50,000,00 Twitter fans can't be wrong
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50,000,00 Twitter fans can't be wrong

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A review of Cha, Meeyoung, et al. "Measuring User Influence in Twitter: The Million Follower Fallacy." ICWSM 10 (2010): 10-17.

A review of Cha, Meeyoung, et al. "Measuring User Influence in Twitter: The Million Follower Fallacy." ICWSM 10 (2010): 10-17.

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50,000,00 Twitter fans can't be wrong 50,000,00 Twitter fans can't be wrong Presentation Transcript

  • 50,000,00 Twitter fans can’t be wrong ...right? Marie Boran Wednesday 9 October 13
  • Measuring User Influence in Twitter: The Million Follower Fallacy Cha et al., 2010 ICWSM 2010 Cited by 678 (Google Scholar), 498 readers (Mendeley), Wednesday 9 October 13
  • What is influence? • Traditional communication theory - target the influentials (Rogers 1962) • Influence spreads through opinion leaders (Katz and Lazarsfeld 1955), innovators (Rogers), hubs/connectors/mavens (Gladwell 2002) • Doesn’t take into account the ordinary users • Influentials are neither vital nor sufficient for all diffusions (Watts and Dodds 2007) • Anyone can spark a revolution as long as the mood is right! (Watts 2007) Wednesday 9 October 13
  • Influence on Twitter RT retweets @mentions indegree Wednesday 9 October 13
  • Influence on Twitter value of one’s tweets user’s name value popularity Wednesday 9 October 13
  • An empirical analysis of influence patterns • Treated Twitter as a news spreading medium • Studied types and degrees of influence within the network • Focused on three “interpersonal” Twitter activities • Used collected data to analyse characteristics of top users Wednesday 9 October 13
  • • Used Twitter API to gather tweets and social links for user IDs 0-80 million (Back in ’09 when Twitter API was more accessible!) • Gathered 55m in-use accounts & 1.75bn tweets • Filtering: ignored private accounts, those not connected to anyone, users <10 tweets, invalid usernames • Left with 6m active, connected users - computed 3 influence values for each and compared Wednesday 9 October 13
  • Methodology • Used Twitter API to gather tweets and social links for user IDs 0-80 million (Back in ’09 when Twitter API was more accessible!) • Gathered 55m in-use accounts & 1.75bn tweets • Filtering: ignored private accounts, those not connected to anyone, users <10 tweets, invalid usernames • Left with 6m active, connected users - computed 3 influence values for each and compared Wednesday 9 October 13
  • Findings Based on top 20 users for each measure Most followed users (unsurprisingly) were public figures and news sources Most retweeted were content aggregation services, businesspeople, news sites Most mentioned users were mostly celebrities: people like to mention them without necessarily retweeting their content Marginal overlap between categories. Two users made the top 20 in all three *cough* Ashton Kutcher and Puff Daddy *cough* < they are entrepreneurs as well as celebs after all! Mr Fry has 6.2m followers Wednesday 9 October 13
  • Insights • RTs are content driven (92% contain URL), mentions are identity driven ( >30% contain URL) • RT activity reinforces theory that probability of adopting an innovation increases when not one but a group of users repeat the same message (Watts and Dodds 2007) • Strong correlation between retweet influence and mention influence • Indegree was not related to the other measures thus providing evidence for the million follower fallacy so it’s not the follower count that matters but how you use it! Wednesday 9 October 13
  • Is influence topic-dependent? Top news trends in 2009: Michael Jackson’s death, Iranian elections, swine flu. Authors searched Twitter dataset for related keywords <2% (13,219) of Twitter users mentioned these topics discussed all three These users were: well connected, average of 2k followers, tweeted about many topics - perfect group to study user influence across varied topic genres Power-law: top influentials were RT’d or @’d disproportionately more times than majority of users Wednesday 9 October 13
  • Message to marketers: tapping into these top influentials has great potential payoff Wednesday 9 October 13
  • Maintaining engagement Authors measure influence over time in two ways: 1.Track popularity of top users over long term 2. Look at users who increased influence in specific topic over short time period Remember Figure 1 overlap? - we look at these all-time influentials and their scores over a 9 month period (had to normalise for Twitter growth spurt; more users, more tweets). FYI Google does this when analysing search trends All three groups (top 10, top 100, top 233) increased their influence over time but interesting stuff happening with top 10; their popularity fell over time. These were mostly media sources so while users RT breaking news as the follower count grows it becomes difficult for top 10 to engage with audience Wednesday 9 October 13
  • Group 2 (celebs) get mentioned more than RT’d due to their name value Group 3 (evangelists) increased influence by conversing with others (they’re driven by desire to promote themselves!) Note: Authors say overall slight increase due to limited number of tweets per day. “Broadcasting too many tweets puts even popular users at risk of being classified as spammers”. Wednesday 9 October 13
  • What about us ordinary folk? Back to the news topics: top 20 users (based on follower count) for each topic, referred to as the topical influentials Included previously unheard of users & figures like Kevin Rose (Digg) who increased popularity after mentioning these news topics If we look at influence (both RT and mentions) of those talking about Iranian elections we see it peaks in June/July ’09 when elections were ongoing Those who talked about swine flu and Jackson had bumps in mentions but this soon faded as the news grew stale Authors found (by manual inspection) that users who stick to a single topic gained the largest increase in influence scores Wednesday 9 October 13
  • Conclusions • Indegree represents popularity but is not related to other kinds of influence such as engaging the audience • Retweets are content driven, mentions are personal brand driven • There are three distinct kinds of influential users on Twitter • Top Twitter users have disproportionate amount of influence • News orgs good at getting RTs, celebs consistently get high no. of mentions • Influence isn’t spontaneous or accidental, takes time and effort Wednesday 9 October 13
  • Critique • Conclusions apply to general news topics. Authors don’t explain difference between niche topics or users, could have done this with their dataset, identified communities of influence perhaps, might find different results in e.g. tech, science, sports, politics. Romero et al., 2001. Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter Information diffusion across various topics by using hashtags On premise that “widespread intuitive sense that different kinds of information spread differently on-line” Use concept of “social contagion” to explain spread of topics Look at “stickiness,” the probability of adoption based on one or more exposures, but also to a quantity that could be viewed as a kind of “persistence”—the relative extent to which repeated exposures to a hashtag continue to have significant marginal effects Wednesday 9 October 13
  • Thank you very much ...questions? Wednesday 9 October 13