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Social Media Research Methods

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Presentation given at advanced course "Uses and effects of social media" (by Merja Mahrt) at Heinrich-Heine-University Düsseldorf. 26.11.2013

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Social Media Research Methods

  1. 1. Social Media Research Methods. New Approaches and Open Challenges Dr. Katrin Weller katrin.weller@gesis.org, @kwelle, http://katrinweller.net Presentation at Advanced Course “Uses and effects of social media” HHU Düsseldorf 26.11.2013
  2. 2. Background
  3. 3. Facebook vs. Twitter Scopus (26.11.2013) (TITLE-ABS-KEY(Twitter) AND PUBYEAR > 2006) (TITLE-ABS-KEY(Facebook) AND PUBYEAR > 2004)
  4. 4. Facebook AND Twitter 4975 2007-2013 Scopus: Facebook Scopus: Twitter scopus: Facebook AND Twitter 1274 4975 4136 1274 4136
  5. 5. Scopus: TITLE-ABS-KEY(facebook), searched on 26.11.2013
  6. 6. Scopus: TITLE-ABS-KEY(twitter) AND PUBYEAR > 2006 searched on 26.11.2013
  7. 7. Researching Social Media?
  8. 8. What is being studied? • • • • • • • • • • User groups Events Audiences Practices Information flow Influence Opinions and sentiments Networks Interactions …
  9. 9. How to study social media? „information disclosure and privacy on Facebook“ „Election prediction with Twitter data“
  10. 10. Social Media Data?
  11. 11. Social Media Data • • • • • • • Texts Images Videos Mixed formats Connections I (friends, followers) Connections II (links/URLs) Connections/Actions (likes, favs, comments, downloads)
  12. 12. Example: Twitter Data
  13. 13. Example: Twitter Data
  14. 14. Data Collection and Access? API
  15. 15. https://dev.twitter.com/console
  16. 16. Research Methods
  17. 17. Approaches • • • • Surveys Experiments Interviews Web ethnography • Content analysis • Network analysis • Linguistic analyses (eg. sentiment analysis)  Rather rarely used in combination
  18. 18. Content Analysis • E.g. with MAXQDA, QDAMiner, ATLAS.ti, Qualrus, Nvivo
  19. 19. Network Analysis • E.g. with R, NodeXL
  20. 20. Sentiment Analysis • Mainly based on dictionaries such as ANEW http://link.springer.com/article/10.1007%2Fs 10902-009-9150-9/fulltext.html
  21. 21. Top 4 Chances
  22. 22. #1 Observing spontaneous interactions and ephemeral communication
  23. 23. #2 Access to data for a variety of contexts and questions
  24. 24. #3 Rich data: multimedia content plus networks plus interactions
  25. 25. #4 Interdisciplinary field, option to experiment with new and combined methods
  26. 26. Top 4 Challenges
  27. 27. #1 Lack of theoretical background
  28. 28. #2 data haves, data have-nots boyd, danah and Kate Crawford. (2012). “Critical Questions for Big Data”
  29. 29. #3 Representativeness?
  30. 30. #4 Missing standards (on different levels)
  31. 31. Exciting work ahead! Katrin Weller @kwelle katrin.weller@gesis.org http://katrinweller.net www.gesis.org
  32. 32. References • • • • • • • Ackland, R. (2013). Web Social Science. Los Angeles et al: SAGE. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication, & Society, 15(5), 662-679. Bruns, A. (2013). Faster than the speed of print: Reconciling ‘big data’ social media analysis and academic scholarship. First Monday 10(18). Available http://firstmonday.org/ojs/index.php/fm/article/view/4879 Giglietto, F., Rossi, L., & Bennato, D. (2012). The Open Laboratory: Limits and Possibilities of Using Facebook, Twitter, and YouTube as a Research Data Source. Journal of Technology in Human Services, 30(3-4), 145–159. Karpf, D. (2012). Social science research methods in internet time. Information, Communication & Society, 5(15), 639-661. Weller, K., Bruns, A., Burgess, J., Mahrt, M., & Puschmann, C. (2014). Twitter and Society. New York et al.: Peter Lang. Williams, S. A., Terras, M. M., Warwick, C. (2013). What do people study when they study Twitter? Classifying Twitter related academic papers. Journal of Documentation, 69(3), 384-410.

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