Who says what to whom on twitter
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Transcript of 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
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.
Motivation of this paper
Theories of communications have tended to focus either on “mass” communication or 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
Related work
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
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.
Data and methods
Data set
Twitter follower graph
Twitter firehouse Twitter list
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)
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
Twitter lists
Conclusions
Who listens to whom Who listens to what Two-step flow of information Lifespan of content Lifespan by category
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.
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.
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
Lifespan of content
Different types of content exhibit different lifespans Classic music videos,movie clips,long-format magazi
ne articles have long lifespan than daily news stories
Lifespan by category
For vast majority of URLs,longevity is determined by rediscoving
For URLs introduced by elite users,longevity is determined by retweet
Strength & Weakness
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
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.
Thank you