case studies - web science (vu) (707.000)kti.tugraz.at/staff/socialcomputing/courses/web... · what...

Case StudiesWeb Science (VU) (707.000)

Elisabeth Lex

KTI, TU Graz

June 6, 2016

Elisabeth Lex (KTI, TU Graz) Case Studies June 6, 2016 1 / 24

Study Structural Features of Online Social Networks

Study of linking patterns and discussion topics of political bloggers1

Goal: measure degree of interaction between liberal and conservativeblogs, uncover differences in structure of the two communities

2004 first presidential elections where blogs played a role, precedingelections, increasing readership

Data set: large set of political blogs ( 1500, balanced set) over theperiod of two months preceding the U.S. Presidential Election of 2004

Community detection (Girvan-Newman method)

1The political blogosphere and the 2004 US election: divided they blog, Adamic,Lada A., and Glance N. , Proceedings of the 3rd international workshop on Linkdiscovery, p.36-43, (2005)


Liberal and conservative blogs

Figure: Community structure of the political blogs. Red for conservative, blue forliberal. Orange links go from liberal to conservative, purple from conservative toliberal

Divided blogosphere: liberals and conservatives link primarily within theircommunities (91%), far fewer cross-links


Findings

Stronger interaction patterns within conservative community(in-between, but also links outside of the community) but fewer posts

Differences in links to mainstream media (e.g., more conservativeblogs linked to Fox News)

Both communities focus on different news, topics, political figures


Online Conversational Practices of Political Parties onTwitter

Study of online conversational practices of German politicians onTwitter during the German federal election 20132

Investigated homophily in follow, retweet and mention Twitternetworks of politicians

Homophily: the tendency of a political party to communicate within partyboundaries

2Lietz, Haiko, Claudia Wagner, Arnim Bleier, and Markus Strohmaier. 2014. ”Whenpoliticians talk: assessing online conversational practices of political parties on Twitter(Best Paper Award).” In Proceedings of the Eighth International AAAI Conference onWeblogs and Social Media (ICWSM2014)



Figure: Following, retweeting, and mentioning networks, colors = party

Structural differences: e.g., Homophily effects stronger in follow andretweet networks, weaker in mention network

Two parties (CDU/CSU and FDP) tightly knit in the follow andretweet networks, which formed previous coalition



Figure: Using sociological constructs to assess political online conversations


Analysing Researchers on Social media

Identification and classification of scientists on Twitter3

Goal: generate Twitter user directory for e.g. computer scientists

Why?

Twitter, due to the speed of retweets, is a good medium forinformation diffusion from which scientists can benefit4

3Asmelash Teka Hadgu and Robert Jaeschke. Identifying and Analyzing Researcherson Twitter. In Proceedings of the 6th Annual ACM Web Science Conferencem 23-30,2014.

4H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or anews media? In Proc. Int. Conf. on World Wide Web, pages 591-600, 2010



Goal of the study by Hadgu and Jaeschke:

Insights into how researchers use TwitterSupport users to find researchers on Twitter who work in specific fieldsInvestigate visibility of scientific articles in the social web: altmetrics

“Scientists”: users being interested in the topics of the targetdiscipline and having similar, Twitter- based features like users thathave published scientific papers



What kind of computer scientists use Twitter - Empirical analysis ofidentified users: age, popularity, influence, and social network

E.g. Who are the most influential researchers on Twitter?


Selected Results

Figure: Researchers ranked by number of researchers who retweet (r), follow (f)and mention (m) them, and number of publications (p) in DBLP


Information Diffusion in Social Media

Information and behaviors propagate over online social networks dueto social exposure (i.e., number of friends of a user that have exposedinformation to her)

Recent study: propagation of hashtags and urls (“meme”) consideringtopical alignment5

A user diffuses or adopts a meme if she writes it in one of her tweets

Consider the topical distributions, i.e., the topicality, of active usersand popular memes

5Przemyslaw Grabowicz, Niloy Ganguly, and Krishna Gummadi. 2015. MicroscopicDescription and Prediction of Information Diffusion in Social Media: Quantifying theImpact of Topical Interests. In Proceedings of the 24th International Conference onWorld Wide Web Companion (WWW ’15 Companion)


Information Diffusion in Social Media


Results

Measure adoption probability as a function of social exposure k andtopical alignment S(user ,meme)

Findings: probability of adoption increases with social exposure, aswell as with the topical alignment between user and meme

Max of adoption probability slightly larger for social exposures thanfor topical alignments

Adoption probability grows much more steadily with topical similarity,reaches plateau only few social exposures


Information Diffusion in Networks of Researchers

Mapping of activities of researchers in academics to activity on socialnetworks6

Link authors of research papers to their twitter activity

Analysis of information flow between different users and/or researchareas

6Pujari, S., Hadgu, A., Lex, E., Jaschke, R. (2015) Social Activity versus AcademicActivity: A Case Study of Computer Scientists on Twitter In Proceedings of the 15thInternational Conference on Knowledge Technologies and Data-Driven Business(i-KNOW 2015)


Dynamics in Online Collaboration Networks

How do individual agents produce complex behaviour without centralcontrol?7

Goal: Study impact of social status on opinion diffusion incollaboration graphs to configure the degree of influence of socialstatus on network dynamicsEmpirical networks with heterogeneous community structure (Reddit,Wikipedia editions)Synthetic networks as benchmarking

7Hasani-Mavriqi, I., Geigl, F., Pujari, S., Lex, E., Helic, D. (2015) Influence of Statuson Consensus Building in Collaboration Networks. In Proceedings of the IEEE/ACMInternational Conference on Advances in Social Networks Analysis and Mining(ASONAM 2015)


A Web Science Approach Towards Recommender Systems- Example

Recommender systems: approach for Web users to cope withinformation overload

Collaborative Filtering (CF): one of the most successful methods

CF aims to recommend resources to user based on digital traces sheleaves behind on the Web

I.e., her interactions with resources and the interactions of othersimilar users


Recommender Systems - Example

Example: personalized resource recommendation - user-basedCollaborative Filtering (CFU)

CFU : resulting resources not ranked

Plus: neglects user-resource dynamics that shape attention andinterpretation

E.g. One single ressource can cause a shift in a user’s attention!

Idea: Combine user-based CF with a model from cognitive psychologythat captures such dynamics8

Goal: better personalize recommendations (rank) and improveaccuracy

8Seitlinger, P., Kowald, D., Kopeinik, S., Hasani-Mavriqi, I., Ley, T., and Lex, E.(2015). Attention Please! A Hybrid Resource Recommender MimickingAttention-Interpretation Dynamics. In Proceedings of 24th International World WideWeb Conference, WWW’15


Results

Evaluation: Social tagging systems (Bibsonomy, Delicious, CiteULike)

Compared approach to several baselines (e.g., Most Popular (MP)User-Based Collaborative Filtering (CFU))

We found: our approach improves CFU on all three datasets


Learning from Human Cognition

How do activation processes in human memory influence whether users inonline systems re-use information?

Goal: Design a decision support algorithm that mimics how peopleaccess information in their long term memory

Exploit cognitive architecture ACT-R from human memory theory topredict reuse probability


Learning from Human Cognition

Results


Example Theses

Social Learning Analytics

Social learning analytics make use of data generated by learners’online activity in order to identify behaviors and patterns within thelearning environment.

Goal of this thesis: collect data about learners and their contextsfrom the Social Web (e.g. StackExchange, Reddit) and to analyzethis data using Social Network Analysis (e.g. using graph-tool)

Focus is on studying direct interactions (e.g. messaging, friending,following) and/or indirect interactions, which happen when learnersleave behind activity traces such as e.g. ratings or tags.

Work will be in the context of the H2020 project AFEL (Analytics forEveryday Learning)9

9http://afel.know-center.tugraz.atElisabeth Lex (KTI, TU Graz) Case Studies June 6, 2016 22 / 24

Example Theses

Opinion Dynamics in Social Networks

The aim of this thesis is to build on our existing work, in which westudied the impact of social status on Opinion Dynamics andconsensus building in Online collaboration networks. Seehttp://www.know-center.tugraz.at/cms/wp-content/

uploads/2015/08/ASONAM_2015_Paper.pdf for details.

Goal: extend our existing analysis to other types of networks.

Our open source framework for Opinion Dynamics can be used(Python, graph-tool) and extended.


http://www.know-center.tugraz.at/cms/wp-content/uploads/2015/08/ASONAM_2015_Paper.pdf

http://www.know-center.tugraz.at/cms/wp-content/uploads/2015/08/ASONAM_2015_Paper.pdf

Example Theses

Implementing a Recommender System Using a Cognitive Model on theWeb

Goal: implement a computational cognitive model that explainsbehavior of users on the Web and to deploy this model forrecommender systems.

Evaluate model on well-known datasets from the research communityand compare it to other state-of-the art approaches.

Algorithm should be implemented in Java or Python


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