This document discusses different methodologies for conducting research using social media platforms as data sources. It outlines three main approaches: ethnographic, statistical, and computational. For each platform - YouTube, Facebook, and Twitter - it provides examples of common research questions, strategies, and references for each methodology. It emphasizes that the methodology should be driven by the specific research questions being asked.

1. The Open laboratory
PRIN 2009 | Social Network Studies Italia
Limiti e possibilità per l’uso di FaceBook,
Twitter e YouTube come sorgente dati
Davide Bennato, Università di Catania
Fabio Giglietto, Università di Urbino Carlo Bo
Luca Rossi, IT University of Copenhagen

2. methodology
We can define it as a problem solving applied to research
questions
One methodology, many methods (or tecniques)
Sociological research in social media: the
qualitative/quantitative debate is very difficult to apply
Reasons
1. Social media are software objects/texts
2. Difficulties in applying the concept of representativeness
3. Digital texts are performative activities
4. Digital texts are culturally embedded

3. methodology
Ricolfi 1997

4. methodology
Three great models of social research in participative web
1. Ethnography
Interpretative approach
Text as unit of analysis
2. Statistical
Mathematical approach
Quantification/metrics as unit of analysis
3. Computational
Computer science approach
Formal relationship and unit of analysis

5. Youtube
Videosharing platform: different use, different metrics
Audience interaction
1. visualizations (videos)
2. visualizations (channells)
Social interaction
1. comments
2. I like/I don't like
3. friending/subscriptions
Platform interaction
1. metadata (tag, video title, ID video, contributor, date added,
description)

6. Youtube
Ethnographic research
Strategies
Videos as significative object
Use in small communities (e.g. vloggers)
Research characteristics
Small size of videos analized
Great use of different qualitative tecniques (e.g interviews,
audio transcriptions)
Multi-methods/triangulation approaches preferred

7. Youtube
Harley, Fitzpatrick 2009

8. Youtube

9. Youtube
Statistics research
Strategies
Video as traces of a social bevahiour
Video as way to access a community (e.g political candidates)
Research characteristics
Sampling tecniques: the construction of the universe
Content analysis: human or automatic (e.g. Leximancer)
Coding tecniques (Grounded Theory)

10. Youtube
Ricke 2010

11. Youtube
Klotz 2010

12. Youtube
Bal 2010

13. Youtube
Bennato 2012

14. Youtube

15. Youtube
Computational research
Strategies
Any software object express something (about platform or
about users)
Analysis have to consider also platform structural
characteristics
Research characteristics
Big/enormous data collection
Web services approach (e.g. Tubekit, Tubemogul)
Alghoritmic approach (Google API manipulations)
Modelling (e.g. power law, graph structure)

16. Youtube
Shra, 2009

17. Youtube

18. Youtube
Wallsten, 2010

19. Youtube

20. Youtube
Conclusions
1. Different ways to analyze video
2. Different units of relevation
3. Different research strategies
Different questions, one answer: methodology driven by
research questions

21. Facebook
What you can get:
- Graph API
- Apps
- Public Feed API & Keyword Insight API
More than 114 scientific article (Caers et al 2013)

22. Facebook
meta-information/external Graph enabled website
sample RQs:
•Are there inconsistency between media focus and user attention?
•What is the lifespan of a news?
•Can the number of likes received by a movie on IMDb.com be a good predictor of the
movie's box office revenues?
references:
1. Lifshits, Y. & Clara, S. EDISCOPE : SOCIAL ANALYTICS FOR ONLINE NEWS. (2010).
2. Schmeh, J. Rankify – Aggregated News Ranking based on User Engagement in the
Social Web. 63 (2011).
notes: this approach could be complemented by qualitative content analysis

23. Facebook
meta-information/pages and groups meta-information
sample RQs:
•are Italian universities adopting social media to communicate and relate with students
and other strategic publics?
•what is the role of Facebook in the spread of Occupy Wall Street movement?
references:
1. Lovari, A. & Giglietto, F. Social Media and Italian Universities: An Empirical Study on the
Adoption and Use of Facebook, Twitter and Youtube. SSRN eLibrary (Jenuary 2,
2012). Available at SSRN: http://ssrn.com/abstract=1978393 or doi:10.2139/ssrn.1978393
2. Caren, Neal and Gaby, Sarah, Occupy Online: Facebook and the Spread of Occupy
Wall Street (October 24, 2011). Available at SSRN: http://ssrn.com/abstract=1943168 or
doi:10.2139/ssrn.1943168

24. Facebook
meta-information/my friends personal profiles
sample RQs:
•what is the level of students’ online self-disclosures on Facebook?
•what my friend's most liked pages tell about me?
references:
1. Kolek, E.A. & Saunders, D. Online Disclosure: An Empirical Examination of
Undergraduate Facebook Profiles. Journal of Student Affairs Research and Practice 45,
(2008).
2. http://blog.ouseful.info/2012/01/04/social-interest-positioning-visualising-facebook-
friends-likes/

25. Facebook
meta-information/my non-friends profiles with Facebook App
sample RQs:
•what is the level of privacy awareness of Facebook users?
references:
1. Rauber, G. & Almeida, V.A.F. Privacy Albeit Late. Networks 13, 26 (2011).

26. Facebook
meta-information/public pages and groups posts
sample RQs:
•How are nonprofit organizations incorporating relationship development strategies into
their Facebook profiles?
•How do groups focused on McCain versus Obama differ in terms of the frequency of
positive and negative references to candidates, the use of profanity, and references to
race, religion and age?
references:
1. Waters, R.D., Burnett, E., Lamm, A. & Lucas, J. Engaging stakeholders through social
networking: How nonprofit organizations are using Facebook. Public Relations Review 35,
102-106 (2009).
2. Woolley, J.K., Limperos, A.M. & Oliver, M.B. The 2008 Presidential Election, 2.0: A
Content Analysis of User-Generated Political Facebook Groups. Mass Communication
and Society 13, 631-652 (2010).
notes: this kind of study are a reasonable follow up of studies based on the analysis
of pages and groups meta-information

27. Facebook
contents/public pages and groups posts
sample RQs:
•How are nonprofit organizations incorporating relationship development strategies into
their Facebook profiles?
•How do groups focused on McCain versus Obama differ in terms of the frequency of
positive and negative references to candidates, the use of profanity, and references to
race, religion and age?
references:
1. Waters, R.D., Burnett, E., Lamm, A. & Lucas, J. Engaging stakeholders through social
networking: How nonprofit organizations are using Facebook. Public Relations Review 35,
102-106 (2009).
2. Woolley, J.K., Limperos, A.M. & Oliver, M.B. The 2008 Presidential Election, 2.0: A
Content Analysis of User-Generated Political Facebook Groups. Mass Communication
and Society 13, 631-652 (2010).
notes: this kind of study are a reasonable follow up of studies based on the analysis
of pages and groups meta-information

28. Facebook
meta-information/my friends posts
sample RQs:
•To what extent are Facebook users using links to share information with their network
of Facebook “friends”?
references:
1. Baresh, B., Knight, L., Harp, D. & Yaschur, C. Friends who choose your news: an
analysis of content links on Facebook. International Symposium on Online Journalism,
Austin, Texas, April 2011. (2011).

29. Facebook
meta-information/my friends posts
sample RQs:
•To what extent are Facebook users using links to share information with their network
of Facebook “friends”?
references:
1. Baresh, B., Knight, L., Harp, D. & Yaschur, C. Friends who choose your news: an
analysis of content links on Facebook. International Symposium on Online Journalism,
Austin, Texas, April 2011. (2011).
Notes: is such a kind of sample representative of, at last, Facebook users?

30. Facebook
meta-information/my non-friends posts with Facebook App
sample RQs:
•To what extent are Facebook users using links to share information with their network
of Facebook “friends”?
references:
notes: this approach could be attempted in order to create a representative
sample of Facebook users.

31. Facebook
meta-information/whole network collection
sample RQs:
•What is the average number of friends in a bounded group (such as freshman)
•What is the average degree of separation on Facebook or among Italian users?
references:
1.
2. Traud, A. & Mucha, P. Social Structure of Facebook Networks. Arxiv preprint
arXiv:1102.2166 (2011).
notes: the dataset for this studies was provided by Facebook

32. Facebook
meta-information/partial networks collection: groups and ego networks
sample RQs:
•Is there an overlap between pre-existing personal networks and Facebook network?
•Is it possible to identify key local individuals by analysis Facebook network groups
structure?
references:
1. Hogan, Bernie, A Comparison of On and Offline Networks through the Facebook API
(December 18, 2008). Available at SSRN: http://ssrn.com/abstract=1331029 or
doi:10.2139/ssrn.1331029
2. http://larica.uniurb.it/nextmedia/2011/11/urbino-su-facebook/

33. Facebook
meta-information/stream analysis
sample RQs:
•Is there a correlation between number of candidate's mentions on Facebook, post
sentiment and outcomes of the elections?
references:
1. http://www.politico.com/news/stories/0112/71345.html

34. Facebook
meta-information/sampling with Facebook
Facebook could also be used to disseminate a survey. By leveraging on
Facebook advertising platform it could be possible to target the survey to
specific segment of population in order to create representative sample of
Facebook population (structured by gender, age and any other kind of
information available in the platform). Moreover this strategy could complement
the once based on Facebook App. Administering a survey via Facebook App
will enable researchers to get both answers and data (age, gender, likes and
other structural variables).

35. Facebook
Facebook
StatisticalEthnographic Computational
*****

36. Twitter
relevant aspects:
•Network Structure Studies:
friends/follower/hubs etc.
•Users activities:
messages/reTweets/@reply
•Users social practices
•Emergent phenomena:
Elections, Natural disasters,
Crisis communication
•Case studies (Journalism)
What you can get:
- Public stream
- Search API
- Streaming API
- Firehose data

37. Twitter
Researchers largely used
(and still use) Twitter search
or streaming API.
Is the sample good enough?
(Morstatter et al. 2013)
- When the number of tweets
monitored increase the reliability of
streaming solution decrease.
- Streaming API data estimates top n
hashtag when n is large but fails
when n is small.
- Streaming API return almost the
complete set of geotagged tweets

38. Twitter
Twitter research started within the traditional approach of
network studies from a computer science perspective (Java, A.
et al., 2007) and was soon followed by many researches aiming
at giving a general description of the phenomenon (Huberman,
B.A., Romero, D.M. & Wu, F., 2009.). The public-by-default
nature of Twitter led toward a massive adoption of
computational methods: data were simple, textual, and easy
accessible.

39. Twitter

40. Twitter
In few years researchers started to focus on the social
aspects of Twitter based interactions (Marwick, A.E. & boyd,
d., 2010) and on the Twitter based emergent phenomena
(Earle, P., 2010)
< Who do you tweet *to*?> No one & I love
that. Or maybe myself five min. ago: I write
the tweets I want to read.
I don’t tweet to anybody; I just do it to do i

41. Twitter
In 2011 the was a peak of interest in Twitter based research
both in social sciences and in computer sciences on the
following directions:
- Automated analysis / event detection (Hong, L. & Davison,
B.D., 2011 - Welch, M.J. et al., 2011. - Weng, J. & Lee, B.-
sung, 2011)
- Events monitor and analysis (Bruns, A., 2011, Bruns, A. &
Burgess, J., 2011, Rossi, L., Magnani, M. & Iadarola).
- Specific case studies (Lasorsa, D., Lewis, S. & Holton, A.,
2011.)

42. Twitter

43. Twitter

44. Twitter
Twitter
StatisticalEthnographic Computational
*****
Even for qualitative research data are usually gathered
through computational methods.

45. Twitter
Bruns, A., 2011. How long is a tweet? Mapping dynamic conversation networks on Twitter
using gawk and gephi. Information, Communication & SocietySociety, p.37-41.
Bruns, A. & Burgess, J., 2011. #Ausvotes: How twitter covered the 2010 Australian federal
election. Communication, Politics & Culture, 44(2).
Dann, S., 2010. Twitter content classification. First Monday, 15(12), p.1-10.
Earle, P., 2010. Earthquake Twitter. Nature Geoscience, 3(4), p.221-222.
Go, A., Huang, L. & Bhayani, R., 2009. Sentiment Analysis of Twitter Data. Entropy,
2009(June), p.17.
Hong, L. & Davison, B.D., 2011. Predicting Popular Messages in Twitter. ReCALL, p.57-58.
Huberman, B.A., Romero, D.M. & Wu, F., 2009. Social Networks that matter: Twitter under
the microscope. First Monday, 14(1).

46. Twitter
Huberman, B.A., Romero, D.M. & Wu, F., 2009. Social Networks that matter: Twitter under
the microscope. First Monday, 14(1).
Java, A. et al., 2007. Why We Twitter : Understanding Microblogging. Network, 1(ACM
Press), p.56-65.
Lasorsa, D., Lewis, S. & Holton, A., 2011. Normalizing Twitter. Journalism Studies,
(August), p.1-18.
Lassen, D.S. & Brown, a. R., 2010. Twitter: The Electoral Connection? Social Science
Computer Review, 29(4), p.419-436.
Marwick, A.E. & boyd, d., 2010. I Tweet Honestly, I Tweet Passionately: Twitter Users,
Context Collapse, and the Imagined Audience. New Media & Society, 13(1), p.114-133.
Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. M. (2013). Is the sample good enough?
comparing data from twitter’s streaming api with twitter’s firehose. Proceedings of ICWSM.
Rossi, L., Magnani, M. & Iadarola, B., 2011. #rescatemineros: global media events in the
microblogging age. In S. Fragoso et al., eds. Selected Papers of Internet Research.

47. Twitter
Tumasjan, a. et al., 2010. Election Forecasts With Twitter: How 140 Characters Reflect the
Political Landscape. Social Science Computer Review, 29(4), p.402-418.
Welch, M.J. et al., 2011. Topical Semantics of Twitter Links. Time, p.327-336.
Weng, J. & Lee, B.-sung, 2011. Event Detection in Twitter. Event London, p.401-408.
Wohn, D.Y. & Na, E.K., 2011. Tweeting about TV: Sharing television viewing experiences
via social media message streams. First Monday, 3(16).

48. Twitter
What you can get:
•user:
oname
olocation*
olanguage*
ofollowers/friends
olists
•message
otext
otype (message, RT*, reply*)
olocation *
otime
•network structure:
onetwork of followers/friends
onetwork of conversations
onetwork of propagations

49. anobii
Single research (Aiello, L.M. et al., 2012) on link creation:
creation on social ties is strongly driven by:
- homophily and proximity (language, similarity of interests,
geographic proximity).
Data available upon request:
- user's profile
- library information
- groups affiliations