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Who says what to whom on twitter

Who says what to whom on twitter. Xiaomei Wu Winter A.Mason Jake M.Hofman Duncan J.Watts. Main research of this paper.

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Who says what to whom on twitter

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  1. Who says what to whom on twitter Xiaomei Wu Winter A.Mason Jake M.Hofman Duncan J.Watts

  2. Main research of this paper • Introduce a method for classifying users using Twitter Lists into “elite” and “ordinary” users, further classifying elite users into one of four categories of interest—media,celebrities, organizations, and bloggers. • Investigate the flow of information among these categories, finding that although audience attention is highly concentrated on a minority of elite users, much of the information they produce reaches the masses indirectly via a large population of intermediaries. • Find that different categories of users emphasize different types of content, and that different content types exhibit dramatically different characteristic lifespans,ranging from less than a day to months.

  3. Motivation of this paper • Theories of communications have tendedto focus either on “mass”communicationor on “interpersonal”communication • New channels of Mass communication :cable television, satellite radio, specialist book and magazine publishers, sponsored blogs, online communities,and social news sites. • New channels of interpersonal communication :personal blogs, email lists, and social networking sites • Masspersonal channel:Twitter

  4. Kwak et studied the topological features of the Twitter follower graph, number of followers, page-rank, and number of retweets Cha et compared three measures of influence—number of followers, number of retweets, and number of mentions Weng et compared number of followers and page rank with a modified page-rank measure which accounted for topic Bakshy et studied the distribution of retweet cascades on Twitter Related work

  5. Innovation of this paper Shifting attention to the flow of information among different categories of users. Focus on 4 identifying specific categories of “elite” users:media,celebrities, organizations, and bloggers.

  6. Data and methods

  7. Twitter follower graph • Observed by Kwark et in July 31st,2009 • Included 42M users and 1.5B edges • Follower graph network is a directed network characterized by skewed distributions both of in-degree(followers) and out-degree(friends)

  8. Twitter firehouse • 5B tweets generated over a 223 days from July 28,2009 to March 8,2010 from the Twitter • Focus on the subset of 260M containing bit.ly URLs

  9. Twitter lists

  10. Conclusions • Who listens to whom • Who listens to what • Two-step flow of information • Lifespan of content • Lifespan by category

  11. Who listens to whom • 0.05% of the population accounts for almost half of all posted URLs. • Attention is highly homophilous-celebrities following celebrities, media following media, and bloggers following bloggers.

  12. Who listens to what • Category:World News,U.S News,business, sports,Heath,Technology,Science,Arts • organizations show little interest in business and arts-related stories, and high interest in science, technology, and possibly world news. Celebrities, by contrast, show greater interest in sports and less interest in health, while the media shows somewhat greater interest in U.S. news stories.

  13. Two-step flow of information Half the information that originates from the media passes to the masses indirectly via a diffuse intermediate layer of opinion leaders

  14. Lifespan of content • Different types of content exhibit different lifespans • Classic music videos,movie clips,long-format magazine articles have long lifespan than daily news stories

  15. Lifespan by category • For vast majority of URLs,longevity is determined by rediscoving • For URLs introduced by elite users,longevity is determined by retweet

  16. Use twitter as their research object Classify twitter lists into elite users and ordinary users Emphasize elite users Restrict attention just to URLs on Twitter Overlook the unanticipated categories that may be of equal or greater relevance than the selected four categories Strength & Weakness

  17. Future work • Apply similar methods to quantifying information flow via more traditional channels, such as TV and radio • Explore automatic classification schemes from which additional user categories could emerge. • To extract content information in a more systematic manner—the “what” of Lasswell’s maxim; and second, to focus more on the effects of communication by merging the data regarding information flow on Twitter with other sources of outcome data.

  18. Thank you

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