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
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. 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. Pleasures in Studying Social Media
4

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. Social Media Data
• Texts
• Images
• Videos
• Mixed formats / Multimedia
• Connections I (friends, followers)
• Connections II (links/URLs)
• Connections/Actions (likes, favs, comments, downloads)

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. #3: Multi-disciplinary environment
• Freedom to explore new approaches
• Multi-method
• Exchange with other disciplines
8

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. Perils? – Challenges!
10

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. 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. Reasons?
• Accessibility
• Legal framework
• Ethical framework
13

14. #2: Comparability?
• Longitudinal studies
• Cross-plattform studies
• Comparisions across countries, populations, topics
• Lack of standards (methods, metadata, benchmark
datasets)
14

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. 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. # 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. 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. 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. #5 Moving target
• Platforms and users change
• „Forgotton Features“
– Favs on Twitter
– Hashtags on Facebook
– …
20

21. Lost context: interfaces, look and feel
21
https://www.flickr.com/photos/jackdorsey/182613360

22. Lost context: interfaces, look and feel
22Facebook on Dec. 25, 2005. Via Internet Archive‘s Wayback Machine

23. Lost context: stories and meaning
#jan25
23

24. #6 Data loss
• Deleted tweets
24

25. 25

26. 26

27. 27
Format supported by
Twitter Terms of services

28. #7: Ethics and Privacy
28
2005 2011

29. 29
EXCITING
Pleasures or Perils?

30. Questions and Feedback
30
katrin.weller@gesis.org
@kwelle
http://katrinweller.net