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Automated Discovery and Visualization of Communication Networks from Social Media

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Automated Discovery and Visualization of Communication Networks from Social Media

  1. 1. Automated Discovery and Visualization of Communication Networks from Social Media Anatoliy Gruzd @dalprof gruzd@dal.ca Associate Professor, School of Information Management Director, Social Media Lab Faculty of Management / Faculty of Computer Science Dalhousie University Colloquium on Compromised Data? New paradigms in social media theory and methods. Toronto, October 29, 2013
  2. 2. Dalhousie University Faculty of Management School of Information Management Social Media Lab
  3. 3. Social Media Lab
  4. 4. SocialMediaLab.ca
  5. 5. New Sage Journal: Big Data & Society • Open Access & Multidisciplinary • Editors • • • • • • • • Evelyn Ruppert (Sociology, Goldsmiths, UK); Paolo Ciuccarelli (Density Design, Milan, IT) Anatoliy Gruzd (School of Information Management, Dalhousie University, CA) Adrian Mackenzie (Sociology, Lancaster, UK) Richard Rogers (Digital Methods Initiative, Amsterdam, NL) Irina Shklovski (Digital Media & Communication Research Group, IT University of Copenhagen, DK) Judith Simon (Institute for Technology Assessment and Systems Analysis, Karlsruhe Institute of Technology, DE) Matt Zook (New Mappings Collaboratory, Geography, Kentucky, US). Anatoliy Gruzd @dalprof
  6. 6. Agenda • Automated Discovery of Communication Networks from Online Data • Sense of Community in Online Communities • Netlytic.org Anatoliy Gruzd @dalprof
  7. 7. Growth of Social Media and Social Networks Data Social Media have become an integral part of our daily lives! Facebook Twitter 1B users 500M users
  8. 8. How to Make Sense of Social Media Data? Anatoliy Gruzd Twitter: @dalprof 8
  9. 9. How to Make Sense of Social Media Data? Social Network Analysis (SNA) • Nodes = Group Members/People • Edges /Ties (lines) = relations / Connections Anatoliy Gruzd Twitter: @dalprof 9
  10. 10. Advantages of Using Social Network Analysis to Analyze Social Media Data • Reduce the large quantity of data into a more concise representation • Makes it much easier to understand what is going on in a group Anatoliy Gruzd Twitter: @dalprof
  11. 11. Why Do We Want To Discover Online Social Networks? •Researchers – Ability to ask and answer deeper questions about the nature and operation of online communities • How and why one online community emerges and another dies? • How people agree on common practices and rules in an online community? • How knowledge and information is shared among group members? Anatoliy Gruzd @dalprof
  12. 12. Anatoliy Gruzd Twitter: @dalprof #1b1t Twitter Book Club
  13. 13. Anatoliy Gruzd Twitter: @dalprof 2012 Olympics in London
  14. 14. Anatoliy Gruzd Twitter: @dalprof #tarsand Twitter Community
  15. 15. How Do We Collect Information About Social Networks? •Common approach: surveys or interviews •A sample question about students’ perceived social structures (based on C. Haythornthwaite’s 1999 LEEP study protocol) Please indicate on a scale from [1] to [5], YOUR FRIENDSHIP RELATIONSHIP WITH EACH STUDENT IN THE CLASS [1] - don’t know this person [2] - just another member of class [3] - a slight friendship [4] - a friend [5] - a close friend Alice D. [1] [2] [3] [4] [5] … Richard S. Anatoliy Gruzd [1] [2] [3] [4] [5] @dalprof 15
  16. 16. How Do We Collect Information About Online Social Networks? Problems with surveys or interviews • • • • • Time-consuming Questions can be too sensitive Answers are subjective or incomplete Participant can forget people and interactions Different people perceive events and relationships differently Anatoliy Gruzd Twitter: @dalprof 19
  17. 17. How Do We Collect Information About Social Networks? Different Types of Online Social Networks •Email networks •Forum networks •Blog networks •Friends’ networks on Facebook, Twitter, etc •Networks of like-minded people on Anatoliy Gruzd @dalprof 20 http://www.visualcomplexity.com/vc
  18. 18. Automated Discovery of Social Networks Emails • Nodes = People Nick • Ties = “Who talks to whom” • Tie strength = The number of messages exchanged between individuals Rick Dick Anatoliy Gruzd @dalprof 21
  19. 19. Automated Discovery of Social Networks “Many to Many” Communication Forum Anatoliy Gruzd Mailing listserv @dalprof Chat Comments 22
  20. 20. Automated Discovery of Social Networks Approach 1: Chain Network (Reply-to) Posting header FROM: Sam PREVIOUS POSTER: Gabriel Content .... .... .... Anatoliy Gruzd 23
  21. 21. Automated Discovery of Social Networks Approach 1: Chain Network (Reply-to) Posting header FROM: Sam PREVIOUS POSTER: Gabriel Content “ Nick, Gina and Gabriel: I apologize for not backing this up with a good source, but I know from reading about this topic that … ” Possible Missing Connections: • Sam -> Nick • Sam -> Gina Anatoliy Gruzd • Nick <-> Gina 24
  22. 22. How Do We Collect Information About Social Networks? Research Question What content-based features of online interactions can help to uncover nodes and ties between group members? Anatoliy Gruzd @dalprof 26
  23. 23. Automated Discovery of Social Networks Approach 2: Name Network This approach looks for personal names in the content of the messages to identify social connections between group members. FROM: Ann “Steve and Natasha, I couldn't wait to see your site. I knew it was going to [be] awesome!” Anatoliy Gruzd @dalprof 27
  24. 24. Automated Discovery of Social Networks Approach 2: Name Network •Main Communicative Functions of Personal Names (Leech, 1999) –getting attention and identifying addressee –maintaining and reinforcing social relationships •Names are “one of the few textual carriers of identity” in discussions on the web (Doherty, 2004) •Their use is crucial for the creation and maintenance of a sense of community (Ubon, 2005) Anatoliy Gruzd @dalprof 28
  25. 25. Automated Discovery of Social Networks Name Network Method: Challenges Kurt Cobain, a lead singer for the rock band Nirvana chris is not a group member Santa Monica Public Library John Dewey, philosopher & educator mark up language Solution: - Name alias resolution Anatoliy Gruzd @dalprof 29
  26. 26. Evaluating Name Networks Example: Youtube comments Name Network Chain Network Chain Network Name Network (less connections) (more connections) Anatoliy Gruzd @dalprof 30
  27. 27. Evaluating Name Networks Results from Online Learners Dataset Dataset Classes 6 School year Spring 2008 No. of students 30 20 10 0 Duration of each class 15 weeks No. of students per class 17 – 29 Data source • Bulletin board messages • Online questionnaire Class #1 Class #3 Class #4 Class #5 Class #6 No. of all postings 2000 1500 1000 500 0 Class #1 Gruzd, A. (2009). Studying Collaborative Learning Using Name Networks.Journal of Education for Library and Information Science 50(4): 243-253. Class #2 Class #2 Class #3 Class #4 Class #5 Class #6 31
  28. 28. Results Name Network Chain Network QAP correlation ~ 0.5 • Name networks provide on average 40% more information about social ties in a group as compared to Chain networks “New” Info 82% 18% An addressee has not posted to the thread An addressee is not the most recent poster 70% Thread-starting posting 30% A subsequent posting in the thread 33
  29. 29. Results • Self-reported networks are almost twice as likely to share the same ties with Name networks than with Chain networks. Name Network Chain Network Self-Reported Network 34
  30. 30. Results • The following social relations were found by the “name network” method Learn ● Collaborative Work ● Help • These social relations are considered by many researchers to be crucial in shared knowledge construction and community building thus, name networks can be useful in the assessment of collaborative learning 35
  31. 31. Agenda • Automated Discovery of Communication Networks from Online Data • Sense of Community in Online Communities • Netlytic.org Anatoliy Gruzd @dalprof
  32. 32. Case Study: Online Communities Among Blog Readers •Can a blog support the development of an online community? •How do we know if a community has emerged among blog readers? Gruzd, A. (2009). Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog. Proceedings of the Internet Research 10.0 Conference, October 7-11, 2009, Milwaukee, WI, USA.
  33. 33. Characteristics of Online Community Virtual Settlement (Jones, 1997) –virtual common-public-place –interactivity –sustained membership Sense of Community (McMillan & Chavis, 1986) –feelings of membership & influence –reinforcement of needs –shared emotional connection Anatoliy Gruzd @dalprof
  34. 34. Comments Posted by Blog Readers Anatoliy Gruzd @dalprof
  35. 35. Changes in Social Networks over Time Anatoliy Gruzd @dalprof
  36. 36. SNA Statistics –the posters became more connected and more of them took a stand in a group Anatoliy Gruzd @dalprof
  37. 37. Agenda • Automated Discovery of Communication Networks from Online Data • Sense of Community in Online Communities • Netlytic.org Anatoliy Gruzd @dalprof
  38. 38. Netlytic Content Stats Anatoliy Gruzd Networks a cloud-based analytic tool for automated text analysis & discovery of social networks from online communication Twitter: @dalprof 80
  39. 39. http://netlytic.org 1) Capture public, online, conversational-type data such as tweets, blog comments, forum postings, and text messages, etc. 2) Find and explore emerging themes of discussions among individuals within your data set, 3) Build and visualize communication networks to discover and explore emerging social connections between individuals.
  40. 40. Twitter search keywords: Compromised Data Anatoliy Gruzd @dalprof
  41. 41. Twitter search keywords: #compdata13 Anatoliy Gruzd @dalprof
  42. 42. Automated Discovery and Visualization of Communication Networks from Social Media Anatoliy Gruzd @dalprof gruzd@dal.ca Associate Professor, School of Information Management Director, Social Media Lab Faculty of Management / Faculty of Computer Science Dalhousie University Colloquium on Compromised Data? New paradigms in social media theory and methods. Toronto, October 29, 2013

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