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

1. 50,000,00 Twitter fans can’t be
wrong ...right?
Marie Boran
Wednesday 9 October 13

2. 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

3. 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

4. Influence on Twitter
RT retweets
@mentions
indegree
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5. Influence on Twitter
value of one’s
tweets
user’s name
value
popularity
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6. 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
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7. • 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

8. 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

9. 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

10. 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

11. 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
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12. Message to marketers: tapping into these top
influentials has great potential payoff
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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

14. 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

15. 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

16. 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

17. 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
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18. Thank you very
much ...questions?
Wednesday 9 October 13