Social Network Mining

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1 SSIIM - Seminários de Sistemas Inteligentes, Interacção e Mul8média, MIEIC Social Network Mining Eduarda Mendes Rodrigues Assistant Professor DEI- FEUP, Universidade do Porto hhp://

2 Social Media Landscape

3 Social Media Landscape People the individual is at the center of the social web Social media networks explicit and implicit social 8es interac8on among millions of people User- generated content rich source of collec8ve knowledge diffusion of informa8on and opinions drives social engagement

4 Informa8on Retrieval and Social Media Proper8es of social media Scale: millions of ac8ve users, millions of posts per day Real-.me: breaking news, informa8on novelty Duplicates: informa8on diffusion (re- tweets, cross- posts, etc.) Content quality: spelling, grammar, punctua8on, emo8cons, etc. Social fabric: informa8on credibility, opinion leaders, topic experts Some challenges: relevance and ranking Social vs. non- social content Novelty detec8on Credibility of the informa8on sources

5 Informa8on Credibility Several newspapers picked up the fake photos Wrongly indexed by search engines based on the news stories Led to wider dissemina8on

6 Social Media Mining People interact through social media " and patterns are left behind!"

7 Social Media Mining Can social network analysis enrich the content analysis? user ac8vity sta8s8cs interac8on paherns social network metrics community detec8on visualiza8on Social Network Analysis! Content Analysis! text features topic analysis clustering and classifica8on informa8on extrac8on Can the content analysis help explain the social network structure and dynamics?

8 Current Research Data mining and IR in social media social network mining text classifica8on, opinion mining micro- blog search Network visualiza8on layout and clustering algorithms design of interac8ve tools Data journalism informa8on extrac8on from news real- 8me social media analy8cs Social compu8ng applica8ons

9 Social Media Networks Explicit social.es Friends on Facebook Followers on TwiHer Professional contacts on LinkedIn... Implicit social.es Like, favorite, repin Reply, retweet, share Comment, review Tag, rate, vote...

10 Implicit Networks for Social Media Mining Discussion groups (usenet newsgroups) Can we iden.fy posts with answers in Q&A groups? Can we predict agreement and disagreement in debate groups? Community Q&A What type of ques.ons are posted? Can we infer user intent when pos8ng a ques8on?

11 Discussion Group Communi8es Discussion groups are extremely valuable sources of informa8on Iden8fying the polarity of people s opinions about certain topics is useful for business intelligence People seeking informa8on through newsgroup search may want to be pointed at answers to their ques8ons

12 Implicit Networks in Discussion Groups discussion thread" thread structure" social network graph" replies-to! w=2!

13 Mining PaHerns of Social Interac8on Author Networks Reply-to Network: connects authors who reply to other authors Thread Participation Network: connects authors who coparticipate in threads Text Similarity Network: connects authors of similar content Thread Networks Common Authors Network: connects threads that have common authors Text Similarity Network: connects threads of similar content Feature Sets Supervised Learning (Linear SVM) Message Categories Agreement, Disagreement, Insult Ques8on, Answer B. Fortuna, E. Mendes Rodrigues, N. Milic-Frayling. Improving the Classification of Newsgroup Messages through Social Network analysis. ACM 16th Intl. Conf. on Information and Knowledge Management, CIKM 2007 (PDF).

14 Mining PaHerns of Social Interac8on Debaters Topic Experts Reply-to network at distance 2 for the most prolific authors of talk.politics.guns (LEFT) and microsoft.public.internetexplorer.general (RIGHT) newsgroups.

15 Analysis of CQA Communi8es Community Question-Answering (CQA)! 2010" question" 2006" 2006" answers" 2005" 2003" 2002" 2002" CQA services aim build a large knowledge base of ques.ons and answers, on any topic, and make it available through search Challenge: content quality!

16 User Intent & Ques8on Types Is the community sharing knowledge? Or socializing? Mendes Rodrigues, E., Milic-Frayling, N., Sharing Knowledge or socializing? Characterizing User Intent in Community Question Answering, Proceedings of the 2009 ACM International Conference on Information and Knowledge Management, CIKM 09.

17 Mining Ques8on Types Automa8c classifica8on problem Social vs. Non- social ques.ons Feature sets Ques.on features Content (c.idf scores for single terms and n- grams), message length Thread features Responsiveness, user par8cipa8on, presence of URLs in answers Tags and topic features Aggregate informa8on about specificity of tag or topic Social network features for users involved in the thread Clustering coefficient, degree

18 Social Network Structure Community ecosystem evolved in such a way that encouraged interac8ons of a social nature 84.5% of ques8on are non- social and 6.5% are social Over 8me, the percentage of social ques8ons and respec8ve answers and comments increased significantly How social are individual users? Social score: S(u) = social / non- social S(u) > 1 most contribu8ons are with a social intent

19 Social Network Structure Users with high degree post a large percentage of social ques8ons Users who answer and comment on social threads have dense in- neighborhoods

20 Social Network Analysis Mapping and measurement of rela8onships and flows between en88es that include people Views social rela8onships in terms of network theory consis8ng of nodes and links node: actor on which rela8onships act link: rela8onship connec8ng nodes Social network graph

21 Social Network Analysis Social network graphs can be analysed using a number of metrics including: cohesion of the network or sub- network measures the ease with which connec/ons can be made density of the network or sub- network measures the robustness of the connec/ons centrality of the nodes gives a rough indica/on of the social power of a node in the network - degree - betweenness - closenness Social network graph

22 Degree Centrality Count of the number of links to other nodes in the network Higher degree of a node might indicate that the node is a hub in the network David Ramos / GeHy Images Most connected does not mean most powerful!

23 Betweeness Centrality Number of shortest paths between each node pair that a node is on Boundary spanners that bridge between groups have high betweeness John Lund High betweenness generally indicates a powerful posi8on in the network!

24 Closeness Centrality Mean shortest path between a node and all other nodes in the network reachable from it Reflects the ability of a node in accessing informa8on through the network Will Ockenden Low closeness generally indicates high visibility of what s going on in the network!

25 Centrality Mesures and Node Roles Peripheral below average centrality (C) Central connector above average centrality (D) Broker above average betweenness (E) Social network graph

26 Visual Signatures of Social Roles Answerer Connector Originator Outward links to local isolates Rela8ve absence of triangles Few intense links Links from local isolates oren inward only Dense, many triangles Numerous intense links Links from local isolates oren inward only Sparse, few triangles Few intense links Welser, H., Smith, M., Gleave, E. and Fisher, D. Visualizing the Signatures of Social Roles in Online Discussion Groups. Journal of Social Structure, vol. 8, 2007.

27 Network Visualiza8on Visualiza8on should support knowledge discovery and communica8on

28 Ideally How good is a network visualiza8on? Every node is visible The degree of every node can be counted It is possible to follow every link from source to des8na8on Clusters and outliers are iden8fiable NetViz Nirvana!!! C. Dunne and B. Shneiderman, Improving graph drawing readability by incorpora8ng readability metrics: A sorware tool for network analysts, University of Maryland, HCIL Tech Report HCIL , May 2009.

29 How good is a network visualiza8on? Challenge: real networks are oren very complex structures. Standard layout algorithms don t help much when the size of the network is above a few hundred nodes and the network is rela8vely dense in the number of links. Edges crossings and node occlusions! Interpreta8on of the network structure oren requires visualizing addi8onal informa8on about the nodes and links.

30 Some Visualiza8on Approaches Overview of the network Zoom and details on demand Dynamically filter nodes and links Integrate metrics and visualiza8on Layout through seman8c substrates

31 Network Analysis and Visualiza8on Process Model Define Analysis Goals Adjust visual proper8es Collect Interpret Network Data Data Choose network layout Interpret Data Apply data filters D. L. Hansen, D. Rotman, E. M. Bonsignore, N. Milic- Frayling, E. Mendes Rodrigues, M. Smith, and B. Shneiderman, Do you know the way to SNA?: A process model for analyzing and visualizing social media data. in University of Maryland Tech Report: HCIL

32 Network Analysis and Visualiza8on Process Model Collect Network Data Adjust visual proper8es Interpret Data Choose network layout Apply data filters D. L. Hansen, D. Rotman, E. M. Bonsignore, N. Milic- Frayling, E. Mendes Rodrigues, M. Smith, and B. Shneiderman, Do you know the way to SNA?: A process model for analyzing and visualizing social media data. in University of Maryland Tech Report: HCIL

33 Network Analysis and Visualiza8on Process Model Discovery may trigger further analyses Define Analysis Goals Collect Network Data Interpret Data Refining / adjus8ng goals arer the first look at the data Analysis may require addi8onal data D. L. Hansen, D. Rotman, E. M. Bonsignore, N. Milic- Frayling, E. Mendes Rodrigues, M. Smith, and B. Shneiderman, Do you know the way to SNA?: A process model for analyzing and visualizing social media data. in University of Maryland Tech Report: HCIL

34 Flickr Related Tags Network Mouse

35 Flickr Related Tags Network Mouse Computer Mickey Animal

36 US Senators Vo8ng PaHerns

37 US Senators Vo8ng PaHerns

38 US Senators Vo8ng PaHerns

39 US Senators Vo8ng PaHerns

40 US Senators Vo8ng PaHerns

41 US Senators Vo8ng PaHerns

42 GitHub Data Challenge rd Prize

43 NodeXL Project TEAM Connected Action: Marc Smith Microsoft Research: Natasa Milic-Frayling, Tony Capone University of Porto: Eduarda Mendes Rodrigues University of Maryland: Ben Shneiderman, Cody Dunne University of Stanford: Jure Leskovec University of Washington: Eric Gleave Cornell University: Vladimir Barash Social Network Analysis add- in for MS Excel makes graph theory as easy as a bar chart, integrated analysis of social media sources. Open source project at: hhp://nodexl.codeplex.com

44 REACTION Project Computa.onal journalism Intensive use of sorware tools for news research, produc8on and presenta8on Retrieval, Extrac/on and Aggrega/on Compu/ng Technology for Integra/ng and Organizing News What is the impact in the rou.nes of newsrooms? What effect will these tools have on the quality of news and the produc.vity of journalists? hhp://dmir.inesc- id.pt/project/reac8on

45 Data Journalism Implicit News Networks Informa8on extrac8on from thousands of online news ar8cles Pedro Passos Coelho,407,128 Silvio Berlusconi,271,106 Aníbal Cavaco Silva,234,98 'Paulo Bento' e 'Cris8ano Ronaldo' co- ocorreram em 72 no cias 'Paulo Bento' e 'Bruno Alves' co- ocorreram em 39 no cias 'Paulo Bento' e 'Raul Meireles' co- ocorreram em 37 no cias SAPO Labs developed NLP technology for Named En8ty Recogni8on in news (Verbetes service) Rela8onship extrac8on based on co- occurrence

46 Data Journalism Implicit News Networks News social networks Named en8ty extrac8on En8ty co- occurrences Interac8ve visualiza8on Applica8ons Inves8ga8ve journalism Review of the week User engagement

47 Data Journalism Opinion Mining Real- 8me opinion trends about poli8cal candidates

48 Data Journalism Opinion Mining Opinion Mining Module Dic8onary of names Sen8ment lexicon Query TwiHerEcho Rule- based classifier Stats Aggregator DB TwiHerEcho Crawler

49 Data Journalism - TwiHeuro Real- 8me social media monitoring Big data crawling and analy8cs En8ty extrac8on Interac8ve visualiza8on Journalism applica8ons Event repor8ng (#Euro 2012)

50 Data Journalism Sen8ment Words

51 Project Themes Survey paper on mining social media data for business intelligence (e.g. brand management; targeted adver8sing; new product development) on opinion mining techniques for social media content and applica8ons on community detec8on techniques for implicit social networks Social media visualiza.on widgets visualiza8on for tracking the propaga8on of twiher memes spa8o- temporal visualiza8on of tweets with named en88es

52 Thank you! Ques8ons?

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