Il laboratorio aperto: limiti e possibilità dell’uso di Facebook, Twitter e YouTube come sorgente dati

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Intervento di Davide Bennato, Fabio Giglietto, Luca Rossi tenuto durante il convegno "Così vicini, così lontani: la via italiana aia social network" (26-27 Settembre Milano)

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Il laboratorio aperto: limiti e possibilità dell’uso di Facebook, Twitter e YouTube come sorgente dati

  1. 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. 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. 3. methodology Ricolfi 1997
  4. 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. 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. 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. 7. Youtube Harley, Fitzpatrick 2009
  8. 8. Youtube
  9. 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. 10. Youtube Ricke 2010
  11. 11. Youtube Klotz 2010
  12. 12. Youtube Bal 2010
  13. 13. Youtube Bennato 2012
  14. 14. Youtube
  15. 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. 16. Youtube Shra, 2009
  17. 17. Youtube
  18. 18. Youtube Wallsten, 2010
  19. 19. Youtube
  20. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 35. Facebook Facebook StatisticalEthnographic Computational *****
  36. 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. 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. 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. 39. Twitter
  40. 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. 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. 42. Twitter
  43. 43. Twitter
  44. 44. Twitter Twitter StatisticalEthnographic Computational ***** Even for qualitative research data are usually gathered through computational methods.
  45. 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. 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. 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. 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. 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

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