7.1.2 Design, application and evaluation of a context management
framework for social awareness

In chapter five, and in (Joly, 2009b), we specified our approach for designing a frame-
work in order to validate our hypothesis:

'a social update shared by person U on a social networking system is relevant to another person X if the current context of X is similar to U's context at the time of sharing.'

Following our requirements (e.g. crowd-sourcing, user control), we designed an interac-
tion model, and a formal tag-based context model based on relevant sources of context
for awareness applications. Then, we formalized four social matching schemes based
on temporal combinations of users’ contexts, and described the implementation of our
context management framework for real-time and batch matching.

In chapter six, we applied our conceptual framework to develop a social awareness
application for improving communication and collaboration between employees of large
enterprises (e.g. a company, a research lab), motivated by a case study. In this applica-
tion, users’ context is inferred from their computer-based activities (e.g. browsed web
pages). As an alternative to traditional content-based keyword extraction, we defined
five additional techniques leveraging crowd-sourced/human annotations for extracting
descriptive tags about web documents: social bookmarking tags (annotations given
by human readers), search query terms (also entered by humans), document meta-
data (provided by the authors of the web pages), completed with semantic content and
resource location analysis (for web pages that have not been annotated by humans).
Then, we described the implementation of the application (relying on a context sniffing
extension for Firefox, a local context aggregation server, and a common filtering server),
and of those underlying techniques, integrated as virtual and social context sensors.

In the second half of this chapter, we evaluated the performance of our social aware-
ness application (demonstrated in the CSCW’2010 conference (Joly et al., 2010)), and
of the underlying context management framework, by running context sensors and so-
cial matching algorithms on experimental data (social updates and web browsing logs)
gathered from volunteers, and asking them to rate the results. This evaluation was
twofold:
* Firstly, as explained in our upcoming publication (Joly et al., to appear), we evaluated the perceived accuracy of contextually-recommended social updates, which average relevance rating was 72%. However, the relevance of social updates with the context in which they were posted, upon which their contextual recommendation relies, was rated 50.3% by their authors. It has been observed that, on Twitter, only 41% of status updates are related to the context of their author at the time of posting. Therefore, we stated that our hypothesis could only be validated if this ratio could be increased. In this regard, we observed that social bookmarking updates naturally imply a higher relevance-to-context ratio.

* Then, we evaluated the efficiency of social sensors (e.g. the Delicious-based SocialBookmarks weighting function), compared to more traditional content-based extraction techniques (implemented in virtual sensors), for extracting highly descriptive contextual information about users, from their web browsing logs. We observed that tags extracted by our social sensor implied more matches in looking for similar contexts, than tags extracted by virtual sensors. This result proves the efficiency of our novel context modeling approach based on crowd-sourced repositories.

## 7.2 Contributions

In this thesis, we presented the following contributions:

* a context-awareness framework based on 'tags', instead of ontologies,

* an underlying multidimensional context model that can enable powerful end-user applications without sacrificing their privacy,

* a set of context sensors that can gather and interpret contextual information from human-generated content of several crowd-sourced repositories, instead of solely relying on physical, content-based and specific software sensors,

* and a social awareness application for large enterprises, in which contextual information is extracted from computer-based activities, in order to create new collaboration and communication opportunities, and maintain them seamlessly.
## 7.3 Findings

This thesis resulted in several findings:

* Contextual information can not only be extracted from physical sensors, humans can also be considered as a ‘social sensor’ as long as they are many to contribute on structured crowd-sourced repositories (e.g. delicious).

* The descriptive tags emerging from social sensors (e.g. delicious) are promising features for social matching.

* Contextual similarity between users is a promising criteria for ranking social updates they produced.

* Users accept to share more private information about their context, as long as they can control and manipulate its contents.

## 7.4 Recommendations and best practices

Throughout our research, we identified some best practices that we recommend to users (and potential users) of social networking systems, creators of those systems, and to researchers.

Firstly, in order to facilitate filtering and recommendation of social updates (which are short, by nature), algorithms need to know whether or not a social update is related to the current context of its author. Several solutions and usages can be adopted in this regard:

* users can maintain separate feeds (e.g. Twitter accounts): one for context-related statuses (e.g. ‘my train is late’), and another one for non-related information (e.g. general news, information sharing and relaying, etc...);

* social networking tools, and particularly microblogging systems, can provide an option for users to precise whether or not their status is contextual, whenever they enter a new status;

* researchers can analyze social updates, to determine their relation with their author's context automatically.
Secondly, we observed that some tagging interfaces (e.g. delicious bookmarking dialog) recommend most popular/frequent tags to a user when he/she is invited to provide tags to a resource that has already been tagged by others. Because it's easier for users to select predefined tags than to write down tags that they could naturally find to describe a resource, such recommendations lead to overweighting tags that are already high-weighted (i.e. because they are general and/or consensual), which implies a reduction of the variety, subjectivity and granularity of folksonomies. In this regard, we propose that:

* users try to enter tags more spontaneously, and avoid as much as possible to select recommended tags;

* tagging systems remove or hide recommended tags, or that they recommend less weighted tags in a more random manner.

Thirdly, we found that social bookmarking systems are a great way for tagging resources. However, we deplore that too few other crowd-sourced sites support tagging. In order for us to be able to leverage tags from more crowd-sourced sites, we propose that:

* users intentionally provide lists of tags (preferably preceded by a 'tags:' prefix), instead of plain text annotations on crowd-sourced sites, whenever possible;

* crowd-sourced sites add new fields for users to enter tags instead of full text annotations.

Finally, we would like to address some feature requests to Twitter, other microblogging services, and third-party service developers that rely on social feeds:

* Microblogging services (including Twitter) provide each user a feed which contains status updates posted by people that he/she decided follow, and the search feature allows one to subscribe to new status updates that conform a query with given criteria (e.g. presence of a keyword/hashtag). We believe that aggregating followed feeds and subscribed searched into one feed would be a nice feature to add.
* The respondents of our survey revealed a need for subject-based filtering of social updates from their followed feeds. Today, it is already possible to subscribe to a keyword/hashtag-based query. However, some social updates do not use common keywords when they post about a same subject. Some semantic query enrichment would be welcome to help those users.

* Additionally, it is common that the same news are shared and relayed by many followed feeds, which increases the number of received updates. Clustering social updates that relate to the same information (e.g. a same article), or that link to the same content, would be an appreciated feature to reduce information overload.

* Furthermore, tag-cloud based visualizations of most popular keywords (e.g. twitscoop) are a great way to watch trending topics, and thus to summarize the activity of social feeds. However, we have not found any service that applies the same visualization for one's followed feeds only.

## 7.5 Opportunities for improvement

In this section, we recall several limitations we have encountered, and opportunities for improvement that we have identified:

* In order to increase the chances of finding higher scored similarities while leveraging several sensors, the dimension of resulting term vectors must be reduced. For example, tags could be clustered by semantic proximity (i.e. merge synonyms, hypernyms and hyponyms into one tag, or hierarchies of tags), or noisy tags could be automatically identified and removed. In chapter 3, we have identified several techniques that could be leveraged in this regard.

* Currently based on computing cosine similarity between every users' context, the complexity of our matching algorithm is to be reduced in order to suit high-scale deployments of our application. We believe that online clustering techniques should be integrated to our framework to classify new contexts, so that the matching process could scale linearly.
* Giving back to users the control of their context imply a need for effective con-textual clouds visualization, browsing and manipulation interfaces, especially on mobile devices.
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# Appendices
<figure><img src="image_70.png" /></figure>
# Appendix A

## Introduction to ontologies and the Semantic Web

In their study, Strassner et al. (2007) define ontologies as ‘*a formal, explicit specification of a shared, machine-readable vocabulary and meanings, in the form of various entities and relationships between them, to describe knowledge about the contents of one or more related subject domains throughout the life cycle of its existence.*’ Semantic technologies, including ontologies and semantic description languages, are quite similar to human thinking and memorization: they allow the definition of concepts and instances (of these concepts) that are related with each other using semantically qualified links. They also allow to develop an inferred knowledge from the reasoning on this knowledge (Gruber, 1993). Applying such approach to information technologies enable machines to understand the actual meaning of data which is formulated using a distributed and evolving vocabulary. That way, ontologies fill the gap between ambiguous/fuzzy human thinking (e.g. in natural languages, a word can have different meanings) and formalized digital data (i.e. stored using specific formats and interpreted by specific applications for a specific purpose).

One of the benefits of using semantic languages is to allow progressive/incremental modeling of a system, reflecting the natural progression of conceptual understanding of domains. Ontologies can ease the communication between heterogeneous entities (i.e. using different languages/protocols) by matching similar portions of the semantic graph of the sender’s knowledge with the recipient’s knowledge.
On the other hand, we would like to prevent the reader to make the naive as-
sumption that semantic technologies are a magic solution to empower machines with
autonomic intelligence. It may seem possible to model our universe as an ontology,
allowing computers to understand the human world, but it is actually impossible. In-
deed, modeling is always relative to a point of view, and integrating ontologies from
experts of several domains would necessarily lead to inconsistencies. There is also a
usual confusion about the so-called "Semantic Web" (Berners-Lee et al., 2001). This
expression does not mean that Internet users will have to deal with semantic languages
to communicate on the web, but it refers to a set of languages and tools that would
allow web resources (i.e. web pages and services) to be described semantically in or-
der to allow seamless processing of knowledge distributed among heterogeneous sites.
Today, with the rise of the ‘Web 2.0’ (O’Reilly, 2005), users are already able to create
‘mash-ups’ relying on several components and data streams hosted on different sites.
However, the next step is possibly to automatize (or, at least, to ease) the development
of such mash-ups, assuming that web data and components are semantically described.
# Appendix B

## Samples of personalized surveys

<figure><img src="image_71.png" /><figcaption>Figure B.1: A social update from a participant's personalized survey -</figcaption></figure>

<figure><img src="image_72.png" /><figcaption>Figure B.2: Another social update from a participant's personalized survey -</figcaption></figure>
B. SAMPLES OF PERSONALIZED SURVEYS

Survey Question n°5

On Fri Dec 11 13:30:00 CET 2009, we generated the following tag cloud to represent your context, based on the web pages you were browsing.

<figure><img src="image_73.png" /><figcaption>Figure B.3: A sample context from a participant's personalized survey -</figcaption></figure>
# Appendix C

## Survey on usage of real-time microblogging
# Twitter (or other microblog) usage poll

With this survey, we intend to gather usage preferences in order to evaluate the impact of microblogging usage on people's attention and productivity. Please select (or specify) the microblogging platform that receives most of your attention all day long, and answer to the following questions, according to your own usage of this platform.

This survey should take you less than 10 minutes to complete.

Thanks a lot for your help! :-)

Adrien Joly

PhD candidate

University of Lyon, France

E-mail: (first name) . (last name) (at) liris.cnrs.fr

* Required

## A) Among microblogging platforms that you use, which one takes most of your attention? *

(please select the most appropriate answer)

○ A1 Twitter

○ A2 Yammer

○ A3 Friendfeed

○ A4 Identi.ca (or other laconi.ca-based)

○ Other:

<figure><img src="image_74.png" /></figure>

## B) What do you use microblogging for? *

(please check all appropriate answers)

☐ B1 For work / professional activity and awareness

☐ B2 For real-time news

☐ B3 For networking (e.g. annoucements, asking for advices...)

☐ B4 For a personal cause, a hobby

☐ B5 For keeping in touch with friends/contacts

☐ B6 Personal improvement

☐ B7 For fun

☐ Other:

<figure><img src="image_75.png" /></figure>

## How many people do you follow on this platform? *

(please select the most appropriate answer)

○ less than 10

○ 10-50

○ 50-100

○ 100-250
○ more than 250

**Do you usually receive real-time notifications of microblogging updates, and how?**

even if you receive Atom/RSS updates, please indicate how you are actually notified of new updates

☐ Mobile notifications (e.g. SMS)

☐ Computer pop-ups / "toaster" notifications (e.g. installed AIR software, or plug-in)

☐ I keep my feed visible on the screen

☐ I keep the number of new unread updates visible on the screen

**How systematically do you read new updates? (using real-time notifications)***

by "reading an update", we mean only "read the tweet", not even opening linked content or retweeting

○ I read all updates as they appear (i.e. when I'm notified)

○ I read most updates as they appear, depending on my ongoing activity and availability

○ I am aware of new updates in real-time, but I ignore them most of the time

○ I am not aware of new updates in real-time / I don't receive notifications

**When reading an update, what is the probability that you consult attached content?***

Attached content can be: a video, a blog post, or a link to any other resource that takes time to consult...

0 1 2 3 4 5 6 7 8 9 10
Never (0%) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Systematic (100%)

**When reading an update, what is the probability that you respond on the microblogging platform?***

A "response" includes: replying (RE), re-tweeting (RT), or tweeting about something related

0 1 2 3 4 5 6 7 8 9 10
Never (0%) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Systematic (100%)

**Please estimate the average usefulness of updates for your current activity, at the time you read them***

0 1 2 3 4 5
Useless (0%) ○ ○ ○ ○ ○ ○ Needed (100%)

**Please estimate the usefulness of updates that you read, for the longer term***

0 1 2 3 4 5
Useless (0%) ○ ○ ○ ○ ○ ○ Needed (100%)

**How are you filtering updates to read?***

(please select the most appropriate answer)
○ No filtering at all! I follow every contribution (I'm using another microblogging platform than Twitter)

○ I simply read updates from the people I follow

○ I mostly use a service to watch the trends (e.g. twitscoop)

○ Yes, I mostly use a service to emphasize the updates that are most relevant to me (e.g. my6sense)

○ Yes, I specified my own filtering rules (e.g.@yahoo pipes filtering mash-up)

**If there was a simple way to filter notifications from microblogging platforms, what criteria would you choose?***

E.g. Twitter natively sorts updates chronologically, filters by people you follow, and allows you to create lists of people. What criteria would you add?

☐ Filter by type of update (news, professional, personal, fun...)

☐ Filter by type of attached content (videos, blog posts, geographical check-ins...)

☐ Filter by subject (hashtags...)

☐ Filter by geographical location (local updates...)

☐ Filter by relevance with current activity

☐ Filter by relevance with future activities (trip plans, goals...)

☐ Filter by relevance with current interests

☐ Other:

<figure><img src="image_76.png" /></figure>

**Link to your microblogging profile**

E.g. Twitter username, or the URL of your profile on the microblogging platform which usage you described in this survey

<figure><img src="image_77.png" /></figure>

**Let's keep in touch!**

If you are interested in this study, or in a novel microblogging filtering service relevant to the needs you described in the survey, please provide your email address

<figure><img src="image_78.png" /></figure>

**Additional details to share?**

If there is something specific about your usage, please let us know

<figure><img src="image_79.png" /></figure>

Submit

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FOLIO ADMINISTRATIF

THESE SOUTENUE DEVANT L'INSTITUT NATIONAL DES SCIENCES APPLIQUEES DE LYON

NOM : JOLY
(avec précision du nom de jeune fille, le cas échéant)

Prénoms : Adrien

TITRE : M.

NATURE : Doctorat

Ecole doctorale : InfoMaths

Spécialité : Informatique

Cote B.I.U. - Lyon : T 50/210/19 / et bis

RESUME :

« A Context Management Framework based on Wisdom of Crowds, for Social Awareness applications »

At a time when social networking sites revolutionize the usages on the Web 2.0, it has become rich, easy, and fun to share private or professional content. Sharing personal information in real-time (such as news, moods, etc...), supports the maintenance of social ties at a high scale. However, the information overload which emerged from the growing quantity of signals exchanged on these services, often in real-time, motivates a regulation of these signals (called "mediated interactions"), in order to reduce the temporal cost for maintaining social networks, and implied interruptions, which have a negative impact on productivity on tasks that require long-lasting attention.

In the frame of this thesis, we have developed a filtering and recommendation system that relies on contextual similarity between users that produce and consume social signals, as relevance criteria. In our approach, contextual information is aggregated and interpreted on users' terminal(s), before being submitted on-demand to a server in the form of a set of weighted tags. In this thesis, we present a broad state of the art on context-awareness, social networks and information retrieval, we propose a formalization of our filtering problem, and we implement and evaluate its application for enterprise social networking.

MOTS-CLES : information retrieval, context-awareness, recommender systems, social networks, information overload

Laboratoire (s) de recherche : LIRIS + Alcatel-Lucent Bell Labs France

Directeur de thèse: Pr. Pierre MARET

Président de jury : Hervé MARTIN - LIG, Laboratoire d'Informatique de Grenoble

Rapporteurs :

- Anind K. DEY - Carnegie Mellon University, PA, USA

- Eddie SOULIER - UTT, Université de Technologie de Troyes

Examineurs :

- Fréderique LAFOREST - INSA de Lyon

- Bernd AMANN - LIP6, Laboratoire d'Informatique de Paris 6

DATE de SOUTENANCE : 14/10/2010

Numéro d'ordre : 2010 ISAL 0081

CLASSE :