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HICSS Social Media Research Workshop

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Presentation @ Social Media Research Workshop:
Tools and Methods / Results from Analyses of Social Media Data

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HICSS Social Media Research Workshop

  1. 1. Anatoliy Gruzd @gruzd gruzd@ryerson.ca Associate Professor, Ted Rogers School of Management Director, Social Media Lab Ryerson University January 5, 2015 HICSS48 Presentation @ Social Media Research Workshop: Tools and Methods / Results from Analyses of Social Media Data Slides available at http://slideshare.net/primath
  2. 2. Social Media Lab @ Ryerson University SocialMediaLab.ca
  3. 3. Anatoliy Gruzd 3 2015 Social Media & Society Conference http://SocialMediaAndSociety.com
  4. 4. Netlytic.org - a web-based tool for automated text analysis & discovery of social networks from online conversations in social media Networks Stats Content Anatoliy Gruzd Twitter: @gruzd 6
  5. 5. Growth of Social Media and Social Networks Data Facebook 1B users Twitter 500M usersSocial Media have become an integral part of our daily lives!
  6. 6. Studying Online Social Networks http://www.visualcomplexity.com/vc • Forum networks • Blog networks • Friends’ networks (Facebook, Twitter, Google+, etc…) • Networks of like-minded people (YouTube, Flickr, etc…)
  7. 7. How to Make Sense of Social Big Data? Anatoliy Gruzd Twitter: @gruzd 9
  8. 8. Social Big Data -> Visualizations -> Understanding (Development, Application & Validation) How to Make Sense of Social Big Data? Anatoliy Gruzd Twitter: @gruzd 10
  9. 9. How to Make Sense of Social Big Data? Social Network Analysis (SNA) • Nodes = People • Edges /Ties (lines) = Relations/ “Who retweeted/ replied/ mentioned whom” Anatoliy Gruzd Twitter: @gruzd 11
  10. 10. • Reduce the large quantity of data into a more concise representation • Makes it much easier to understand what is going on in a group Advantages of Social Network Analysis Once the network is discovered, we can find out: • How do people interact with each other, • Who are the most/least active members of a group, • Who is influential in a group, • Who is susceptible to being influenced, etc… Anatoliy Gruzd Twitter: @gruzd 12
  11. 11. Common approach for collecting social network data: • Self-reported social network data may not be available/accurate • Surveys or interviews 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 How Do We Collect Information About Online Social Networks? Anatoliy Gruzd Twitter: @gruzd 13
  12. 12. • Common approach: surveys or interviews • A sample question about students’ perceived social structures How Do We Collect Information About Social Networks? 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. [1] [2] [3] [4] [5] Source: C. Haythornthwaite, 1999 Anatoliy Gruzd Twitter: @gruzd 14
  13. 13. How Do We Collect Information About Online Social Networks? Anatoliy Gruzd Twitter: @gruzd 15
  14. 14. Example: Twitter Network @John @Peter @Paul • Nodes = People • Ties = “Who retweeted/ replied/mentioned whom” • Tie strength = The number of retweets, replies or mentions How to Make Sense of Social Media Data? Anatoliy Gruzd Twitter: @gruzd 16
  15. 15. Anatoliy GruzdTwitter: @dalprof 2012 Olympics in London
  16. 16. Anatoliy GruzdTwitter: @dalprof #tarsandTwitter Community
  17. 17. Anatoliy GruzdTwitter: @dalprof #1b1tTwitter Book Club Gruzd, A., and Sedo, D.R. (2012) #1b1t: Investigating Reading Practices at the Turn of the Twenty-first Century.Journal of Studies in Book Culture, Special issue on New Studies in the History of Reading 3(2). DOI: 10.7202/1009347ar
  18. 18. Netlytic.org - a web-based tool for automated text analysis & discovery of social networks from online conversations in social media Networks Stats Content Anatoliy Gruzd Twitter: @gruzd 21
  19. 19. SampleProject:Health Social Media & Health Gruzd, A. & Haythornthwaite, C. (2013). Enabling Community through Social Media. Journal of Medical Internet Research 15(10):e248. DOI: 10.2196/jmir.2796
  20. 20. Background • #hcsmca is a vibrant community of people interested in exploring social innovation in health care. • #hcsmca hosts a tweet chat every Wednesday at 1 pm ET. The last Wednesday of the month is our monthly evening chat at 9 pm ET. Source: http://cyhealthcommunications.wordpress.com/hcsmca-2/ Health Care Social Media Canada Case Study: #hcsmca Twitter Community SampleProject:Health Anatoliy Gruzd Twitter: @gruzd 23
  21. 21. Health Care Social Media Canada Case Study: #hcsmca Twitter Community Research question: 1. What accounts for the relative longevity of this particular online community? – Is it because of the founder’s leadership and her continuing involvement in this community? – Or is there a core group of members who are also actively and persistently involved in this community? 2. What is the composition of this community? Does one’s professional role/title determine a person’s centrality within this community. SampleProject:Health Anatoliy Gruzd Twitter: @gruzd 24
  22. 22. Anatoliy Gruzd Twitter: @gruzd 25 Data Collection SampleProject:Health Data: Public Twitter messages that mentioned the #hcsmca hashtag/keyword Collection Period: November 12 – December 13, 2012 Software: Netlytic http://netlytic.org
  23. 23. Some Topics Discussed by the #hcsmca Community Nov 14, 2012 Challenge of engaging SM to inform a research agenda SampleProject:Health Number of Messages Over Time Nov 21, 2012 Are healthcare blogs a useful tool for education and knowledge transfer? Anatoliy Gruzd Twitter: @gruzd 26
  24. 24. Automated Discovery of Communication Networks on Twitter @John @Peter @Paul Nodes = People Ties = “Who retweeted/ replied/mentioned whom” Tie strength = The number of retweets, replies or mentions 27 SampleProject:Health
  25. 25. Net viz in Netlytic: http://netlytic.org/gephi/sigma.php?c=0ZnbSm6D23u07bT0&viz=2 28 SampleProject:Health #hcsmca Communication Network on Twitter
  26. 26. #hcsmca Communication Network on Twitter *Roles are assigned manually Roles Count SM health content providers 110 Unaffiliated individual users 89 Communicators - not specifically health related 74 Communicators - Health related 59 Healthcare professionals 50 Health institutions 31 Advocacy 30 Students 16 Educators, professors 13 Researchers 10 Government and health policy makers 4 Node size = In-Degree Centrality SampleProject:Health Anatoliy Gruzd Twitter: @gruzd 29
  27. 27. #hcsmca Communication Network on Twitter Nodes are automatically grouped based on their roles No apparent clustering among people in the same role (notice cross-group ties) Procedure: Analysis of Variance Density Test using UCINET 30 SampleProject:Health Gruzd, A. & Haythornthwaite, C. (2013). Enabling Community through Social Media. Journal of Medical Internet Research 15(10):e248. DOI: 10.2196/jmir.2796
  28. 28. Social Media Research Toolkit maintained by the Social Media Lab http://socialmedialab.ca/?page_id=7801 Anatoliy Gruzd 31 TOOLS
  29. 29. Anatoliy Gruzd @gruzd gruzd@ryerson.ca Associate Professor, Ted Rogers School of Management Director, Social Media Lab Ryerson University January 5, 2015 HICSS48 Presentation @ Social Media Research Workshop: Tools and Methods / Results from Analyses of Social Media Data Slides available at http://slideshare.net/primath

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