emotional language or images to discuss a ship.

- **Friendship (Narry only)**: Discusses a ship as a friendship.

- **General Fandom**: Mentions ship in the context of a whole fandom discussion.

- **Misc**: Does not fit in any established tweet categories.

- **Negative**: See “Negative Sentiment” definition in section 2.2b.

- **Obs/Moments (Evidence)**: Focuses on interactions between a pair being shipped.

- **Pro-Larry Elounor tweets (Elounor only)**: Discusses Elounor but is composed by a Larry shipper or Elounor skeptic.

- **“Relationship goals” tweets (Sophiam only)**: Describes a pairing as “relationship goals” for the tweeter.

- **Retweet/Fav if You Ship**: Asks fans to favorite or retweet as an expression of support for a pairing.

- **Sexual Content (Narry and Ziam only)**: Discusses ship in explicitly sexual way.

- **Ship as Fantasy**: Constructs a fantasy based on the ship.

- **Ship as Reality**: Explicitly discusses a ship as being “real,” implying belief that the ship represents a real-life romance.

- **Spam**: Photos and GIFs of a person, a ship, or the whole band tweeted in rapid succession.

- **Tags**: Responds to questions posed in a tag that fandom members nominate each other to answer.

## Negative tweet type categories:

- **Ad Hominem**: Attacks/insults those who ship a pairing.

- **Coming Out Discussion (Zerrie only)**: Discusses a hypothetical coming out for a shipped pair, implying belief in the ship's reality.

- **Comparisons (Elounor only)**: Makes unflattering comparisons between a pairing and other romantic relationships.

- **Disgust**: Expresses general distaste for a ship.

- **Dying Ship Narrative (Elounor only)**: Perpetuates narrative that Elounor is a dying ship, while the Larry fandom is growing.

- **Negative Fangirl Behavior (Zerrie only)**: Expresses negative sentiments toward a ship, but also displays fangirl behavior.

- **Predictions (Elounor only)**: Predicts future actions based on past events.

- **Proofs**: Subset of the Obs/Moments category in which a tweet uses events/interactions between a pairing as evidence for or against a ship's reality.

- **Skepticism (Zerrie only)**: Conveys doubts or suspicions about a ship.

- **Straw man**: Presents easily dismantled argument for Elounor, and uses images, GIFs, or text to dismiss the argument.
# 3 Results

## 3.1 Elounor Sample

### Sentiment Analysis:

<figure><img src="image_82.png" /><figcaption>Fig. 1: Elounor Sentiment Analysis Results</figcaption></figure>

### Types of Fans Behind Tweets:

- Larry Shippers: 60%

- Neutral One Direction Fans: 26%

- Elounor Shippers: 14%

### By Category:

- Pro-Larry Tweets: 62%

- Retweet/Fav If You Ship: 15%

- Fangirl Behavior: 9%

- Misc: 5%

- Obs/Moments: 4%

- Defense of Ship: 2%

- Creative Activity: 2%

- General Fandom: 1%

### Pro-Larry Tweets (62% of sample):

- Neutral Sentiments About Elounor: 11%

- Negative Sentiments About Elounor: 89%
## Negative (toward Elounor) Pro-Larry Tweets By Category:

- Straw Man: 24.1%

- Comparisons: 20.3%

- Dying Ship Narrative: 16.7%

- Proofs: 11.1%

- Ad Hominem: 11.1%

- Disgust: 9.3%

- Predictions: 7.4%

## Elounor Shipper Tweets by Category (14% of sample):

- Fangirl Behavior: 69%

- Obs/Moments: 31%

- Creative Activity: 15%

- Defense of ship: 8%

## Neutral Tweets by Category (24% of sample):

- Retweet/Fav If You Ship: 64%

- Misc: 20%

- Spam: 8%

- General Fandom: 4%

- Defense of Ship: 4%
## 3.2 Zerrie Sample

### Sentiment Analysis:

<figure><img src="image_83.png" /><figcaption>Fig. 2: Sophiam Sentiment Analysis Results</figcaption></figure>

### By Category:

- Fangirl Behavior: 40%

- Negative: 23%

- Proofs: 10%

- Defense of Ship: 8%

- Misc: 8%

- Retweet/Fav If You Ship: 7%

- Creative Activity: 6«

- General Fandom: 3%

- Ship As Reality: 2%

- Ship As Fantasy: 1%

- Spam: 1%

- Tags: 1%

### Negative Tweets (toward Zerrie) By Category (23%):

- Skepticism/disbelief: 35%

- Disgust: 35%

- Ad Hominem Attacks: 26%

- Negative Fangirl Behavior: 26%

- Straw Man: 13%

- Proofs: 13%

- Coming Out Discussion: 4%
## 3.3 Sophiam Sample

### Sentiment Analysis:

<figure><img src="image_84.png" /><figcaption>Fig. 3: Zerrie Sentiment Analysis Results</figcaption></figure>

### By Category:

- Fangirl Behavior: 47%

- “Relationship goals” tweets: 15%

- Retweet/Fav If You Ship: 14%

- Misc: 12%

- General Fandom: 9%

- Defense of Ship: 3%

- Reality of Ship: 2%

- Negative: 1%

- Tags: 1%

- Ship as Fantasy: 1%
3.4 Larry Sample

Sentiment Analysis:

<figure><img src="image_85.png" /><figcaption>Fig. 4: Larry Sentiment Analysis Results</figcaption></figure>

By Category:

– Fangirl Behavior: 43%

– Ship as Reality: 17%

– Obs/Moments: 14%

– Negative: 8%

– Spam 8%

– Coming Out Discussion: 7%

– Misc: 6%

– Defense of Ship: 5%

– Ship as Fantasy: 5%

– Tags: 2%

– General Fandom: 1%
## 3.5 Narry Sample

### Sentiment Analysis:

<figure><img src="image_86.png" /><figcaption>Fig. 5: Narry Sentiment Analysis Results</figcaption></figure>

### By Category:

- Fangirl Behavior: 67%

- Obs/Moments: 31%

- Friendship Tweets: 9%

- Misc: 7%

- Negative: 5%

- Fantasy: 5%

- Spam: 4%

- Sexual Content: 4%

- Retweet/Fav If You Ship: 3%

- Tags: 2%

- General Fandom: 1%
## 3.6 Ziam Sample

### Sentiment Analysis:

<figure><img src="image_87.png" /><figcaption>Fig. 6: Ziam Sentiment Analysis Results</figcaption></figure>

### By Category:

- Fangirl Behavior: 59%

- Spam: 2%

- Obs/Moments: 18%

- Fantasy: 9%

- Misc: 8%

- Retweet/Fav If You Ship: 6%

- General Fandom: 6%

- Negative: 4%

- Ziam as Reality: 4%

- Tags: 2%

- Sexual Content: 2%

- Creative Activity: 2%

## 4 Discussion

The experiment confirmed the hypothesis that discussions about Larry demonstrate different linguistic behaviors than discussions about other ships within the One Direction fandom on Twitter. However, it was the Elounor sample, not the Larry sample, which provided the strongest evidence of a difference between Larry and other shipping communities in the One Direction fandom.

Most Larry shippers know what it is like to be bullied for their beliefs. Shipping a homosexual relationship between two members of a high-profile band is not an act that
<figure><img src="image_88.png" /><figcaption>Fig. 7: Negative Tweets in the Elounor sample by category.</figcaption></figure>

conforms to the heteronormative mainstream. On the contrary, those who publicly identify as LGBTQ+ or express support for the LGBTQ+ community often experience significant marginalization and suppression. Additionally, public participation in a fandom on any level often exposes people to shaming and mockery by the mainstream (Bennett, 2010). Yet the Elounor sample reveals that Larry shippers have, upon becoming a dominant group within the One Direction fandom, themselves become oppressors.

54% of the tweets in the Elounor sample expressed clearly negative sentiments toward Elounor and those ship it. Nearly all of those negative tweets could be traced back to a Larry shipper. Larry shipper tweets accounted for 60% of the tweets collected for the Elounor sample. 89% of those tweets had clearly negative sentiments toward Elounor.

It has been theorized that the development of opposition narratives on the Internet has a positive impact, fostering a more democratic model of communication and breaking down powerful hegemonies (Dahlberg, 2007). However, the bullying behaviors Larry shippers display suggest that, if oppositional discourses develop enough power, they may fall into the same cycle of oppression as the discourses they seek to dismantle.

The Larry shippers on Twitter utilize classic rhetorical strategies to paint Elounor as the fantasy and Larry as the reality. In the process, they become bully-victims—defending their beliefs from suppression by becoming the suppressors:

> In the most blatantly aggressive tweets, the Larry shippers launch ad hominem attacks on the character of Elounor shippers. For example, one Larry shipper tweeted, “elounor shippers parents: ‘if all of your friends jumped off a bridge would you do it?’ elounor shippers: ‘if they said so.’” This tweet paints the Elounor shippers as naïve and gullible. And by extension, it portrays Larry shippers as enlightened for seeing Elounor as fake. The tweet offers no evidence for Larry or against Elounor to back up these claims.

Although not all of the pro-Larry tweets are as explicitly aggressive as the ad hominem attacks, when broken down, they, too, depend more on rhetorical strategies than concrete evidence and arguments to make their point. Of the seven strategies identified in the sample,
straw man arguments proved most popular, accounting for 24% of the pro-Larry tweets. With this strategy, the Larry shippers took advantage of the lack of vocal Elounor defenders in the Twitter community by replacing their arguments with basic statements about Elounor, statements that could easily be torn apart. For example, one shipper tweeted, “Elounor is real,” but paired the tweet with a screenshot of Harry’s New Year’s tweet: “It’s 2015.” This implied that, by now, it should be obvious to all that Larry, not Elounor, is the real relationship. As with the ad hominem attacks, this strategy did not address real arguments for or against Elounor.

The second most popular approach saw shippers compare affectionate images of Sophiam or Zerrie to images of Louis and Elounor looking bored to suggest that Elounor does not look “loved-up” enough to be a real couple. The evidence many of these tweets provide amounts to a manipulated interpretation of isolated images. With hundreds of pap pictures to choose from, it is easy for Larry shippers to find candid pictures in which Elounor look unhappy, as well as images in which Sophiam and Zerrie appear very much in love. These moments hardly constitute reality. However, in employing rhetorical strategies like this one, it is possible that the Larry shippers have successfully influenced the perception of Larry and Elounor within the fandom.

Conclusions about how many people actively ship a given pairing are beyond the scope of this study. That said, just 14% of the fans in the Elounor sample expressed clear appreciation of the relationship. Every other sample contained significantly more positive sentiments toward the ship discussed than negative. In fact, four of the six samples had less than ten negative tweets. If a majority of One Direction fans still believe Elounor to be a real relationship, it appears they lack the passion and numbers to represent it on social media. By contrast, the Larry fandom has amassed enough social media influence to account for more tweets about Elounor—a ship none of the Larry shippers actively support—than Elounor shippers themselves. Thus, whether the Larry shippers directly discouraged fans from expressing appreciation for Elounor or not, they have successfully disempowered the official One Direction narrative, an incredible feat for a fan community—and one that would not be possible without social media avenues like Twitter. This is an excellent example of the way social media has fundamentally changed the fan-artist relationship.

The Larry sample itself adds further credence to the hypothesis that Larry shippers display different linguistic behaviors than other shipping communities. Notably, 17% of the tweets in the Larry sample contained a variation of the phrase “Larry is real.” While several other samples did discuss the reality of a given ship, such discussion occurred with far less frequency. The Larry sample also featured discussions of a hypothetical “coming out,” implying belief that Louis and Harry were really in love and would eventually announce it to the world. Although one tweet in the Zerrie sample discussed Ziam “coming out,” there were no other mentions of ships besides Larry coming out in any of the samples.

Taken together with the behavior displayed in the Elounor sample, these differences observed in the Larry sample support the hypothesis that Larry shippers engage in linguistic behaviors distinct from other shipping communities within the fandom and consistent with a desire for recognition of their ship as reality.
In general, all of the samples but the Elounor sample contained more positive sentiments than negative. The samples also shared high levels of typical fangirl behavior—in some of the samples, more than half of the tweets display fangirl behavior or language. There were, however, a few differences between the discussions about Band Member + Band Member pairings and romances. The romance samples contained more neutral sentiments, suggesting that the enthusiasm for romances in the One Direction fandom is not as high as that for bromances, even if fans accept the romantic relationships. Additionally, the tweets in the Narry, Ziam, and Larry samples were more likely to feature reactions to interactions between the band members being shipped, or observations that connected to the ship. Perhaps because the ships centered on actual romances are less popular and more private, this form of fan interaction did not occur as often in any of those samples.

The tweets in the Narry sample distinguish that community even further from romance-focused ships. 9% of the tweets in the Narry sample specifically define Narry as a friendship, rather than a romance. While the Ziam sample contains more romantically focused content than the Narry sample, many of the Ziam tweets also portray Ziam as fantasy. The Ziam sample has more tweets focused on fiction constructed around the ship than any of the other samples. Furthermore, a number of tweets in the Ziam sample display tolerance of multiple ships. Unlike Larry shippers, who only ship Harry and Louis together, Ziam shippers appear comfortable with ships between Zayn and Liam and females, as well as other band mates. The fact that Ziam shippers acknowledge and accept Liam and Zayn's romantic relationships suggests that most Ziam shippers do not see Ziam as a real-life romance. This represents an important difference in mindset between the Larry shippers and the Ziam and Narry shippers. Larry shippers do see their ship as reality, and this study affirms the hypothesis that their linguistic behavior reflects that viewpoint.

# 5 Conclusions

This study supports the hypothesis Larry discussions on Twitter possess statistically significant linguistic differences from other shipping discussions. Larry shippers assert their ship's reality at much higher rates than do the other groups in the study, and they are the only group to frequently attack another shipping community in their tweets. The study finds that, by attacking Elounor, Larry shippers have used their numbers and influence within the One Direction fandom to successfully take control of the Elounor narrative on Twitter, dominating discussions about Elounor with rhetoric that undermines the ship's validity. This is a monumental achievement for a fan community. However, the study also reveals that, in the process, the Larry shippers, who represent a belief system that is suppressed by the heteronormative mainstream, have themselves become oppressors. Within the One Direction fandom, more research on fandom behavior over different periods of time is needed. This study only looked at Twitter behavior between September and December 2014. Incidentally, Louis Tomlinson and Eleanor Calder broke up in March 2015, which could drastically change the way in which Larry shippers discuss Elounor. Further research might examine how discussions of Elounor have shifted in light of the break up, as well as how conversations may have evolved in the years leading up to the time span on which this study focuses. Additional
research could also delve into the linguistic behavior and sentiments within the fandom and outside of it following Zayn Malik's departure from the band in March 2015.

Scholars interested in expanding the study beyond the One Direction fandom may pursue linguistic study of another fandom or people-group on social media to see if the behaviors are consistent with the findings of this study.
Bibliography

[1] Anthony, L. (2014). AntConc (Version 3.4.3) [Computer Software]. Tokyo, Japan: Waseda University. Available from http://www.laurenceanthony.net/

[2] Bennett, C. (2010). Flaming the Fans: Shame and the Aesthetics of Queer Fandom in Todd Haynes's Velvet Goldmine. *Cinema Journal*, 49 (2), 17-39.

[3] Dahlberg, L. (2007). The Internet, deliberative democracy, and power: Radicalizing the public sphere. *International Journal of Media & Cultural Politics*, 3 (1), 47-64.

[4] Galuszka, P. (2014). New Economy of Fandom. *Popular Music and Society*, 38 (1), 25-43.

[5] Twitter Search, Monitoring, & Analytics. (2014). Retrieved from http://topsy.com/
# A friend, not a phone call: Brands on Twitter and the future of customer service

Josephine Bromley

University of Leeds

Leeds, UK

**Abstract.** It is clear that social media has had a huge impact on the relationship that consumers and companies have. Consumers expect more and, with the widespread availability and adoption of social media technology, are able to demand more from companies. Evaluating the language that companies use on social media can therefore indicate what strategies companies employ in order to manage the changing relationship between consumers and customers, in the face of the new expectations placed on them. This research suggests that while companies tend to employ language reflective of an individual, human identity in order to establish the relationship of a friend, others continue to maintain a corporate tone. The efficacy of both methods is discussed, suggesting further research into consumer opinion of companies on social media.

**Keywords:** corpus linguistics, customer service, customer experience, brand identity

## 1 Introduction

Social media has become a ubiquitous tool for connectivity, with two out of the top three most used websites globally being social media sites (Alexa Internet, 2015), along with almost one quarter of the world's population registered as users on social media sites in 2014 (eMarketer, 2013). Indeed, it is certainly not unreasonable to claim that “[t]oday, everything is about social media” (Kaplan & Haenlein, 2010 p 67) and just a couple of poignant examples, such as social media’s role in the Arab Spring (Khondker, 2011; Eltantawy & Wiest, 2011; Howard & Hussain, 2011), as well as in the success of the 2008 Obama presidential campaign (Cogburn & Espinoza-Vasquez, 2011; Harfoush, 2009), indicate the pivotal influence social media can have. This study focuses on the effect social media has had on the identity of brands that use this type of media in order to communicate with and connect to consumers, and ultimately what this means for the future of customer service. Section 1 deals with an overview of the change in identity for brands on social media, as well as detailing the data studied and methodology used. Sections 2 and 3 involve analysis of the data and arguments for particular brand identities. Finally, Section 4 and 5 conclude the paper with a discussion of consumers’ reaction to communicating with brands on social media and how this relates to the future of customer service.
## 1.1 The impact of social media

The arrival and wide spread uptake of social media has had a huge impact on the relationship between companies and consumers. Social media has empowered consumers not only in having their voice heard, as noted by Deighton and Kornfeld (2009, p 4) “digital innovations of the last decade have made it effortless, indeed second nature, for audiences to talk back”, but also to even become promoters and distributers of their own offers, with Hennig-Thurau et al (2010, p 311) citing examples such as video content on YouTube and products on eBay. The same authors (2010, p 311) continue to write that this development serves to threaten established business models, with a salient example being the crisis traditional print newspapers face regarding profitability in the digital age (Harper, 2010, p 1). This increase in consumer agency may be seen to be affecting a change in the powerful identity of businesses in a company-consumer relationship that is correlational; as consumers become more empowered, traditional businesses become less authoritative, “the key thing [...] is how social networking shifts power, and in the business context that means shifting power from producer to consumer” (Breakenridge, 2008, p 132). This has consequently caused the identity of the company to shift from a one-way transmitter of products and services to consumers, to a more equal participant in a dialogue, as Holt and Perren (2009, p 8) note, “the ‘broadcasting model’ of one-way communication from a powerful central agency to dispersed and mutually disconnected passive consumers, is giving way at an unprecedented pace to a ‘broadband model’”, where, “consumers are linked in social networks, and productive energy comes from anywhere in the system”.

As a result of this, businesses are arguably forced to establish an identity on these “social networks” that will be accepted by the newly empowered consumer, if they hope to communicate successfully with consumers and build a relationship with them. This is particularly true for consumer-facing, branded businesses, as they rely on an understanding of their customers in order to be able to market and sell their products effectively. Arockiaraj and Baranidharan (2013, p 473) echo this idea when they write “[t]o ensure their brands survival, the brand owners have to seek an ever-greater understanding of what the consumer wants and to develop a relationship between them and the brand”. Indeed, to disregard the importance of the new platform can even be said to be detrimental to the future success of consumer-facing businesses, as Qualman (2009, p 13) notes “while we may only be at the beginning of this revolution [...] it is imperative for social media to be an integral part of a company’s overall strategy”. The impudence of this “revolution” may be therefore seen as a key incentive for brands to engage with social media, as it is an investment in marketing the brand to an ever growing proportion of consumers.

In order to communicate effectively with the empowered consumers on social media, brands are required to define a more personable identity. To this, Fournier and Avery (2011, p 193) note that “as more branding activity moves to the web, marketers are confronted with the stark realisation that social media was made for people, not for brands”. Indeed, in order for customers on social media to engage with brands, Ramsay (2010, p 258) writes that humanised communication is an important factor, “[f]ailure to humanise contact [...] can result in very low or no engagement around a campaign”. Malhotra, Malhotra-Kubowicz,
and See (2012) also advise brands communicating with consumers to “[h]umanise the brand” and “[i]nject emotions”, as they argue that consumers “like messages that paint the brand as a living object and express human emotions”. This humanisation of communication can be seen to lead to a consumer feeling at ‘one’ with the brand (Engeseth, 2005, as cited by Yan, 2011, p 690). Yan (2011, p 691) further quotes Engeseth when he writes, “Engeseth’s theory follows that the separate nature of many brand relationships – the ‘them’ and ‘us’ – is now obsolete”, resulting in the distinction between brand and consumer, in terms of identity, becoming “blurred” (Yan, 2011, p 691), further reflecting the notion of the equalising nature of social media on the power between company and consumer. In this way, the identity of brands on social media may be seen to be that of a personable entity that communicates as an equal to the consumer. In order to investigate the ideas surrounding this humanised identity of brands on social media, the research questions this study hopes to explore are:

- What identity do brands have on social media?, and

- How is this identity established?

In order to answer these questions, I will analyse the language that brands use on social media in order to establish their identity on the platform. In the first instance, creating a successful brand identity is a precise process and company marketing is therefore a carefully considered process (Armstrong & Kotler, 2015, p 33), which includes of course the marketing language used. This investment in developing a brand identity has a clear economic incentive, as Erdogmus and Cicek (2012, p 1354) explain succinctly:

> “As brands gain exclusive, positive, and prominent meaning in the minds of a large number of consumers, they become irresistible and irreplaceable, and win the loyalty of the consumers. Brand loyalty, in return, brings sales revenues, market share, profitability to the firms, and helps them grow or at least maintain themselves in the marketplace.”

True, the meaning that brands have for consumers can take precedence over the product, as may be evidenced in the discussion of the data used for study, with three separate brand brands appearing in the top five best-selling fast moving consumer goods (FMCG) brands of 2014. Finally, and central to the reason for analysing the language brands use, as brands’ communication with consumers on social media is a key research focus for this study, then it is appropriate to study what these brands are actually posting on the platform.

## 1.2 The data

Social media cannot be defined by a particular genre, but instead should be viewed as encompassing outlets that are numerous and varied. For example, Mangold and Faulds (2009, p 358) cite examples of social media outlets such as social networking sites (Facebook, MySpace), company-sponsored websites and blogs (Apple.com, P&G's Vocalpoint) and collaborative websites (Wikipedia). The social media outlet Twitter is defined as “a microblogging service” where “users tweet about any topic within the 140-character limit and follow others to receive their tweets” (Kwak, Lee, Park & Moon, 2010, p 591). Established in 2006 (Carlson, 2011),
Twitter now has an estimated 288 million users, with an average 10 million new users joining the platform every year's quarter since 2010 (Statista, 2015a), highlighting again the massive growth of social media.

Twitter was chosen as the social media outlet of study for this paper because it lends itself well to the research focus of company-consumer relationships and the identity of brands on social media. Indeed, Twitter is a key platform “for dialogic communication between companies and consumers” (Lin & Peña, 2013 p 17), which entails that companies must be able to establish a clear brand identity in order to engage customers. The data comes from the Twitter accounts of the top 10 best-selling FMCG brands in the United Kingdom (UK) from 2014 according to Brand Footprint 2014, an online data source which “reveals how consumers around the world today are buying FMCG brands” (Kantar Worldpanel, 2014a). FMCG brands were focussed on because their target audience is everyday consumers, such as those that use social media sites like Twitter. Indeed, the site is also one of the most popular forms of social media in the UK as of 2014 (Statista, 2015b). The data was collected over a time period of the first two years of each brand’s Twitter use, as the brands started using the platform at different times. The full breakdown of the data is displayed in Table 1, with the order of the brands representing their ranking in Brand Footprint 2014 (Kantar Worldpanel, 2014b).

<table><caption>Table 1: Breakdown of data in corpus.</caption><thead><tr><td>Company</td><td>Time Period</td><td>Tokens</td></tr></thead><tbody><tr><td>Warburtons</td><td>10/2011 – 09/2013</td><td>78,245</td></tr><tr><td>Heinz</td><td>04/2013 – 11/2014</td><td>28,791</td></tr><tr><td>McVitie's</td><td>10/2010 – 09/2012</td><td>31,425</td></tr><tr><td>Hovis</td><td>12/2010 – 11/2012</td><td>9,859</td></tr><tr><td>Kingsmill</td><td>12/2011 – 11/2013</td><td>11,928</td></tr><tr><td>Birds Eye</td><td>05/2013 – 11/2014</td><td>43,922</td></tr><tr><td>Müller</td><td>07/2011 – 06/2013</td><td>2,840</td></tr><tr><td>Walkers</td><td>01/2009 – 12/2010</td><td>199,550</td></tr><tr><td>Coca Cola</td><td>12/2011 – 11/2013</td><td>82,157</td></tr><tr><td>Cadbury's</td><td>07/2010 – 06/2011</td><td>240,460</td></tr><tr><td>Total Corpus Size</td><td></td><td>729,177</td></tr></tbody></table>

## 1.3 Difficulties with the data

As is always the case when working with real instance of language use, the data is not entirely ‘clean’. For instance, not all brands use Twitter to the same extent, with two of the brands’ Twitter use starting only in 2013, meaning that there was not two years’ worth of data available for collection for every brand (Table 1), as the data was collected in December 2014. Further, not all brands used Twitter to the same extent, again resulting in an inequality
between the sub-corpora. Nevertheless, despite these imperfections, the corpus still provided adequate data for analysis, and methodological practises, such as comparing the data in terms of percentages relative to the sub-corpora, have arguably served to remedy the issue of differences in sub-corpora size.

## 1.4 Methodology

Biber, Connor and Upton (2007, p 2) write that “[c]orpus linguistic studies are generally considered to be a type of discourse analysis because they describe the use of linguistic forms in context”. A corpus linguistic approach was chosen as the method for the study of the discourse of brands in a social media context because it allows for generalisations to be made about real instances of language use, due to the representative nature of the text sample (Biber, Connor & Upton, 2007, p 3). Baker (2006, p 13) also concludes that corpora are useful in providing better evidence for “an underlying hegemonic discourse” than relying on intuition about a single text sample alone. Furthermore, corpora allow for researcher bias to be reduced because it becomes less easy to be selective about certain texts, “we at least are able to place a number of restrictions on our cognitive biases” (Baker, 2006, p 12).

McEnery and Hardie (2012, p 1) also note “corpus linguistics is a heterogeneous field” and a mixture of methodologies within corpus linguistics is often utilised in order to more convincingly address the research question/s. For this study, a mixture of both quantitative and qualitative analysis have been used, for example by analysing the frequency of a certain word across sub-corpora, as well as investigating specific uses of the word in concordance lists in order to gain a fuller picture of how the word is used. This follows the benefits of triangulation, in that “more than one method should be used in the validation process to ensure that the variance reflected that of the trait and not of the method” (Cambell & Fiske, 1959, as cited by Jick, 1979, p 602). In order to explore the data, the corpus analysis software Wordsmith (Scott, 2012) was utilised, as it allows for this mixture of both qualitative and quantitative analysis.

## 2 Customer service

In the 2015 report for The Definitive Guide to Social Customer Service (Conversocial, 2015, p 5), it is written that “social media is changing the entire business of customer service, posing great challenges and presenting new opportunities for brands”. This may be seen because it is causing a shift, as outlined in Section 1, from one-way communication from a powerful company, to more equalised, humanised communication. The report notes this communication as being one of the five pillars of the concept of “social first”, placing social media at the centre of customer engagement (Conversocial, 2015, p 13), with the pillar in question being to “[c]onnect with customers on a deeply personal and emotional level to build relationships and trust” (Conversocial, 2015, p 13), thereby reflecting this notion of a more humanised identity.
## 2.1 Customers and consumers

Twitter may be seen as a key platform on which companies can provide customer service to consumers also using this form of social media. Indeed, even on the Twitter website itself, it is suggested that providing customer service is a major reason for businesses to be on Twitter (Twitter Basics, 2015). Thomases (2010, p 280) writes that Twitter can be very conductive to delivering certain aspects of customer service, and that reaching out on the platform to help customers with issues and problems is a focal point for many brands' Twitter strategy. The reasons for using Twitter in this way comprise benefits for both company and consumer. For example, the real-time conversational nature of the platform means that customer issues can be handled immediately, which is favourable for consumers, while the transparent nature of companies' responses allows companies to demonstrate their expertise and commitment to customer service in a visible and impactful way (Postman, 2009, p 54). An initial search of the words 'customer*' and the synonymous 'consumer*', with the asterix indicating that this search included plural forms of the words, in the entire corpus revealed that the words occurrences amount to 0.04% and 0.06% respectively. Figure 1 displays the usage of both words in percentages according to each brand, with the percentage on top referring to occurrences of 'consumer*' and the percentage beneath to 'customer*'. Although some brands may be seen to use these words more than others, with Heinz's use of 'consumer' being particularly salient, when these words are used in general across the entire corpus they can be seen to collocate almost exclusively in reference to entities related to customer services (Table 2), which would seem to reflect this method of using Twitter to rectify customer issues. Furthermore, the word 'sorry' appears as the 80th most frequent word in the corpus, which may again indicate the common utilisation of Twitter as a method of providing customer service, as sorry suggests that the companies are apologising for something. This ranking increases to 64th position in the keyword list for the corpus, indicating that its usage is particularly salient. Baker (2006, p 125) writes that a keyword list give a measure of saliency, rather than simply frequency, as it measures the frequency of a particular word's occurrence in general language use against that in the corpus being studied. This serves to provide a greater indication of notable usage of the word being examined. The keyword list for the corpus of this study was created by comparing it with a sample of the British National Corpus, "a 100 million word collection of samples of written and spoken language [...], designed to represent a wide cross-section of British English" (The British National Corpus, 2007). Figure 2 displays the distribution of 'sorry' in each sub-corpus. As may be seen in this distribution, as well as indicated in the previous Figure 1 and Table 2, Heinz can be seen to be a main contributor to the semantic field of customer service present in the corpus, along with the brands Kingsmill and Birds Eye, as suggested in Figure 2.

Although these brands may be seen to be responsible for the majority of language related to customer service, most brands in the corpus may be seen to provide some form of customer service. Table 3 displays qualitative examples of this provision of customer service by the brands. Regarding what this means for the identity of brand, this arguably results in a somewhat discrepant effect on a more humanised identity. Although there are logically beneficial reasons for using Twitter in this way, as outlined above, this may be
<figure><img src="image_89.png" /><figcaption>Fig. 1: Percentage of occurrence of ‘customer*’ and ‘consumer*’ according to each sub-corpus.</figcaption></figure>

<table><caption>Table 2: R1 collocates of ‘customer*’ and ‘consumer*’ in the entire corpus.</caption><thead><tr><th>Word</th><th>R1 Collocate</th><th>Percentage</th></tr></thead><tbody><tr><td rowspan="3">customer</td><td>care</td><td>40.5%</td></tr><tr><td>relation<sup>1</sup></td><td>2%</td></tr><tr><td>service</td><td>53.6%</td></tr><tr><td colspan="2"></td><td><strong>Total 96.1%</strong></td></tr><tr><td rowspan="7">consumer</td><td>care</td><td>2%</td></tr><tr><td>response@cokecce</td><td>7%</td></tr><tr><td>information centre</td><td>3%</td></tr><tr><td>relations</td><td>0.6%</td></tr><tr><td>care@hjheinz.com</td><td>81%</td></tr><tr><td>services@unitedbiscuits.com</td><td>0.5%</td></tr><tr><td>@walkers.co.uk</td><td>3%</td></tr><tr><td colspan="2"></td><td><strong>Total 97.1%</strong></td></tr></tbody></table>

seen to construct a situation where brands uphold a depersonalised, corporate identity when communicating with consumers. In support of this notion of the impersonal identity of corporations, Toth and Heath (2009, p 166) write that “the study of communication in the corporate or organisational context makes especially challenging questions about [...] the degree of ‘personalness’”, noting interestingly as well that the corresponding terms for ‘Inc.’ in French and Spanish are société anonyme and sociedad anónima, “[t]he corporation is then the anonymous society” (Toth & Heath, 2009, p 166). Sheth, Sisodia and Wolfe (2007, p 229) further note that traditional corporate company values do not particularly identify with “the human species”, quoting to this William Vanderbilt, the 19th century American railroad magnate, “[t]he public be damned. I am working for my stockholders”. Although not as overtly disregarding of consumers as William Vanderbilt, some brands in the corpus may be seen to indeed communicate with consumers in an impersonal manner. For instance,
focussing on the concordance list for 'sorry' in the Heinz sub-corpus, it is revealed that the
phrase:

"Sorry to hear, please call us on 08005285757 or email your telephone number to
consumercare@uk.hjheinz so we can call you."

is repeated 276 times, accounting therefore for 58% of all occurrences of 'sorry' in the Heinz
sub-corpus. It is clear that this tweet from Heinz is a canned response, noted as being
disheartening to customers as the customer raising the issue feels they are not considered on
a personal basis (Performance Research Associates, 2012, p 130). Kingsmill may also be seen
to use such a practise of a mechanical, rather than human response, as 75% of the instances of
'sorry' occur in the same repeated phrase (Figure 3). The repetitive nature of these responses
may be seen to reflect McGuinn's (2009) point that the foundation of customer service is
mechanistic in nature, noting that this is a key weakness of customer service because it
fails to treat the consumer as an individual, "[t]he customer is viewed in indirect terms,
with a focus on collective customers rather than individual customers" (McGuinn, 2009,
p 61). In this way, customer service interactions would seem to affect the communication
with consumers negatively, with regards to a humanised brand identity, as the language
can become impersonal and indifferent to the customer's emotional satisfaction. Indeed,
Meyer and Schwager (2007, p 1) also note that customer service tends to be dispassionate,
"a paucity of the personal touch [is] evidence of indifference to what should be a company's
first concern: the quality of customers' experiences".

<figure><img src="image_90.png" /><figcaption>Fig. 2: Distribution of ‘sorry’ among sub-corpora.</figcaption></figure>
<table><caption>Table 3: Examples of brands providing customer service.</caption><thead><tr><td>Brand</td><td>Example</td></tr></thead><tbody><tr><td>Warburtons</td><td>"Hi! That doesn't sound right. Could you contact customer care team?<br>0800 243 684 or talk2us@warburtons.co.uk"</td></tr><tr><td>Heinz</td><td>"We're sorry to hear about this. Please contact us on 0800 5285757. We'd appreciate the chance to talk. Consumer Care"</td></tr><tr><td>McVitie's</td><td>"Sorry to hear that. Please contact customer services on vipclubsupport@coinks.com and they'll sort it out for you."</td></tr><tr><td>Hovis</td><td>"That does not look good. Do not fear, we will forward it to our customer services dept."</td></tr><tr><td>Kingsmill</td><td>"Hi there, we've checked with customer services and we replied on the 4th Dec. We will send it out again as you didn't get it."</td></tr><tr><td>Birds Eye</td><td>"We'll pass your email address onto our Consumer Care team and they'll get in contact with you. Thank you."</td></tr><tr><td>Walkers</td><td>"Hmmm... That's strange. In that case, it's best if you contact our customer care team... Šam"</td></tr><tr><td>Coca</td><td>"Hi Simon, that does sound unusual! You can talk to our Consumer Information Centre about it on 0800 22 77 11 (press option 3)"</td></tr><tr><td>Cola</td><td>"how very disappointing! There's a customer service contact in the pack that should be able to help you..."</td></tr><tr><td>Cadbury's</td><td>"that should be able to help you..."</td></tr></tbody></table>

## 2.2 Grammar of apologies

The way in which Heinz apologises arguably has the effect of mitigating the degree of fault the company assumes, which may be seen to echo this notion of apathetic communication of corporate companies. For example, the list of 3-word clusters, which Biber, Conrad and Cortez (2004, p 372) explain are "multi-word prefabricated expressions" that function similarly to single word units, that are generated in the concordance list of 'sorry' in the Heinz sub-corpus reveals that the phrase 'sorry to hear' is the most frequent cluster to collocate with 'sorry' (Figure 4), occurring 83% of the time. Arguably, this distances the company from the responsibility for the customer's issue, as the syntax of the phrase would seem to be noting a state of sorrow, rather than explicitly apologising, as observed by Battistella (2014, p 58), "[s]aying 'I'm sorry' is different from saying 'I apologise'. The former reports on an internal state of the speaker but does not literally perform an apology". The author (2014, p 58) continues that the grammar of 'sorry' allows for an infinitive to be used, and if this is an infinitive verb of perception, as in 'sorry to hear', then this is interpreted as expressing empathy rather than an apology. In this way, the point that Heinz is 'sorry to hear' about the customer's problem, but is not 'sorry for' the problem serves to remove Heinz's responsibility from the issue. Indeed, there are only five instances of the verb 'apologise' in the entire corpus (Example 1), and none of these occur in the sub-corpora of Heinz, Birds Eye or Kingsmill, the brands that would seem to lean most towards using Twitter as a platform to provide customer service. The only brands that do use this verb are Walkers and McVitie's, with only McVitie's explicitly using the verb in response to a customer complaint, which may
be evidenced by the fact that they are replacing “dented tins”. In fact, there is barely any explicit ownership of mistakes the company has made which have caused the customer to be dissatisfied in any collocations of ‘sorry’ in the Heinz sub-corpus. This may be found in terms of transitivity, a grammatical system which “construes the world of experience into a set of process types” (Halliday, 2004, p 170). There are just two occurrences of material processes in the Heinz sub-corpus which collocate with ‘sorry’ (Table 4), with the vast majority being relational processes, with the difference between the two process types being that material clauses are “processes of doing & happening” and relational clauses are “processes of being & having” (Halliday, 2004). This gives the impression that Heinz has not done anything and has made no mistake, but rather that they simply are ‘sorry’ that this has happened. The instances of ‘sorry’ in the Kingsmill and Birds Eye sub-corpora were also examined in order to check the number of material process types, and it was found that there was only one occurrence of a material process for Birds Eye (Table 4), which may be seen to be in collocation with a humorous use of the word ‘sorry’ rather than an apologetic use, and no occurrences for Kingsmill. Ultimately, this may be viewed as a dispassionate response from the company, as it does not provide consumers with a true sense that they have received an apology. In this way, the language use of these brands in particular may be seen to display incongruent behaviour regarding establishing a personable, humanised identity on social media.

<table><caption>Fig. 3: Example of canned response from Kingsmill sub-corpus.</caption><tr><td>hi Laura We are sorry to hear this. Please send your details to careline@lovekingsmill.co.uk and we will reimburse you. Thanks</td></tr><tr><td>Hi Dani We are sorry to hear this. Please send your details to careline@lovekingsmill.co.uk and we will reimburse you. Thanks</td></tr><tr><td>@FivewayFilms We are sorry to hear this. Please send your details to careline@lovekingsmill.co.uk and we will reimburse you. Thanks</td></tr><tr><td>Hi Lucy We are sorry to hear this. Please send your details to careline@lovekingsmill.co.uk and we will reimburse you. Thanks</td></tr><tr><td>Hi Chris We are sorry to hear this. Please send your details to careline@lovekingsmill.co.uk and we will reimburse you. Thanks</td></tr><tr><td>@ee61re: We are sorry to hear this. Please send your details to careline@lovekingsmill.co.uk and we will reimburse you. Thanks</td></tr><tr><td>@carolineyes10: We are sorry to hear this. Please send your details to careline@lovekingsmill.co.uk and we will reimburse you. Thanks</td></tr><tr><td>@KingsmillCare: We are sorry to hear this. Please send your details to careline@lovekingsmill.co.uk and we will reimburse you. Thanks</td></tr><tr><td>Hi Aimee We are sorry to hear this. Please send your details to careline@lovekingsmill.co.uk and we will reimburse you. Thanks</td></tr></table>

<table><caption>Fig. 4: Top five most frequent 3-word clusters to collocate with ‘sorry’ in Heinz sub-corpus.</caption><thead><tr><td>N</td><td>Cluster</td><td>Freq.</td><td>Length</td></tr></thead><tbody><tr><td>1</td><td>SORRY TO HEAR</td><td>396</td><td>3</td></tr><tr><td>2</td><td>TO HEAR PLEASE</td><td>278</td><td>3</td></tr><tr><td>3</td><td>HEAR PLEASE CALL</td><td>273</td><td>3</td></tr><tr><td>4</td><td>PLEASE CALL US</td><td>250</td><td>3</td></tr><tr><td>5</td><td>TO HEAR ABOUT</td><td>57</td><td>3</td></tr></tbody></table>
**Example 1.** All instances of ‘apologise’ from entire corpus.

“@walkers_crisps 13 Apr 2010 @kohsamui14 That’s very true! I do apologise! :) Ŝam”

“@walkers_crisps 14 Apr 2010 @RosieAllOver I do apologise! ;o) I think you should buy some new flavours and end the temptation! It’s only right! Ŝam”

“@McVities 13 Aug 2012 @DJMOG1 Oh no, can’t apologise enough for that! Will get a new one (make that 2!) out to you asap, we have your details”

“@walkers_crisps 13 Apr 2010 @MosherAngel No need to apologise. Thanks for the feedback :) Ŝam”

“@McVities 13 Aug 2012 @DJMOG1 No, we apologise for all the probs so far, you seem to have been extra unlucky! 2 new dent free tins will be with you assap.<fnref n="2" />”

This lack of humanisation in the identity of the brands discussed above may be seen as a failure in utilising the full capacity of Twitter to provide a more humanised customer service. As mentioned at the beginning of this section, social media is changing customer service, from “anonymous, one-to-one channels” toward “public, one-to-many channels that are [...] attached to real identity” (Conversocial, 2015, p 4). Indeed, in The Future of Customer Service report by trendwatching (2014, p 15) it is predicted that “webcam enabled face-to-face interaction” with a customer service representative will be a growing practise within companies’ customer service remit, thereby providing a definite human interaction. The report writes that this trend echoes the imperative for the customer to feel “[l]istened to, valued and cared for” (trendwatching, 2014, p 4). This acknowledgement of the importance of customers’ emotions is also noted by Frow and Payne (2007, p 91), when writing about creating an “‘outstanding’ or ‘perfect’ customer experience”, in that companies “will need to consider the creation of customers’ experiences from both rational and emotional perspectives”. Note the authors’ use of ‘experience’ rather than ‘service’. Although this may be seen as a pedantic semantic point, by using the word ‘experience’, this arguably highlights the distinction between the impersonal communication manifested in the language use of the brands above and a more personable, humanised interaction. This type of interaction and the effect this has on the identity of the brands in the corpus are explored in Section 3.

<table><caption>Table 4: Material processes that collocate with ‘sorry’ in Heinz and Birds Eye sub-corpora.</caption><tr><td>Heinz</td><td>we no longer produce any of the puddings</td></tr><tr><td>sorry</td><td>we don't make Big Soup pots anymore</td></tr><tr><td>Birds Eye</td><td>we never carry cash – just waffles</td></tr></table>

<footnote n="2">² spelling error made by brand, not by the author of this study.</footnote>
## 3 Customer experience

Moving on from the conceivably depersonalised identities that are established by the brands discussed in Section 2, other brands in the corpus may be seen to be active in establishing an identity more in line with the humanised identities of brands on social media in order to engage consumers, with this engagement also arguably more proactive in relation to the reactive nature of brands when managing customer service issues.

Initial evidence for this engagement may be seen in the form of asking questions to involve the customer in a dialogue, which is a traditional advertising technique of “simulating the processes, structure and dynamism of everyday conversation” (Frank, 1989, p 255). For example, Figure 5 displays the percentage of questions which include the second person pronoun in all its forms, including ‘you’, ‘your’, ‘yours’ and ‘yourself’, out of all occurrences of questions in each sub-corpus, with the percentages above each column indicating the total percentage occurrence of questions across the sub-corpora. By using the second person pronoun, the brands may be seen to be using direct address in order to engage the consumer, “generic forms of ‘you’/‘your’ draw audiences into the discourse” (Pollach, 2005, p 296). Moreover, questions including reference to the second person serve to establish a dialogue with the referee, as it is inherent in proposing a question that an answer is expected, as Thornborrow and Wareing observe (1998, p 192) “asking questions is a powerful way of addressing readers, since questions do not usually occur without a potential answer”. As Figure 5 denotes, the majority of questions asked by all brands in the corpus contain direct address, with 100% of questions posed by Heinz referring to the second person. This may be seen to be as a result of Heinz’s preoccupation of using Twitter mainly as a platform to provide customer service, with the majority of tweets asking customers to send their details (e.g. “Sorry to hear that. Help us report this to our team. Can you send us a DM with the codes from the can & your name & address?”). This prevalence of questions including direct address can be seen to reflect the brands’ attempts to proactively engage the consumer in a dialogue “questions [...] have a direct appeal in bringing the second person into a kind of dialogue with the writer” (Webber, 1994, as cited by Thompson, 1998, p 138), in order to build an interactive relationship with them.

### 3.1 Content of tweets

After engaging the customer into conversation, the content of the brands' tweets also reveals that it is geared towards consumer engagement with the brand. For example, in the word list for entire corpus 'win' appears as the 72nd most frequent word. In order to examine all forms of the words, the distribution of the 'win' lexeme between the sub-corpora was quantified (Figure 6). Again, the keyword list for the corpus was referred to, which further evidenced the saliency of the word 'win', as the word appears in 55th position. The word is also the most frequent lexical item in terms of the language the companies have chosen to use, rather than lexical items that are inherent in the site themselves. For instance, the words 'view' and 'conversation' appear much more frequently than 'win' in 11th and 12th position respectively, but this is due to a functionality of Twitter, which allows users to view a series
<figure><img src="image_91.png" /><figcaption>Fig. 5: Percentage of questions and questions with reference to second person among sub-corpora.</figcaption></figure>

of tweets between particular users. Mangold and Fauld (2009, p 362) suggest that examples
of customer-engaging promotions include contests, online games and online competitions.
Following this, the salient occurrence of ‘win’ across the entire corpus indicates the way in
which companies attempt to engage with consumers on social media, by inviting them to
enter competitions to win prizes. For example, the most common prize that can be won may
be seen to be ‘tickets’, as it is the second most frequent noun collocate, after ‘chance’, to
occur with ‘win’ (Figure 7).

<figure><img src="image_92.png" /><figcaption>Fig. 6: Percentage distribution of ‘win’ lexeme.</figcaption></figure>
Along with competitions and prizes, games played simply for the enjoyment of winning can be seen to be an engagement strategy employed. A notable instance of this was instigated by Cadbury's, when they invited users to take part in a Tweetoff every Thursday. Users could play along by simply tweeting "#Spots" or "#Stripes", dependent on which side, either Spots or Stripes, they chose to support. The winning team would be that which achieved the greatest number of tweeted hashtags by a certain time. Although prizes could be won by randomly selected members of the winning team, a further incentive for playing was arguably the possibility of simply winning the game, by collecting points for the chosen team. Indeed, Lee (2011, p 118) notes that "playfulness is a core activity in many new media contexts", and the specific mode of Twitter can be seen to lend itself well to this type of customer engagement of mass participation in a game. Because of the hashtag function on Twitter, which indicates a topic of discussion (Twitter, 2014), users are able to instantly join a particular topic and therefore, as in the case of the Tweetoff, participate in a game. Indeed, Cadbury's may be categorised as being one of the more playful brands in the corpus, with the brand using the lexeme 'win' the second most frequently, behind McVitie's, as well as accounting for 59% of all overall occurrences of the word 'win', contributing heavily therefore to the word's notable position in the word list for the entire corpus. However, it is worth to bear in mind that the weekly Tweetoff was part of the wider Spots v Stripes campaign, launched in 2011 to coincide with the London 2012 Olympic Games (Bainbridge, 2011), as Cadbury's was an official sponsor for the Olympic Games (BBC News, 2008). Therefore the brand's heavy use of the word 'win' and organisation of the mass Tweetoff game may be said to be due to a vested interest in promoting London 2012, due to the obligations of sponsorship, rather than purely establishing themselves as having a playful identity. More evidence for this may be seen in the high frequency of the collocate '2012' (Figure 7), which may be seen to be in reference to London 2012. This is not to say, of course, that the decision to sponsor London 2012 was not made without considering the impact on the identity of the Cadbury's brand, but rather that this sponsorship may be said to have allowed for a playful identity on Twitter, rather than Cadbury's establishing a playful identity on the platform in the first instance.

<figure><img src="image_93.png" /><figcaption>Fig. 7: Screen shot of top 11 collocates of 'win'.</figcaption></figure>
Nevertheless, as Figure 6 suggests, other brands in the corpus would seem to employ an element of playfulness in establishing their identity on Twitter. Take for example, McVitie's, not a sponsor of London 2012, and the most prolific user of the lexeme 'win' in the corpus (Figure 6). The prizes available to be won by McVitie's if a user takes part in "#FreebieFriday", the name of the hashtag associated with competitions on the McVitie's profile, are varied and may be seen to be more often than not unrelated to McVitie's products. For example, Table 5 displays the most frequent noun collocates of 'win' before explicit mention of the type of products McVitie's sells, 'biscuits'. The right-hand collocation position of nouns such as 'iPhone', 'cinema' and 'tickets' indicate that these are the objects that are available to be won, due to the transitive nature of the verb to win, in that it may take an object (Hurford, 1994, p 242), with the qualitative examples from the McVitie's sub-corpus underneath Table 5 suggesting evidence for this indication (Example 2). The point that prizes which bear little relation to the product that McVitie's sells, "the UK's favourite biscuits and cakes" (United Biscuits, 2014), may suggest that the activity of playing and engaging with consumers with this way takes precedence over explicit promotion of the product.

<table><caption>Table 5: Most frequent noun collocations of 'win' before 'biscuits' collocation in McVitie's sub-corpus.</caption><thead><tr><th>Collocate</th><th>Frequency of Left-hand Collocation</th><th>Frequency of Right-hand Collocation</th><th>Total Frequency</th></tr></thead><tbody><tr><td>Chance</td><td>30.5%</td><td>0%</td><td>30.5%</td></tr><tr><td>Draw</td><td>19.7%</td><td>1.2%</td><td>20.9%</td></tr><tr><td>iPhone</td><td>0%</td><td>8.2%</td><td>8.2%</td></tr><tr><td>Cinema</td><td>0%</td><td>6.4%</td><td>6.4%</td></tr><tr><td>Tickets</td><td>0%</td><td>6.4%</td><td>6.4%</td></tr><tr><td>Time</td><td>1.9%</td><td>3.8%</td><td>5.7%</td></tr><tr><td>Day</td><td>3.2%</td><td>2.5%</td><td>5.7%</td></tr><tr><td>Voucher</td><td>0%</td><td>5.0%</td><td>5.0%</td></tr><tr><td>Box</td><td>0%</td><td>5.0%</td><td>5.0%</td></tr><tr><td>Mug</td><td>0.6%</td><td>3.9%</td><td>4.5%</td></tr><tr><td>Pair</td><td>0%</td><td>4.5%</td><td>4.5%</td></tr><tr><td>Biscuits</td><td>0%</td><td>3.9%</td><td>3.9%</td></tr></tbody></table>

**Example 2.** Qualitative examples of right-hand collocates of 'win'.

> "Wow, this must be your lucky week! You should enter our draw to win 2 cinema tickets w/popcorn & drinks"

> "Today's draw is for a Kindle!!! Get over to http://mcvitiesvipclub.co.uk to enter + you'll go in the Dec 25 draw to win an iPhone 4S!"

This may also be reflective of a recommended Twitter strategy of companies on the platform in order to engage consumers. Sierra (2012, as cited by O'Reilly & Milstein 2012,
p 223) says that “[w]ith few exceptions, the worst mistake a ‘business blog’ can make is to blog about the business”, while Kaplan and Haenlein (2011, p 110) note that “the first rule of micro-blogging is to focus on messages that are relevant for the target group”. Indeed, this strategy may be seen in the content of the brands’ tweets. For instance, Birds Eye uses the hashtag ‘#foodjoke’ after writing a food-related joke (Example 3), which may be seen as a way of engaging customers, by tweeting about things related to the brand’s products, rather than the actual products themselves. The use of humour may furthermore be seen as an engagement strategy, “[p]eople like to laugh, funny things are appreciated” (Malhotra, Malhotra-Kubowicz & See, 2012), and simultaneously works towards establishing an identity for the brand that is more human. To this, Brown (2012, p 41) writes that a sense of humour allows customers to discover that the company is staffed by “real people”, as opposed to “mechanoids without a soul”, while Burkhardt (2010, p 12) notes that, in regards to communication with “a personal touch” on social media, “[b]eing human involves humour”.

Arguably, this technique of tweeting about things unrelated to the product in a playful sense serves to reflect a more humanised identity, as the emphasis on sales and profitability is reduced because the consumer is not being overtly and directly sold the product. Instead, the consumer is presented with an invitation to engage with the brand under more light-hearted circumstances, such as by playing a game or entering a competition, ultimately allowing the brand to establish an identity which is more personable and even more likeable.

Therefore, consumers interacting with these brands on Twitter are arguably provided a proactive experience, which is in contrast to the reactive, depersonalised service interaction as explored in Section 2.

**Example 3.** All instances of ‘#foodjoke’ in Birds Eye sub-corpus.

#FoodJoke: What's the fastest vegetable? A runner bean!

“Why did the tomato blush? Because it saw the salad dressing! #foodjoke”

“Why do the french like to eat snails? Because they don’t like fast food. #foodjoke”

“Waiter, will my pizza be long? No sir, it will be round! #foodjoke”

“What is black, white, green and bumpy? A pickle wearing a tuxedo! #foodjoke”

**Example 4.** All instances of first person singular pronoun use in Müller sub-corpus.

“@FreshMilkDaily 18 Mar 2013 @ironfarmer86 John – dm me with the contact details and I’ll put farm services in touch.” “@FreshMilkDaily 10 Mar 2013 @Rob_Hitch a little confused. Did you message @freshmilkdaily? I’ve passed your tweet to farm services – they’ll be in touch.” “@Fresh Milk Daily 14 Nov 2011 @chriswalkland chris – sitting on your email now. We’ll take username and password off I think…”

“@FreshMilkDaily 6 Apr 2013 @ironfarmer86 sort with your contacts in the marketing team. I’ve passed your tweet to them.”
## 3.2 Pronoun use

This personable identity may be further evidenced in the brands' use of pronouns. Waugh (2010, p 82) writes that “[i]n the identity literature there has been some attention given to personal pronouns like I, you, we, they as important indicators of identity”. De Fina (1995, p 380) also references studies that have looked at “pronouns within social diexis”, citing Levinson (1983), who writes “the social identities of participants” are systematically encoded in pronominal paradigms (De Fina, 1995, p 380). In this way, observing the pronoun use of brands in the corpus can help to determine the humanised identity of brands on Twitter.

In a word list of the entire corpus, the second person pronoun 'you' appears around 30% more frequently than the first person plural pronoun 'we' (Figure 8). This may again be seen as an established engagement strategy by using direct address, “[advertisement's] use of 'you' is part of a high-involvement strategy which attempts to win us over by very direct address” (Cook, 1992, p 157). However, what may strike as noteworthy about the pronoun use in the corpus is that around 1/7th of first and second person pronoun use refers to the first person singular, resulting in a greater occurrence of first person pronoun use overall. This may be said to subvert the expected use of pronouns that may be seen in traditional marketing language, which can be said to focus more on the consumer. Indeed, this is inferred by Cook (1992, p 156) when writing about pronouns in advertising, “most striking and most frequent [...] and most divergent from the uses of other discourse types, is the ubiquitous use of 'you', with Kalmane (2012, p 87) also noting that in advertising “[t]he second person pronouns are used more than first person pronouns”. In order to explore this atypical use of pronouns further, the brands' pronoun use was broken down into further detail (Table 6). The figures of first and second person pronoun use for each sub-corpus include use of pronouns in all forms, which includes personal pronouns (e.g. 'I'), reflexive pronouns (e.g. 'ourselves'), possessive pronouns functioning as adjectives (e.g. 'your') and possessive pronouns functioning as nouns (e.g. 'yours'). As displayed in the table, the majority of brands would seem to conform to the expected first and second pronoun usage in advertising discourse, with the majority of pronouns being in the second person. Yet, it is clear that some brands do not follow this convention, and it may be said that none have an overwhelming majority of second person pronoun usage that is suggested in the literature, “[a]dvertisements are peppered with you’s and I’s and we’s and your’s – especially the you’s” (Sedivy & Carlson, 2011, p 165). This notable distribution of pronouns may therefore be interpreted as the brands’ attempt at establishing a more human identity. In a similar study to this one, Kwon and Sung (2013) studied elements of brand anthropomorphism, including pronouns, and found that, just as may be viewed in Table 6, there was roughly a 50-50 split between first person pronouns and second person pronouns, “[a]pproximately 54% of the tweets contained personal pronouns: 736 with second-person and 650 with first-person pronouns” (Kwon and Sung, 2013, p 11). The researchers noted that by using pronouns in this way, “marketers try to imbue human personality into their brands” (Kwon & Sung, 2013, p 4). Thus, in the same way, by regularly using the first person pronoun, the brands can be seen to be consistently marking a humanised identity.
<figure><img src="image_94.png" /><figcaption>Fig. 8: Total pronoun use in entire corpus.</figcaption></figure>

The notable occurrence of ‘I’ in the corpus may be seen to further this notion of a “human personality”, according to Small, Geldart, Gutman, and Clarke-Scott (1998, p 298) “the use of first person pronouns [...] is an expression of one’s personal identity”. It may be worth to point out that all brands apart from Kingsmill use a mixture of both first person singular and first person plural (Table 6), which may be seen to be as a phenomena related to the discourse of Twitter. Boyd, Golder and Lotan (2010, p 5) write that regarding the conversational aspects of Twitter “[a]mbiguities abound [...] with the respect to pronoun usage in the content of messages”. This may be due to differences in the conversational function of initial tweets in comparison to replies. Indeed, referring again to Kwon and Sung’s study (2013), it was found that the first person was more common in tweeted replies, “more anthropomorphism variables occurred in replies than original tweets” (Kwon & Sung, 2013, p 14). True, Müller displays this kind of variability of pronouns in replying to tweets (Example 4). By writing, for example, “We’ll take off your username and password I think”, the writer of the tweet can be seen to be referring to both Müller as a whole company in the first person plural pronoun, while the first person singular pronoun refers to the writer themselves and their own uncertainty towards the statement about one of the company’s practises, manifested in the use of epistemic modality in “think”. In this way, these tweets from the Müller data with the inclusion of the first person singular pronoun gives the impression that a real person has written this response.

This perception of a person, and thus a human identity, may also be seen most prominently in the Walkers sub-corpus, which can be viewed in Table 6 to be an exceptional user of the first person singular, as it occurs close to four times as often as the first person plural. Closer analysis of the concordance line for ‘I’ in the Walkers sub-corpus reveals that this would seem to be accredited to a referee with a name: ‘Sam’ (Figure 9). Walkers appears to be the only brand in the corpus which employs this technique of including the tweet author’s name. This may be viewed as a tactic intended to humanise the organisation and as a consequence the brand, as Pollach (2005, p 294) notes “[c]ompanies appeal to readers’ emotions when they present the people behind the organisation” such as when “members are identified with names”. Focussing on this qualitative example from the Walkers sub-corpus (Figure 9), some of these tweets by ‘Sam’ would seem to emulate the language that a real person could conceivably use. For example, Sam thanks another Twitter user for an “awesome
<table><caption>Table 6: First and second person pronoun use in each sub-corpus.</caption><thead><tr><th rowspan="3">Brand</th><th colspan="3">Percentage of pronoun use out of total first and second person pronoun use for each sub-corpus</th></tr><tr><th colspan="2">First Person</th><th>Second Person</th></tr><tr><th>Singular (e.g. 'I', 'my')</th><th>Plural (e.g. 'we', 'our')</th><th>(e.g. 'you', 'your')</th></tr></thead><tbody><tr><td>Warbutons</td><td>4.9%</td><td>44.3%</td><td>50.8%</td></tr><tr><td>Heinz</td><td>1.9%</td><td>42.7%</td><td>55.4%</td></tr><tr><td>McVitie's</td><td>1.2%</td><td>41.1%</td><td>57.7%</td></tr><tr><td>Hovis</td><td>7.1%</td><td>50.1%</td><td>42.8%</td></tr><tr><td>Kingsmill</td><td>0.0%</td><td>39.0%</td><td>61.0%</td></tr><tr><td>Birds Eye</td><td>2.0%</td><td>49.5%</td><td>48.5%</td></tr><tr><td>Müller</td><td>6.2%</td><td>47.7%</td><td>46.2%</td></tr><tr><td>Walkers</td><td>39.6%</td><td>10.3%</td><td>50.1%</td></tr><tr><td>Coca Cola</td><td>1.1%</td><td>45.5%</td><td>53.4%</td></tr><tr><td>Cadbury's</td><td>12.0%</td><td>43.8%</td><td>44.2%</td></tr></tbody></table>

recommendation” and asks if they are still performing music (Line 788), while declaring her/his enjoyment in listening to “Snow Patrol” (Line 792), “Stereophonics” (Line 796) and “Paolo Nutini” (Line 797). Sam also asks other Twitter users if they are doing anything nice (Line 800) and if they are going anywhere nice (Line 805). These tweets from Sam bear no relation to Walkers products, and arguably read as genuine “turns” in a conversation, suggesting a conversational sequence (Hutchby & Wooffitt, 2008, p 13), or remarks indicative of the discourse of social media, where users “confess their personal thoughts and feelings” (Zappavigna, 2012, p 28). This production of seemingly authentic discourse of a real person may be seen to be an extension of Fairclough’s (2001) notion of “synthetic personalization”, which is explained as “a compensatory tendency to give the impression of treating each of the people ‘handled’ en masse as an individual” (2001, p 52). With this in mind, ‘Sam’ can indeed be seen to connect with people at an individual level, while the public nature of conversations on Twitter arguably helps to give the impression that Walkers treats all people “en masse” in this individual manner. Fairclough (2001, p 184) further notes that “[a] common dimension of synthetic personalization is simulated equalization”, which again may be reflected in the questions ‘Sam’ asks other Twitter users, “Are you doing anything nice?” (Line 800), seemingly simulating a discourse between friends, and thus ‘equals’, who take an interest in one another’s activities. Ultimately, the conception of ‘Sam’ as a human representative for Walkers can be seen to afford the brand truly a humanised identity.

In this way, along with the other brands discussed in this section, Walkers may be seen to provide consumers with a customer experience, where the brand is imbued with human characteristics and where the consumer is treated on an individual, personalised basis, where their emotional status is taken into account (“How’s it going?” (Line 788)). As opposed to the brands analysed in Section 2, these brands may be seen to be employing the capability
<figure><img src="image_95.png" /><figcaption>Fig. 9: Qualitative extract from the concordance list for ‘Sam’ in the Walkers sub-corpus.</figcaption></figure>

of Twitter to engage consumers by appealing to them on an emotional level, creating a
personable humanised experience and therefore reflecting the future of customer service.

4 Discussion

It must be emphasised that the structure of this study, which separates brands that maintain
a more detached corporate identity from brands that establish a more humanised identity,
was not done with the intention of making definite black-and-white distinctions between the
identities of the brands. As has hopefully been gathered by the quantitative displays of data
from the whole corpus, it is clear that all brands use a mixture of customer service language
and language more in line with the discourse of customer experience. Due to this, it may be
worth to consider what effect this could have on the brands’ identities. For example, referring
back to Section 2, brands such as Walkers, which arguably has a very humanised brand
identity, may also be seen to provide customer service on Twitter. An instance indicating
Walkers’ provision of customer service can be seen in the following tweet:

“@Reeb198Hi. Sorry 2hear ur unhappy with the french fries. If you’d like 2 request a refund, pls call customer services on 0800 274 777 ÂSam”

By merging these two identities, as shown in this example, of a business service with
the friend-like persona of ‘Sam’, this may perhaps cause discordance in the identity of the
brand. O’Reilly and Milstein (2012, p 221) suggest that “opening an account or several just
for customer service” may be a way in which to clearly direct consumers to register their
problems effectively, while De Beule (2014) also notes that there are certain cases where it is beneficial to have a separate customer service account. Nevertheless, the author maintains that “[n]o matter what kind of questions customers are dealing with, they seek to personally engage with a company” (De Beule, 2014). In this way, De Beule echoes the notion behind the title of this study, A friend, not a phone call, in that customers respond better to a humanised, known identity than detached customer service communication with a person they do not know.

Yet, in contrast to this argument, the opposite may also be suggested in that customers have been found to be unresponsive to, or even dislike, this friendly identity. For instance, Andriuzzi (2015) found that participants in a qualitative interview study did not particularly like when the brand attempted to engage consumers by discussing other topics unrelated to the company’s products or services, while Baird and Parasnis (2011, p 1) discovered that “most [consumers] do not engage with companies via social media in order to feel connected”, rather that they mostly engage in order to receive discounts on products. This pragmatism may be seen to be in contrast to the emotional connection that consumers are thought to desire. This may be seen to be related to the notion of social distance which “specifies the extent to which individuals have a close or a more distant relationship” (Meyer, 2010, p 64). For this reason, because consumers do not feel a true closeness to a brand in exactly the same way as they would to a friend, as suggested in the use of synthetic in “synthetic personalization”, then this may perhaps be the reason why consumers may reject or ignore this type of engagement strategy from companies. In this way, the depersonalised identity of brands discussed in Section 2 may perhaps be a more comfortable and appropriate interaction for consumers who feel this way.

<table><caption>Table 7: Ranking in Brand Footprint 2014 and no. of followers on Twitter as of May 2015.</caption><thead><tr><th>Companies in Brand Footprint 2014 order ≠</th><th>Companies in order of number of followers on Twitter</th><th>No. of followers</th></tr></thead><tbody><tr><td>Warburtons</td><td>Cadbury's</td><td>253,000</td></tr><tr><td>Heinz</td><td>Coca Cola</td><td>106,000</td></tr><tr><td>McVitie's</td><td>Walkers</td><td>51,300</td></tr><tr><td>Hovis</td><td>McVitie's</td><td>32,000</td></tr><tr><td>Kingsmill</td><td>Warburtons</td><td>19,800</td></tr><tr><td>Birds Eye</td><td>Birds Eye</td><td>13,200</td></tr><tr><td>Müller</td><td>Hovis</td><td>3848</td></tr><tr><td>Walkers</td><td>Müller</td><td>1509</td></tr><tr><td>Coca Cola</td><td>Heinz</td><td>927</td></tr><tr><td>Cadbury's</td><td>Kingsmill</td><td>714</td></tr></tbody></table>

With regards to the kind of consumer this may be, it would be of use to consider characteristics of the target audience for particular brands. For example, Table 7 shows
that Hovis places on the lower end of the scale in terms of number of followers on Twitter, which may be seen to indicate how engaged customers are with the brand on the platform, as Castronovo and Huang (2012, p 120) write that engagement is signified by “the size of the community”. However, the average Twitter user in 2012 was an American female aged between 15-25 (beevolve, 2012), in comparison to Hovis’ target audience, British women aged between 25-64 with children (Newsworks, 2013). Thus, it may be suggested that there still remains a disparity between the type of consumer actually purchasing the product and the type of Twitter user that brands could potentially engage with. Nevertheless, as predicted in Section 1 of this study, it may be ill-advised of brands to believe this will always be the case and a presence on the platform may still be seen as a necessary investment.

# 5 Conclusion

This study attempted to define the identities of the top 10 best-selling FMCG brands of 2014 on Twitter and how these are established using certain, salient linguistic features. For example, it was argued that by using the first person singular pronoun, brands can be seen to establish a more humanised identity, whereas brands that mitigate responsibility in the transitivity structure of their apologies may be viewed as having a more impersonal identity. The former identity can be seen to reflect trends in the future of customer service, while the latter may arguably fail to engage customers on social media in the same way.

However, in order to explore these conclusions further, as indicated in the previous Discussion Section, there is much to be said for further research into how consumers truly perceive brands on Twitter and other forms of social media, as well as consumers’ reactions to brands’ engagement strategies. This could be explored by analysing the discourse between brands and consumers, in order to gather an indication of what engagement strategies from companies that consumers respond to the most, as well as how consumers feel about this kind of communication. A continuation of a corpus linguistics approach may also be suitable in order to provide representative evidence of company-consumer interaction on social media, combined with instances of qualitative analysis, to be able to add richer detail to the research evidence.
Bibliography

[1] Alexa Internet. (2015). The top 500 sites on the web. Retrieved from: http://www.alexa.com/topsites

[2] Andriuzzi, A. (2015). The tweeting brand: When conversation leads to humanization. *Twitter Mix Days*, 24 April, Lyon.

[3] Armstrong, G. & Kotler, P. (2015). *Marketing: An Introduction*. 12th ed. Essex: Pearson Education.

[4] Arockiaraj, G. & Baranidharan, K. (2013). Impact of Social Media on Brand Awareness for Fast Moving Consumer Goods. *International Journal of Logistics & Supply Chain Management Perspectives*. 2(4). 472-477. Retrieved from: http://search.proquest.com/openview/fe07cd28f66db95a88cd42aa83fe0b57/1?pq-origsite=gscholar

[5] Bainbridge, J. (2011). Spots v Stripes: Cadbury versus the critics. Retrieved from: http://www.marketingmagazine.co.uk/article/1063866/spots-v-stripes-cadbury-versus-critics

[6] Baker, P. (2006). *Using Corpora in Discourse Analysis*. London: Continuum.

[7] Battistella, E.L. (2014). *Sorry about that: The language of public apology*. Oxford: Oxford University Press.

[8] BBC News. (2008). Cadbury becomes Olympic sponsor. 20 October. Retrieved from: http://news.bbc.co.uk/1/hi/england/london/7679635.stm

[9] beevolve. (2012). An Exhaustive Study of Twitter Users Across the World. Retrieved from: http://www.beevolve.com/twitter-statistics/

[10] Biber, D., Conrad, S. & Cortes, V. (2004). If you look at...: Lexical Bundles in University Teaching and Textbooks. *Applied Linguistics*. 25(3). 371-405. Retrieved from: http://www.corpus4u.org/forum/upload/forum/2005110322472496.pdf

[11] Biber, D., Connor, U. & Upton, T.A. (2007). *Discourse on the Move: Using corpus analysis to describe discourse structure*. Amsterdam: John Benjamins Publishing Co.

[12] Boyd, D., Golder, S. & Lotan, G. (2010). Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. In: *System Sciences (HICSS), 2010 43rd Hawaii International Conference on System Sciences*, 5-8 January 2010, Hawaii. Hawaii: IEEE Computer Society. 1-10. doi: 10.1109/HICSS.2010.412

[13] Breakenridge, D. (2008). *PR 2.0: New Media, New Tools, New Audiences*. New Jersey: Pearson Education.

[14] British National Corpus, The, version 3 (BNC XML Edition). (2007). Distributed by Oxford University Computing Services on behalf of the BNC Consortium. http://www.natcorp.ox.ac.uk/

[15] Brown, E. (2012). *Working the Crowd: Social media marketing for business*. 2nd ed. Swindon: British Informatics Society.

[16] Burkhardt, A. (2010). Social media: A guide for college and university libraries. *College & Research Libraries News*. 71(1). 10-24. Retrieved from: http://crln.acrl.org/content/71/1/10.short
[17] Carlson, N. (2011). The Real History of Twitter. *Business Insider*. 13 April. Retrieved from: http://www.businessinsider.com/how-twitter-was-founded-2011-4?IR=T

[18] Castronovo, C. & Huang, L. (2012). Social Media in an Alternative Marketing Communication Model. *Journal of Marketing Development and Competitiveness*. 6(1). 117-134. Retrieved from: http://www.na-businesspress.com/jmdc/castronovoc_web6_1_.pdf

[19] Cogburn, D.L. & Espinoza-Vasquez, F.K. (2011). From Networked Nominee to Networked Nation: Examining the Impact of Web 2.0 and Social Media on Political Participation and Civic Engagement in the 2008 Obama Campaign. *Journal of Political Marketing*, 10, 189-213. doi: 10.1080/15377857.2011.540224

[20] Cook, G. (1992). The Discourse of Advertising. London: Routledge. Conversocial. (2015). *The Definitive Guide to Social Customer Service*. Retrieved from: http://www.conversocial.com/social-customer-service/introduction#.VUjWrvlVhHw

[21] De Beule. (2014). Should a Company Create a Separate Customer Service Twitter Handle? January 14. *engagor*. Retrieved from: https://engagor.com/blog/should-a-company-dedicate-a-separate-customer-service-twitter-handle/

[22] De Fina, A. (1995). Pronominal Choice, Identity and Solidarity in Political Discourse. Text – *Interdisciplinary Journal for the Study of Discourse*. 15(3). 379-410. doi: 10.1515/text.1.1995.15.3.379

[23] Deighton, J.A. & Kornfeld, L. (2009). Interactivity's Unanticipated Consequences for Marketers and Marketing. *Journal of Interactive Marketing*. 23(1). 2-12. doi: 10.1016/j.intmar.2008.10.001

[24] Eltantawy, N. & Wiest, J.B. (2011). Social Media in the Egyptian Revolution: Reconsidering Resource Mobilization Theory. *International Journal of Communications*. 5. 1207-1224. Retrieved from: http://ijoc.org/index.php/ijoc/article/view/1242/597

[25] eMarketer. (2013). Social Networking Reaches Nearly One in Four Around the World. *eMarketer*. Retrieved from: http://www.emarketer.com/Article/Social-Networking-Reaches-Nearly-One-Four-Around-World/1009976

[26] Erdogmus, I.E. & Cicek, M. (2012). The impact of social media marketing on brand loyalty. *Procedia: Social and Behavioural Science*. 58. 1353-1360. doi: 10.1016/j.sbspro.2012.09.1119

[27] Fairclough, N. (2001). *Language and Power*. 2nd ed. Abingdon: Pearson Education.

[28] Fournier, S. & Avery, J. (2011). The uninvited brand. *Business Horizons*. 54. 193-207. doi: 10.1016/j.bushor.2011.01.001

[29] Frank, J. (1989). On conversational involvement by mail: The use of questions in direct sales letters. *Text and Talk*. 9(2). 231-259. doi: 10.1515/text.1.1989.9.2.231

[30] Halliday, M.A.K. (2004). *An Introduction to Functional Grammar*. London: Arnold.

[31] Harfoush, R. (2009). *Yes We Did! An inside look at how social media built the Obama brand*. Berkeley: Pearson Education.

[32] Harper, R. A. (2010). The Social Media Revolution: Exploring the Impact on Journalism and News Media Organizations. *Student Pulse*. 2(3). Retrieved from: http://www.studentpulse.com/a?id=202

[33] Hennig-Thurau, T., Malthouse, E.C., Friege, C. Gensler, Lobschat, L., Rangaswamy, A. & Skiera, B. (2010). The Impact of New Media on Customer Relationships. *Journal of Service Research*. 13(3). 311-330. doi: 10.1177/1094670510375460
[34] Holt, J & Perren, A. (2009). *Media Industries: History, Theory and Method*. Oxford: Wiley-Blackwell.

[35] Howard, P.N. & Hussain, M.M. (2011). The Role of Digital Media. *Journal of Democracy*. 22(3). 35-48. Retrieved from: http://muse.jhu.edu/login?auth=0&type=summary&url=/journals/journal_ofDemocracy/v022/22.3.howard.html

[36] Hurford, J.R. (1994). *Grammar: A Student's Guide*. Cambridge: Cambridge University Press.

[37] Hutchby, I & Wooffitt, R. (2008). *Conversation Analysis*. Cambridge: Polity Press.

[38] Jick, T.D. (1979). Mixing Qualitative and Quantitative Methods: Triangulation in Action. *Administrative Science Quarterly*. 24(4). 602-611. doi: 10.2307/2392366

[39] Kalmane, R. (2012). *Advertising: Using Words as Tools for Selling*. 2nd ed. Riga: Lulu Enterprises.

[40] Kantar Worldpanel. (2014a). What is Brand Footprint?. Retrieved from: http://www.brandfootprint-ranking.com/what-brand-footprint/

[41] Kantar Worldpanel. (2014b). Explore The Data: UK/FMCG Ranking. Retrieved from: http://www.brandfootprint-ranking.com/report/ranking/fmcg/2013,2012/country/uk/

[42] Kaplan, A.M. & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. *Business Horizons*. 54. 59-68. doi: 10.1016/j.bushor.2009.09.003

[43] Kaplan, A.M. & Haenlein, M. (2011). The early bird catches the news: Nine things you should know about micro-blogging. *Business Horizons*. 54. 105-113. doi: 10.1016/j.bushor.2010.09.004

[44] Khondker, H.H. (2011). Role of New Media in the Arab Spring. *Globalizations*. 8(5). 675-679. Retrieved from: http://www.tandfonline.com/doi/citedby/10.1080/14747731.2011.621287

[45] Kwak, H., Lee, C., Park, H. & Moon, S. (2010). What is Twitter, a Social Network or a News Media?. In: *WWW 2010, 26-30 April 2010, Raleigh. North Carolina: International World Wide Web Conference Committee*. 591-600. Retrieved from: http://www.eecs.wsu.edu/~assefaw/CptS580-06/papers/2010-www-twitter.pdf

[46] Kwon, E.S. & Sung, Y. (2013). Follow Me! Global Marketers' Twitter Use. *Journal of Interactive Advertising*. 12(1). 4-16. Retrieved from: http://jiad.org/article149.html

[47] Lin, J. & Peña, J. (2013). Are You Following Me? A Content Analysis of TV Networks' Brand Communication on Twitter. *Journal of Interactive Advertising*. 12(1). 17-29. Retrieved from: http://jiad.org/article150.html

[48] Mangold, W.G. & Faulds, D.J. (2009). Social media: The new hybrid element of the promotion mix. *Business Horizons*. 52. 357-365. doi: 10.1016/j.bushor.2009.03.002

[49] Malhotra, A., Malhotra-Kubowicz, C. & See, A. (2012). How to Create Brand Engagement on Facebook. *MIT Sloan Management Review*. 18 December. Retrieved from: http://sloanreview.mit.edu/article/how-to-create-brand-engagement-on-facebook/

[50] McEnery, T. & Hardie, A. (2012). *Corpus Linguistics: Method, Theory and Practise*. Cambridge: Cambridge University Press. Retrieved from: http://www.dawsonera.com
[51] McGuinn, C. (2009). The Future of Customer Service. *Irish Marketing Review*. 20(1).
57-66. Retrieved from: http://connection.ebscohost.com/c/articles/44050938/
future-customer-service

[52] Meyer, C & Schwager, A. (2007). Understanding Customer Experience. *Harvard Business Review*. 1-11. Retrieved from: https://hbr.org/2007/02/understanding-customer-experience

[53] Meyer, C.F. (2010). *Introducing English Linguistics*. Cambridge: Cambridge University Press.

[54] Newsworks. (2013). Hovis: Case Study. Retrieved from: http://www.newsvorks.org.uk/case-studies/67824

[55] O'Reilly, T & Milstein, S. (2012). *The Twitter Book*. 2nd ed. Sebastopol: O'Reilly Media.

[56] Performance Research Associates. (2012). *Delivering Knock Your Socks Off Service*. New York: American Management Association.

[57] Pollach, I. (2005). Corporate self-presentation on the WWW: Strategies for enhancing usability, credibility and utility. *Corporate Communications*. 10(4). 285-301. doi: http://dx.doi.org/10.1108/13563280510630098

[58] Postman, J. (2009). *SocialCorp: Social Media Goes Corporate*. Berkeley: Pearson Education.

[59] Qualman, E. (2009). *Socialnomics: How Social Media Transforms the Way We Live and Do Business*. New Jersey: John Wiley & Sons. Retrieved from: https://www.dawsonera.com

[60] Ramsay, M. (2010). Social media etiquette: A guide and checklist to the benefits and perils of social marketing. *Database Marketing & Consumer Strategy Management*. 17(3).
257-261. doi: 10.1057/dbm.2010.24

[61] Scott, M. (2012). *WordSmith Tools version 6*. Liverpool: Lexical Analysis Software.

[62] Sedivy, J. & Carlson, G. (2011). *Sold on Language: How Advertisers Talk to You and What This Says About You*. Chichester: Wiley-Blackwell.

[63] Sheth, J. N., Sisodia, S. R. & Wolfe, B. D. (2007). *Firms of Endearment: How World-Class Companies Profit from Passion and Purpose*. New Jersey: Wharton School Publishing.

[64] Small, J.A., Geldart, K., Gutman, G. & Clarke-Scott, M-A. (1998). The discourse of self in dementia. *Ageing and Society*. 18(3). 291-316. doi: http://dx.doi.org/ (About DOI)

[65] Statista. (2015a). Number of monthly active Twitter users worldwide from 1st quarter 2010 to 4th quarter 2014 (in millions). *Statista: The Statistics Portal*. Retrieved from: http://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/

[66] Statista. (2015b). Active reach of social networking services in the United Kingdom (UK) in March 2014, by device. *Statista: The Statistics Portal*. Retrieved from: http://www.statista.com/statistics/272805/social-networking-sites-active-reach-in-the-united-kingdom-by-device/

[67] Thomases, H. (2010). *Twitter Marketing: An Hour A Day*. Indianapolis: Wiley Publishing.
[68] Thompson, S. (1998). Why Ask Questions in Monologue? Language choice at work in scientific and linguistic talk. In: Hunston, S. ed. *British Studies in Applied Linguistics: Language at Work*. 137-150.

[69] Thornborrow, J & Wareing, S. (1998). *Patterns in Language: An introduction to language and literary style*. London: Routledge.

[70] Toth, E.L. & Heath, R. (2009). *Rhetorical and Critical Approaches to Public Relations*. Abingdon: Routledge.

[71] trendwatching. (2014). The Future of Customer Service. Retrieved from: http://trendwatching.com/x/wp-content/uploads/2014/09/2014-09-FUTURE-OF-CUSTOMER-SERVICE.pdf

[72] Twitter. (2014). The Twitter Glossary. Retrieved from: https://support.twitter.com/articles/166337-the-twitter-glossary

[73] Twitter Basics. (2015). Learn Twitter: How your business can use Twitter. Retrieved from: https://business.twitter.com/basics/learn-twitter

[74] United Biscuits. (2014). About McVitie's. Retrieved from: http://www.mcvities.co.uk/about

[75] Waugh, L.R. (2010). Pronominal choice in French conversational interaction: Indices of national identity in identity acts. In: Tanskanen, S-K., Helasvuo, M-L., Johansson, M. & Raitaniemi, M. *Discourses in Interaction*. Amsterdam: John Benjamins Publishing Co. 81-100.

[76] Yan, J. (2011). Social media in branding: Fulfilling a need. *Journal of Brand Management*. 18(9). 688-696. doi: 10.1057/bm.2011.19

[77] Zappavigna, M. (2012). *Discourse of Twitter and Social Media: How We Use Language To Create Affiliation On The Web*. London: Continuum. Retrieved from: https://www.dawsonera.com
# The tweeting brand: When conversation leads to humanization

Andria Andriuzzi

Sorbonne Business School
Paris, France
andria@andriuzzi.com *

**Abstract.** Brands can now express themselves through conversation on social media sites such as Twitter but should marketers mix brand personification and conversational techniques together? Based on a series of interviews with web users, this research shows that brands conversing in this way can be seen to be more human and can improve their image even without the physical presence of a speaker. However, simply being present on Twitter or personifying the brand will not suffice. To be more human, brands must participate in quality conversation. Consumers evaluate conversation according to its context, content and form.

**Keywords:** Conversation, Brand, Personification, Anthropomorphism, Social Media, Twitter

## 1 Introduction

As “semiotic instances” (Semprini, 1992), brands have a long tradition of co-producing meanings. Brands convey their own values using advertising to shape people’s perceptions across a number of years (Michel, 2013). With the development of the Internet, consumers are now considered to be fully involved in a co-creation process (Cova & Cova, 2009). Today, echoing the consumers power of expression, brands can also make their voices heard through conversation on social media sites like Twitter. As some consumers supposedly view brands as a ‘person’ thanks to anthropomorphism (Aaker & Fournier, 1995), the use of human language in interaction could generate such reactions. Alongside other personification techniques like the use of celebrities or fictional characters (Cohen, 2014), conversation can be seen as an attempt to humanize the brand by adopting a human behavior.

Just as certain everyday conversations can have a positive or a negative effect on us, we believe that every brand conversation has a different value. While several studies focus primarily on the number of comments or on their valence (de Vries, Gensler & Leeflang, 2012) and methodologies used by specialized agencies are sometimes not very clear (Tissier-Desbordes & Vernette, 2012), this research looks more specifically at the quality of such

<footnote>* The author would like to thank Professor Géraldine Michel, Director of Chaire Marques & Valeurs, for her kind advice, Elise Yarwood, for her help in editing the article, and Xavier Cazard of Entrecom for his support. Correspondence concerning this article should be addressed to Andria Andriuzzi, IAE de Paris, 21 rue Broca, 75240 Paris Cedex 05, France.</footnote>
conversation. The content analysis of a series of interviews reveals that a brand's behavior on social media sites can make it appear more human. However, what is more important than brand personification is the quality of conversation, creating an effect of anthropomorphism that positively affects the brand's image as well as word of mouth and purchase intentions. This article presents the three pillars of quality brand conversation from the consumers' point of view and it is, to our knowledge, the first instance in which these notions have been documented. Whether they are participating in brand conversations or simply observing, consumers evaluate the quality of brand conversation according to its context, content and form. Practitioners are therefore encouraged to develop personalized and valuable conversations in order to strengthen the links between brands and consumers.

## 2 Theoretical framework: Anthropomorphism and brand personification

Brands are not welcome on social media as these tools were originally created for human beings (Fournier & Avery, 2011). Nevertheless, they can be somewhat successful if they adopt the codes of social media sites, such as transparency and accepting a certain loss of control (ibid.). For example, community managers frequently converse in a way similar to that of other users on social media sites. Nevertheless, one study shows that for less-known brands, the adoption of an informal and conversational communication style counter-intuitively reduces confidence in the brand (Gretry et al., 2014). With this in mind, is it really a good idea for these brands try to look more human on social media sites like Twitter?

Anthropomorphism is the propensity of some people to attribute human characteristics to inanimate objects. It is an individual psychological characteristic (Epley, Waytz & Cioppo, 2007). For many years, research has linked the concept of anthropomorphism to the relationship between brands and consumers (Aaker & Fournier, 1995). Anthropomorphism strengthens the relationship with a brand to the point that it can instill loving feelings (Rauschnabel & Ahuvia, 2014). While many articles deal with brand relationship (Fournier, 1998) and brand personality (Aaker, 1997), some researchers question their theoretical foundations and operational applications (Avis, 2012; 2013). Although the propensity of consumers to see brands as people is still debated, practitioners often try to make their brands look more human with the use of personification techniques. Brand personification is the use of a patronymic name, or the promotion of a celebrity, a fictional character or a brand employee in their external communications (Cohen, 2014). It can lead to users identifying with the brand as a fellow person, causing positive effects, particularly on attitudes towards the brand. It can, however, also lead to negative effects. For example, the use of employees in commercials is, strangely enough, something that can lead people to doubt the brand's sincerity (Fleck, Michel & Zeitoun, 2014). Similarly, suspicious consumers appear to have less trust in products with a patronymic brand name (Eskine & Locander, 2014). Moreover, a brand does not need to be personified for consumers to attribute human qualities to it. Logos can, in this way, portray a sense of personality that influences purchase intent (Payne et al., 2013) and the use of narrative themes inspired by history or folklore can humanize a brand (Hede & Watne, 2013).
A new aspect introduced by social media sites such as Twitter is the ability for brands to converse with consumers (Gensler & Völckner, 2013). When brands use conversation - a human characteristic if any - can it create the effects of anthropomorphism for the public online? When the "the brand is speaking" on Twitter, do consumers see the brand as a person? If they are perceived as more human because of these interactions, can it lead to positive attitudes from consumers? In order to answer these questions we carried out a qualitative study and interviewed consumers directly.

# 3 Method

We conducted 12 semi-structured, individual, face-to-face interviews of varied durations, lasting from fifty-eight minutes to one hour thirty-four minutes. This method is best suited to the study of motivations, attitudes and perceptions (Bardin, 1977). Additionally, this method was used to gain insights into the perceptions of Twitter users who actively participate in brand conversation but also those who simply view these interactions, as this category represents the majority of consumers. In general, most consumers interact very little with brands online (Mathwick, 2002). It is, however, interesting to investigate the perceptions of those that view these conversations but do not interact. With this in mind, these points of view were also taken into consideration. We used a convenience sampling strategy that was limited to people using the Internet and social media but did not filter these users according to their level of online interaction with brands. Furthermore, interviewees were not asked if they were Twitter users or not. This decision was made considering that some tweets or some Twitter conversations can be seen elsewhere other than on Twitter, if they are quoted in the media for example and we wanted to collect opinions and feelings from Twitter users as well as from non-Twitter users. Thus, we believe that to understand the impact of Twitter on consumers it is important to take into account the opinions of non-Twitter users as they can be exposed to the brand conversation vicariously. Considering that demographic characteristics can result in diverse reactions to marketing campaigns on social media (Campbell, Ferraro & Sands, 2014), we used age and gender as selection criteria. Two men and two women were selected from three age groups: 18-34, 35-59 and over 60. We also paid attention to the diversity of socio-professional categories and selected participants from a broad range of professions including: an engineer, an entrepreneur, a marine officer, a human resource manager, a painter, two public employees, a touristic guide, a librarian and 3 retired people - two former managers and a former assistant. An interview guide was used that included topics covering the presence of brands on social media, the observation of exchanges between brands and users and the possibility of participation in these conversations.

Even though it was not a selection criteria, we tried to understand if the interviewees had taken part in any Twitter brand interactions. That is to say we ‘tried’ to understand because it was not obvious that all interviewees had noted whether they had ever participated in “brand conversations” or even observed one. For example, it is interesting to note than during a whole one-hour interview, Denis, 68, denied ever having interacted with a brand online. Nonetheless, at the very end of the interview he remembered and thoroughly explained an interaction he had had with his bank, concerning a complaint that was posted on Twitter.
Among the twelve interviewees, nine of them observed some kind of brand conversation on social media, and three of them more specifically on Twitter. Half of them noted participating in brand interaction – even if this was as little as a simple comment on a brand post. This could be seen as a low number but it is, in fact, consistent with the rather low figures noted elsewhere concerning Twitter users participation, which is surprising considering the social media networks high level of awareness and media exposure. Indeed, research institute IPSOS stated that only 5 percent of the French population hold and use a Twitter account (2013). Furthermore, only 2 percent of web users report having ever commented on a brand post on social media, according to IFOP (2011). All of this considered, we were also interested in the reactions of people who observe without participating and we therefore presented interviewees with four stimuli in the form of screenshots at the end of each interview to observe how they reacted to the different types of online interactions between brands and consumers. Consequently, the twelve interviewees were able to express what they felt when observing Twitter brand conversations.

The transcripts of these interviews are 266 pages and 93,655 words long. After a dozen interviews, a phenomenon of information saturation was observed (Glaser & Strauss, 1967). Therefore, we deemed twelve interviews sufficient for the purposes of the study and went on to analyze our data. The collected material was subjected to a content analysis, as this technique can shed light on the non-explicit meaning of speech (Frisch, 1999). We made a preliminary analysis on a quick reading of the transcript and then used qualitative analysis software NVivo to code and categorize the meaning units in the order of utterance, organizing categories and subcategories throughout the analysis.

# 4 Results

With the analysis of complete and direct verbal contributions from interviewed participants (reproduced verbatim in this section), we are able to assess how web users perceptions vary depending on the nature of a speaker within brand conversation. We go on to discuss the positive and negative effects of conversation and to identify four key perceptions of brands depending on the representation of the speaker and the attitude of the users towards conversation. Subsequently, we present the criteria for quality conversation.

## 4.1 Conversational brands and anthropomorphism

When “the brand speaks”. On Twitter, brands are often identified by their name and logo. However, it is possible that the source of the account is seen to be uncertain. How the source is perceived and represented mentally also varies significantly depending on the situation and on the Internet users. The “tweeting brand” generates different projections: the situation and the discourse imply the nature of the spokesperson, even if this person is not physically represented. The impression of a ‘real conversation’, if any, is created by the use of message personalization and the visibility of a brand’s responses to other web users. Consumers can then assign human qualities to the brand such as a sense of empathy, someone that listens and responds:
"Yeah, I like it when it's an exchange, when it isn't just a claim from the client, but a claim which is echoed, and a response. I'm interested in interaction, in communication (...). They try to reach people just like a friend writing and responding. It's as if it's a friend. It's a communication style." (Judith, 69).

Even without the physical representation of the speaker, a brand can appear to have a personality (Aaker, 1997). However, message personalization does not guarantee such a perception. For example, looking at an interaction between a brand and a user on Twitter, one interviewee doubted the sincerity of the exchange:

"I think that if a brand comes and talks to me specifically, then it's okay. But if a brand decides to talk to people it will be a sea, an ocean of comments and we'd never have time to read all the comments in the end. (...) I would think that it's an automated thing (laughs). Yes, like a machine. (...) A large chain wouldn't make the time for us ..." (Marina, 38).

Thus, the act of having a Twitter account is not sufficient to make a brand more human. The interactions on this account create different perceptions of the brand.

**When the brand is personified.** In some situations brands are represented on social media by a spokesperson, for example when experts answer on Twitter for consumer-care services. This is a personification technique comparable to the use of employees or celebrities in commercials (Fleck, Michel & Zeitoun, 2014). This human presence is often perceived as "more credible". However, although a number of the verbal responses collected indicate that this sort of brand representative can give the impression that the brand-consumer relationship is more human, it is not necessarily enough to gain the trust of consumers:

"Interactions with such delegates have to be ... You must have the feeling... the feeling you really talk to someone, you know. Not with someone who doesn't care, not an automated response. You see. Because, well, you can also have... You can also talk to a real person who sounds like they are giving you an automated response. That's frustrating too. I like when you really have the feeling that you're speaking to a human being, that the guy's going to ... even maybe add a touch of humor, something like that ... You know, like in a real discussion. It's more reassuring." (Guillaume, 39).

On social media, an interaction with a real person can be perceived as dehumanized. This perception reminds us of call centers, where the standardization of responses sometimes dehumanizes the relationship (Fielding, 2003).

To summarize, brands that speak on social media may seem more human, even without the physical representation of a speaker. Furthermore, personification does not guarantee an anthropomorphic effect on Internet users and brand behavior is, essentially, more important than the representation of a speaker. This leads us to consider the positive and negative effects of brand conversation.
## 4.2 Brand conversation effects on consumers

**Positive effects of conversation.** When brands are perceived as altruistic, authentic and caring, their attitude in conversation is considered as positive. Brand conversation has a number of potential effects on consumers and our interviewees noted that some conversations could influence purchase intents. This influence could come from the other web users participating in conversations, confirming the impact of online consumer-to-consumer communication (Carl, 2006). However, the participation of the brand can also play a role. In this way, brand conversation also influences attitudes towards the brand, especially for those who simply view the discussions, rather than participating. Furthermore, these conversations can generate word of mouth, both online and in everyday social contexts:

> "In my opinion, a conversation online leads to another conversation outside. (...) If I hear of something like that, about a review, about a brand, a "maison", a product, well I want to have a look too. It makes you want to see if it's true, if it's wrong." (Marc, 78).

From this we can see that exchanges between brands and consumers have effects comparable to online exchanges between consumers. Indeed, online word of mouth leads to people searching for more information and influences purchasing decisions (Keller, 2007). However, the behavior of brands can change the game, creating a positive impact as discussed above or resulting in negative attitudes towards the brand, which we will now consider.

**Negative effects of conversation.** Conversations are negatively evaluated when the exchanges are not personalized, unclear or are deemed unnecessary and unhelpful. In many cases, a brand's interventions are perceived as intrusive or manipulative. For example, brands can be suspected of using online interaction as a sneaky way to advertise themselves further.

> "I'm not interested (...) If I buy a brand it isn't, well... I don't want... Well I do choose the brand; it isn't the brand that will overwhelm me with ... well, you see. (...) There are too many reminders and all. Promotions, stuff like that. (...) You have those things, notifications you know, things like "do this, do that". And, so you see the... special offers, 50% off, blahblahblah. Sometimes you get two, three times the same thing, you know, almost one just after the other." (Alexis, 42).

In many situations, there is simply no conversation: no one responds to brands or brands do not respond to comments. We found that conversational practices can also generate negative effects, specifically degrading the brand image. However, rather than its personification, it is the value of the conversation that is decisive.

## 4.3 Four perceptions of conversational brands

Table 1 shows four situations, highlighted by the analysis of the interviews, depending on the nature of the brand speaker and the attitude of web-users towards brand conversation. This is, to our knowledge, the first time that two of these situations have been described in
<table><caption>Table 1: Perception of brands depending on speaker and attitude toward conversation</caption><thead><tr><th rowspan="2">Attitude towards conversation</th><th colspan="2">Brand Speaker</th></tr><tr><th>a brand</th><th>a spokesperson</th></tr></thead><tbody><tr><td>Positive</td><td>(1) Humanization</td><td>(3) Incarnation</td></tr><tr><td>Negative</td><td>(2) Standardization</td><td>(4) Bureaucratization</td></tr></tbody></table>

literature: a brand that is humanized due to the positive evaluation of its conversation and a spokesperson that is ‘bureaucratized’ because of the negative evaluation of the conversation she or he participates in.

(1) *The humanized brand*: When “the brand talks”, it can make the brand seem ‘more human’ so long as the conversation appears to be human-driven. A non-personification of the speaker, represented in the form of a logo, still offers human qualities if its messages are personalized and oriented towards consumer satisfaction. It is, therefore, not necessary that an individual is represented for the brand to appear more human.

(2) *The standardized brand*: If the brand conversation is not personalized, one might think that a machine is tweeting or that the speaker does not wish to engage in a meaningful exchange. Just having a presence on Twitter, the simple use of the same tools as members of the public online is not enough to make a brand more personable.

(3) *The incarnated brand*: When a brand is represented by a specific speaker and that this speaker’s conversation is deemed appropriate, this can lead to brand humanization as it creates the sense that there is a ‘real discussion’. The human nature of the brand representative is clear and the users appreciate it. This strategy is similar to well-known personification techniques.

(4) *Bureaucratization*: Surprisingly, users may feel that a well-identified speaker is not animated by human feelings, or even is not human at all. When messages appear to be standardized and the conversation is not successful, it can lead to web users assuming that it is not, in fact, a brand representative responding, but an automated system. We found a paradoxical situation that implies that brand personification cannot always be achieved with the physical representation or name of a human being. To look more human, the brand must also portray human behavior. These findings can contribute to brand personification literature in that they go beyond the mere presence of a speaker and consider the value of its discourse. One interpretation of these results could be that success depends on the quality of brand conversation.

## 4.4 A quality conversation

The positive effects of a brand conversation can be explained by its perceived quality. An in-depth analysis of the interviews showed that the three pillars of quality brand conversation are (1) context, (2) content and (3) form. (1) It is most beneficial that conversations have a limited number of participants, so that they are easy to understand and follow. In a
quality conversation, participants are strongly involved and pay close attention to others. The conversation could have a utilitarian aspect, for example where a participants technical problem is solved either by the brand itself or by other Internet users. In this case, what the user needs is a rapid, relevant and useful response leading to a clear result and even if this is sometimes unexpected, it is always appreciated. However, whether to gain information or provide it, contributors seem to expect nothing in return. Thus, the value of the conversation lies in the quality of the interpersonal relationships that it creates. However, value can also be influenced by content. (2) Quality conversation must be, above all, interesting. Being able to learn something about the brand or from other users shows the intrinsic value of the conversation. Interviewees stated that they sometimes like to consult these interactions, commenting on their playful and hedonistic character. These results confirm that people react better to social media marketing operations when they are motivated to search for information and when they are entertained (Campbell, Ferraro & Sands, 2014). (3) The form of the conversation appears to play an equally important role in its value; the richness and precision of the exchange and the attention paid to the contents of the messages are particularly appreciated as is the inclusion of humor:

> "I like to joke, playing with words and all... Yes, yes... In fact it is the... These conversations that attract me, that interest me, often things that are a bit funny, with a humorous side, or wordplays, you see. It makes me aware of... Otherwise if it's flat... No it's the humoristic side that attracts my attention... Yes, if it's fun, then you remember it better." (Denis, 68).

To summarize, a quality conversation involves a small number of highly involved sincere and empathetic interlocutors. Apparently selfless, conversation is appreciated when it is interesting, useful or entertaining. Relevant and clear answers help to track the conversation until its conclusion. These results complement the existing methods used to measure the quality of the conversation by manually coding its relevance, timeliness, duration and frequency of messages (Adjei, Noble & Noble, 2010). Our analysis highlights the perceived quality of the conversation from a consumer point of view with three dimensions: context, content and form. It seems important for brand managers to take account of what makes a quality conversation on Twitter. We noticed that, when exposed to a conversation that they evaluate positively, consumers consider the brand to be more human, whether this is “a logo speaking” or whether a spokesperson is representing the brand. We also saw that brand conversation can have a positive effect on consumers, especially in terms of attitude towards the brand as well as the publicity gained by word of mouth. As one would expect, a consumer if much more likely to have a positive view of a conversation if they have enjoyed observing or participating in it. Nevertheless, our analyses help to better understand what makes a quality conversation. Furthermore, instead of linking positive effects to the valence or the number of comments, this research links them to the perceived quality of these interactions between brand and consumers.
# 5 Discussion and conclusion

In this research we have identified the concept of quality brand conversation, a notion that has not, to our knowledge, been previously identified. On the one hand, we have identified situations where the anthropomorphism of a brand has positive effects and a brand appears human through the use of quality conversation; on the other hand, we have discussed situations in which personification has negative effects, such as when human representatives partake in dehumanized conversation. Some of the situations described confirm theories suggesting that consumers attribute personality traits to brands (Aaker, 1997). However, we can neither confirm that a personality exists before an interaction with the brand, nor that this interaction guarantees that people think the brand has a personality. It is the quality of the conversation that helps to create anthropomorphism, even without the identification of a human speaker.

From a managerial point of view, these results could enable practitioners to rethink their brand presence on social media sites such as Twitter on three levels. (1) Users do not always want to have a conversation with brands. Therefore, brands should not necessarily be interested in developing interactions in which the sole purpose would be to increase the visibility of messages as this approach could harm their image. (2) However, brand conversation is appreciated if it brings value to users. The establishment of true customer relationship services on Twitter can, for example, helps to build consumer loyalty. (3) Brands can also benefit from developing conversational strategies to improve the quality of the conversation itself by running initiatives such as co-creation platforms. A quality conversation can generate useful ideas for the brand while improving its image. The measurement of the quality of the conversation - so far addressed through external indicators - could be the subject of further studies involving consumers directly.

To conclude, this research shows that although the human representation of the speaker may sometimes be seen as more credible, what consumers appreciate above all is brands that contribute to quality conversations, whether they are personified or not.
Bibliography

[1] Aaker, J., & Fournier, S. (1995). A brand as a character, a partner and a person: three perspectives on the question of brand personality, *Advances in Consumer Research*, 22, 1, 391-395.

[2] Aaker, J. (1997). Dimensions of brand personality, *Journal of Marketing Research*, 34, 347.

[3] Adjei, M., Noble, S., & Noble, C. (2010). The influence of C2C communications in online brand communities on customer purchase behavior, *Journal of the Academy of Marketing Science*, 38, 5, 634-653.

[4] Avis, M. (2012). Brand personality factor based models: A critical review, *Australasian Marketing Journal*, 20, 1, 89-96.

[5] Avis, M. (2013). *Humanlike brands and metaphor: applications and consequences*, Thesis, University of Otago.

[6] Bardin, L. (1977). *L'analyse de contenu*, Paris, Presses universitaires de France.

[7] Campbell, C., Ferraro, C., & Sands, S. (2014). Segmenting consumer reactions to social network marketing, *European Journal of Marketing*, 48, 3, 432-452.

[8] Carl, W. J. (2006). What's all the buzz about? Everyday communication and the relational basis of word-of-mouth and buzz marketing practices, *Management Communication Quarterly*, 19, 4, 601-634.

[9] Cohen, R. J. (2014). Brand personification: introduction and overview. *Psychology & Marketing*, 31, 1, 1-30.

[10] Cova, B., & Cova, V. (2009). Les figures du nouveau consommateur : une genèse de la gouvernementalité du consommateur, *Recherche et applications en marketing*, 24, 3, 81-100.

[11] De Vries, L., Gensler, S., & Leeflang, P. S. H. (2012). Popularity of brand posts on brand fan pages: an investigation of the effects of social media marketing. *Journal of Interactive Marketing*, 26, 2, 83-91

[12] Epley, N., Waytz, A., & Cacioppo, J. (2007). On seeing human: a three-factor theory of anthropomorphism. *Psychological Review*, 114, 4, 864-886.

[13] Eskine, K. J., & Locander, W. H. (2014). A name you can trust? Personification effects are influenced by beliefs about company values, *Psychology & Marketing*, 31, 48-53.

[14] Fielding, G. (2003). Taking conversation seriously: The role of the call centre in the organisation's customer contact strategy, *Interactive Marketing*, 4, 3, 257-266.

[15] Fleck, N., Michel, G., & Zeitoun, V. (2014). Brand personification through the use of spokespeople: an exploratory study of ordinary employees, CEOs and celebrities featured in advertising, *Psychology & Marketing*, 31, 84-92.

[16] Fournier, S. (1998). Consumers and their brands: developing relationship theory in consumer research, *Journal of Consumer Research*, 24, 4, 343-353.

[17] Fournier, S., & Avery, J. (2011). The uninvited brand, *Business Horizons*, 54, 3, 193-207.

[18] Frisch, F. (1999). *Les études qualitatives*, Paris, Editions d'organisation.
[19] Gensler, S., & Völckner, F. (2013). Managing brands in the social media environment, *Journal of Interactive Marketing*, 27, 4, 242-256.

[20] Glaser, B.G., & Strauss, A.L. (1967). The Discovery of Grounded Theory. *Strategies for Qualitative Research*, Chicago, Aldine.

[21] Gretry, A., Horváth, C., van Riel, A., & Belei, N. (2014). "Don't you pretend to be my friend!" - The effect of brands' communication style on brand trust in brand-based online communities, working paper, Radboud University, Nijmegen School of Management.

[22] Hede, A.-M., & Watne, T. (2013). Leveraging the human side of the brand using a sense of place: case studies of craft breweries, *Journal of Marketing Management*, 29, 1-2, 207-224.

[23] Keller, E. (2007). Unleashing the power of word of mouth: creating brand advocacy to drive growth, *Journal of Advertising Research*, 47, 4, 448-452.

[24] Mathwick, C. (2002). Understanding the online consumer: A typology of online relational norms and behavior. *Journal of Interactive Marketing*, 16, 1, 40–55.

[25] Michel, G. (2013). *Management transversal de la marque: Une exploration au cœur des marques*, Paris, Dunod.

[26] Payne, C. R., Hyman, M. R., Niculescu, M., & Huhmann, B. (2013). Anthropomorphic responses to new-to-market logos, *Journal of Marketing Management*, 29, 1-2, 122-140.

[27] Rauschnabel, P. A., & Ahuvia, A. C. (2014). You're So Loveable: Anthropomorphism and Brand Love. *Journal of Brand Management*, 21(5), 1–39.

[28] Semprini, A. (1992). *Le marketing de la marque*, Paris, Editions Liaisons.

[29] Tissier-Desbordes, E., & Vernette, E. (2012). Le repérage marketing des influenceurs dans les réseaux sociaux : des dangers de l'ignorance aux risques de l'à peu près, *Décisions Marketing*, 67, 5-7.