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Weller pleasures+perils social media

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Presentation at "Strategies for managing social media research data", Feb 12, 2016. Cambridge. http://www.data.cam.ac.uk/events/strategies-managing-social-media-research-data

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Weller pleasures+perils social media

  1. 1. The Pleasures and Perils of Studying Social Media Dr. Katrin Weller GESIS – Leibniz-Institute for the Social Sciences Dept. of Computational Social Science Cologne, Germany E-Mail: katrin.weller@gesis.org ●Twitter: @kwelle ● Web: www.katrinweller.net Slides are available at: http://de.slideshare.net/katrinweller
  2. 2. 2 SERIOUSLY? DO THEY NOT REALIZE THAT 99% OF TWEETS ARE WORTHLESS BABBLE THAT READ SOMETHING LIKE ‘JUST WOKE UP. GOING TO STARBUCKS NOW. GETTING LATTE.’ READER’SCOMMENTFOUNDINTHECOMMENTSECTIONFORGROSS,D.(2010,APRIL14).LIBRARYOFCONGRESSTOARCHIVEYOURTWEETS.CNN.RETRIEVEDFROMHTTP://EDITION.CNN.COM/2010/TECH/04/14/LIBRARY.CONGRESS.TWITTER/, RETRIEVEDNOVEMBER19. PHOTOS:HTTPS://WWW.FLICKR.COM/SEARCH/?TEXT=COFFEE&LICENSE=4%2C5%2C6%2C9%2C10
  3. 3. Social media research output is growing 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 No. of publications (Scopus) (TITLE-ABS-KEY("social media") OR TITLE-ABS-KEY("social web") OR TITLE-ABS-KEY("social software") OR TITLE-ABS-KEY("web 2.0")) AND PUBYEAR > 1999
  4. 4. Pleasures in Studying Social Media 4
  5. 5. #1: New type of data • Researchers value social media as a new type of data • Previously „ephemeral data“ become visible • Immediate – quick reaction to events • Structured • „natural“ data 5 “What I find really interesting is that structure becomes manifest in internet communication. So it’s the first time in history actually that we can, that social structures between people become manifest within a technology. (...) They become visible, they become crawlable, they become analyzable.” Kinder-Kurlanda, Katharina E., and Katrin Weller. 2014. "'I always feel it must be great to be a hacker!': The role of interdisciplinary work in social media research." In Proceedings of the 2014 ACM conference on Web Science, 91-98. New York: ACM.
  6. 6. Social Media Data • Texts • Images • Videos • Mixed formats / Multimedia • Connections I (friends, followers) • Connections II (links/URLs) • Connections/Actions (likes, favs, comments, downloads)
  7. 7. #2: Various research topics • User groups • Events • Audiences • Practices • Information flow • Influence • Opinions and sentiments • Networks • Interactions • Predictions • Language • Culture • Political communication • Activism • Crisis communication/disaster response • E-learning • Health • Brand communication
  8. 8. #3: Multi-disciplinary environment • Freedom to explore new approaches • Multi-method • Exchange with other disciplines 8
  9. 9. Scopus: 2000-today by subject area 10650; 36% 5542; 19% 2384 2288 2151 1535 773 772 65 Computer Science Social Sciences Engineering Medicine Business, Management and Accounting Mathematics Arts and Humanities Decision Sciences Psychology Nursing Economics, Econometrics and Finance Biochemistry, Genetics and Molecular Biology Health Professions Environmental Science Earth and Planetary Sciences Agricultural and Biological Sciences Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Materials Science Multidisciplinary Neuroscience Immunology and Microbiology Chemical Engineering Veterinary Dentistry Chemistry Energy
  10. 10. Perils? – Challenges! 10
  11. 11. #1: Model organisms in social media research? 11 https://en.wikipedia.org/wiki/Model_organism#/media/File:Drosophil a_melanogaster_-_side_(aka).jpg Tufekci, Z. 2014. Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (ICWSM).
  12. 12. Social Media Research 0 100 200 300 400 500 600 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Twitter Facebook YouTube Blogs Wikis Foursquare LinkedIn MySpace Number of publications per year, which mention the respective social media platform‘s name in their title. Scopus Title Search. See:
  13. 13. Reasons? • Accessibility • Legal framework • Ethical framework 13
  14. 14. #2: Comparability? • Longitudinal studies • Cross-plattform studies • Comparisions across countries, populations, topics • Lack of standards (methods, metadata, benchmark datasets) 14
  15. 15. 15 Different methods and types of datasets, examples from popular social science papers Weller, K. (2014). What do we get from Twitter – and what not? A close look at Twitter research in the social sciences. Knowledge Organization. 41(3), 238-248
  16. 16. Example 2008-2013 papers on Twitter and elections: data sources Weller, K. (2014). Twitter und Wahlen: Zwischen 140 Zeichen und Milliarden von Tweets. In: R. Reichert (Ed.), Big Data: Analysen zum digitalen Wandel von Wissen, Macht und Ökonomie (pp. 239-257). Bielefeld: transcript. 16 Data source number No information 11 Collected manually from Twitter website (Copy-Paste / Screenshot) 6 Twitter API (no further information) 8 Twitter Search API 3 Twitter Streaming API 1 Twitter Rest API 1 Twitter API user timeline 1 Own program for accessing Twitter APIs 4 Twitter Gardenhose 1 Official Reseller (Gnip, DataSift) 3 YourTwapperKeeper 3 Other tools (e.g. Topsy) 6 Received from colleagues 1
  17. 17. # 3: Data Access 17 “But you can’t make your data available for others to look at, which means both your study can’t really be replicated and it can’t be tested for review. But also it just means your data can’t be made available for other people to say, Ah you have done this with it, I’ll see what I can do with it, (…) There is no open data.” Weller, Katrin, and Katharina E. Kinder-Kurlanda. 2015. "Uncovering the Challenges in Collection, Sharing and Documentation: The Hidden Data of Social Media Research?." In Standards and Practices in Large-Scale Social Media Research: Papers from the 2015 ICWSM Workshop. Proceedings Ninth International AAAI Conference on Web and Social Media Oxford University, May 26, 2015 – May 29, 2015, 28-37. Ann Arbor, MI: AAAI Press.
  18. 18. Available datasets • From individual researchers/groups (sometimes „black market“). • From conferences: e.g. ICWSM • Archival institutions: e.g. GESIS (doi:10.4232/1.12319) 18
  19. 19. http://www.politico.com/story/2015/07/library-of- congress-twitter-archive-119698.html https://www.insidehighered.com/views/2015/0 6/03/article-difficulties-social-media-research Unvailable datasets
  20. 20. #5 Moving target • Platforms and users change • „Forgotton Features“ – Favs on Twitter – Hashtags on Facebook – … 20
  21. 21. Lost context: interfaces, look and feel 21 https://www.flickr.com/photos/jackdorsey/182613360
  22. 22. Lost context: interfaces, look and feel 22Facebook on Dec. 25, 2005. Via Internet Archive‘s Wayback Machine
  23. 23. Lost context: stories and meaning #jan25 23
  24. 24. #6 Data loss • Deleted tweets 24
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  27. 27. 27 Format supported by Twitter Terms of services
  28. 28. #7: Ethics and Privacy 28 2005 2011
  29. 29. 29 EXCITING Pleasures or Perils?
  30. 30. Questions and Feedback 30 katrin.weller@gesis.org @kwelle http://katrinweller.net

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