News diffusion ECREA 2012

Transcript

1. Veronika Karnowski, Till Keyling, & Dominik Leiner News diffusion via social media platforms: challenging classical DOI theory? 4th European Communication Conference „Social Media and Global Voices“ October 24-27, 2012 Istanbul, Turkey
2. 2 Social network sites (SNS) and the diffusion of news • The S-shaped curve of diffusion could also be confirmed for news diffusion (Deutschmann & Danielson, 1960; Singhal, Rogers & Mahajan, 1999) • SNS are of ever increasing importance as an information source for their users (Purcell, Rainie, Mitchell, Rosenstiel & Olmstead, 2010) • SNS emphasize the social (sharing, recommending) character of news diffusion (Sim & Fu, 2008, Gil de Zúniga, Jung & Valenzuela, 2012) Karnowski, Keyling & Leiner: News diffusion via social media platforms adoption rate time
3. 3 Why researching news diffusion? • The shape of the news diffusion curve can be considered as an indicator of “information equality” (Sinnreich, Chib & Gilbert, 2008) • Interpersonal communication about news can explain media effects on non- users (Maurer 2004, Krause & Gehrau 2007) • Is of relevance in the discussion of substitution effects and cannibalization (Kolo, 2010) Karnowski, Keyling & Leiner: News diffusion via social media platforms
4. 4 Research questions a) Can we still find the S-shaped curve postulated by diffusions of innovations theory? b) Are there differences regarding the process and patterns of diffusion? c) Are there content-specific emphases for each SNS? Karnowski, Keyling & Leiner: News diffusion via social media platforms
5. 5 • Title • Publication date • Feed update • Thematic category Article-RSS-Parser 10 min Shares-Tracker Facebook* − Likes/Recommends − Shares − Comments Twitter* Google+ * API-Calls Bild.de (10) SPON (12) SZ.de (16) CNN.com(13) FOXNews (11) NYT (11) RSS-Feeds Log. scale Data collection: procedure of the automated diffusion- monitoring Karnowski, Keyling & Leiner: News diffusion via social media platforms
6. 6 Paramters of Diffusion Time Recommendations (Likes, Shares, Comments, Tweets, Plusses) t0 Last Measure (5 days) Total Amount of Recommendation t50 50% Amount Point in time where 50% of recommendations reached Publication Karnowski, Keyling & Leiner: News diffusion via social media platforms
7. 7 An example of a diffusion curve/growth curve • rapid increase in the first 2 hours • S-Curve for Comments found • No S-Curve for Tweets? • instantaneous and concurrent diffusion via mass media Karnowski, Keyling & Leiner: News diffusion via social media platforms Comments
8. 8 Total Amount of Recommendations by Platform Tweets Shares Amount of Tweets Amount of Shares Karnowski, Keyling & Leiner: News diffusion via social media platforms
9. 9 Speed of Diffusion on Twitter by Topic Tweets Karnowski, Keyling & Leiner: News diffusion via social media platforms
10. 10 Keyling, Karnowski & Leiner: Nachrichtendiffusion in der virtuellen MediaPolis Speed of Diffusion on Facebook by Topic Shares
11. 11 Summary and discussion  Promising methodology • The first four hours are crucial in order to estimate the maximal reach • Less time-critical news categories (Service, Feuilleton & Media, Science & Technic) diffuse a lot more slowly • Faster diffusion via Twitter than via Facebook  Might be an indicator for the supposed difference between Twitter as a news source and Facebook as a plattform for the discussion of news  The s-shaped curve of news diffusion can be confirmed for the discussion of news topics (comments), not for the diffusion per se (shares, tweets) Karnowski, Keyling & Leiner: News diffusion via social media platforms
12. 12 Thanks for your attention! Veronika Karnowski, Till Keyling & Dominik Leiner Institut für Kommunikationswissenschaft und Medienforschung LMU München Karnowski, Keyling & Leiner: News diffusion via social media platforms
13. 13 Literatur e Deutschmann, P. & Danielson, W. (1960). Diffusion of a Major News Story. Journalism Quarterly, 37, 345–355. Gil de Zúñiga, H., Jung, N., & Valenzuela, S. (2012). Social Media Use for News and Individuals’ Social Capital, Civic Engagement and Political Participation. Journal of Computer Mediated Communication, 17(3), 319-336. Kolo, C. (2010). Online-Medien und Wandel: Konvergenz, Diffusion, Substitution [Online-media and change: Convergence, diffusion, substitution]. In W. Schweiger & K. Beck (eds.), Handbuch Online-Kommunikation (pp. 283-307). Wiesbaden: VS Verlag. Krause, B. & Gehrau, V. (2007). Das Paradox der Medienwirkung auf Nichtnutzer am Beispiel einer Zeitreihenanalyse auf Tagesbasis zu den kurzfristigen Agenda-Setting Effekten von Fernsehnachrichten. [The paradoxon of media effects on non-users. The example of short-term agenda-setting-effects auf television news on the basis of daily time series analyses.] Publizistik, 57, 191-209. Maurer, M. (2004). Das Paradox der Medienwirkungsforschung. Verändern Massenmedien die Bevölkerungsmeinung, ohne Einzelne zu beeinflussen? [The paradoxon of media effects research. Do mass media change the public opinion without influencing individuals?] Publizistik, 49, 405-422. Purcell, K., Rainie, L., Mitchell, A., Rosenstiel, T. & Olmstead, K. (2010). Understanding the Participatory News Consumer. PEW Report [http://pewinternet.org/Reports/2010/Online-News.aspx]. Sim, C., & Fu, W. W. (2008). Riding the “Hits” Wave: Informational Cascades in Viewership of Online Videos. 2008 Annual Conference of the International Communication Association. Singhal, A., Rogers, E. M. & Mahajan, M. (1999). The Gods Are Drinking Milk! Word-of-Mouth Diffusion of a Major News Event in India. Asian Journal of Communication, 9(1), 86–107. Sinnreich, A., Chib, A. & Gilbert, J. (2008). Modeling Information Equality: Social and Media Latency Effects on Information Diffusion. International Journal of Communication, 2, 132-159. Karnowski, Keyling & Leiner: News diffusion via social media platforms