ABSTRACT
We study several longstanding questions in media communications research, in the context of the microblogging service Twitter, regarding the production, flow, and consumption of information. To do so, we exploit a recently introduced feature of Twitter known as "lists" to distinguish between elite users - by which we mean celebrities, bloggers, and representatives of media outlets and other formal organizations - and ordinary users. Based on this classification, we find a striking concentration of attention on Twitter, in that roughly 50% of URLs consumed are generated by just 20K elite users, where the media produces the most information, but celebrities are the most followed. We also find significant homophily within categories: celebrities listen to celebrities, while bloggers listen to bloggers etc; however, bloggers in general rebroadcast more information than the other categories. Next we re-examine the classical "two-step flow" theory of communications, finding considerable support for it on Twitter. Third, we find that URLs broadcast by different categories of users or containing different types of content exhibit systematically different lifespans. And finally, we examine the attention paid by the different user categories to different news topics.
- E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts. Identifying 'influencers' on twitter. In Fourth ACM International Conference on Web Seach and Data Mining (WSDM), Hong Kong, 2011. ACM.Google Scholar
- W. L. Bennett and S. Iyengar. A new era of minimal effects? the changing foundations of political communication. Journal of Communication, 58(4):707--731, 2008.Google ScholarCross Ref
- M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummad. Measuring user influence on twitter: The million follower fallacy. In 4th Int'l AAAI Conference on Weblogs and Social Media, Washington, DC, 2010.Google Scholar
- J. S. Coleman, E. Katz, and H. Menzel. The diffusion of an innovation among physicians. Sociometry, 20(4):253--270, 1957.Google ScholarCross Ref
- R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences, 105(41):15649, 2008.Google ScholarCross Ref
- T. Gitlin. Media sociology: The dominant paradigm. Theory and Society, 6(2):205--253, 1978.Google ScholarCross Ref
- M. Gomez Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1019--1028. ACM, 2010. Google ScholarDigital Library
- E. Katz. The two-step flow of communication: An up-to-date report on an hypothesis. Public Opinion Quarterly, 21(1):61--78, 1957.Google ScholarCross Ref
- E. Katz and P. F. Lazarsfeld. Personal influence; the part played by people in the flow of mass communications. Free Press, Glencoe, Ill.,, 1955.Google Scholar
- G. Kossinets and D. J. Watts. Empirical analysis of an evolving social network. Science, 311(5757):88--90, 2006.Google ScholarCross Ref
- H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World Wide Web, pages 591--600. ACM, 2010. Google ScholarDigital Library
- H. D. Lasswell. The structure and function of communication in society. In L. Bryson, editor, The Communication of Ideas, pages 117--130. University of Illinois Press, Urbana, IL, 1948.Google Scholar
- P. F. Lazarsfeld, B. Berelson, and H. Gaudet. The people's choice; how the voter makes up his mind in a presidential campaign. Columbia University Press, New York, 3rd edition, 1968.Google Scholar
- R. K. Merton. Patterns of influence: Local and cosmopolitan influentials. In R. K. Merton, editor, Social theory and social structure, pages 441--474. Free Press, New York, 1968.Google Scholar
- C. Sunstein. Going to extremes: how like minds unite and divide. Oxford University Press, USA, 2009.Google Scholar
- J. B. Walther, C. T. Carr, S. S. W. Choi, D. C. DeAndrea, J. Kim, S. T. Tong, and B. Van Der Heide. Interaction of interpersonal, peer, and media influence sources online. In Z. Papacharissi, editor, A Networked Self: Identity, Community, and Culture on Social Network Sites, pages 17--38. Routledge, 2010.Google Scholar
- J. Weng, E. P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on Web search and data mining, pages 261--270. ACM, 2010. Google ScholarDigital Library
Index Terms
- Who says what to whom on twitter
Recommendations
Information resonance on Twitter: watching Iran
SOMA '10: Proceedings of the First Workshop on Social Media AnalyticsTwitter has undoubtedly caught the attention of both the general public, and academia as a microblogging service worthy of study and attention. Twitter has several features that sets it apart from other social media/networking sites, including its 140 ...
A sentiment analysis of audiences on twitter: who is the positive or negative audience of popular twitterers?
ICHIT'11: Proceedings of the 5th international conference on Convergence and hybrid information technologyMicroblogging is a new informal communication medium of blogging that differs from a traditional blog in which content is much shorter. Microbloggers post about topics that describe their current status. Twitter is a popular microblogging service and ...
Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web
WWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceOver the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or “trolls.” ...
Comments