Measuring User Influence in Twitter: The Million Follower Fallacy

By Meeyoung Cha, Hamed Haddadi, Fabrício Benevenuto, Krishna P. Gummadi. 2010

3,363 citations recorded by the source corpus.

Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “structural graph properties in the various kinds of networks that can be constructed from social data (Cha et al., 2010).”
Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls (2014, background): “Another important social media study, based on Twitter, speaks of in-degree (number of followers) as a user’s popularity, and retweets as influence (Cha et al., 2010).”
Sampling Content from Online Social Networks (2015, background): “There are several metrics to rank users in Twitter according to their popularity , and hence to identify most popular users [Cha et al. 2010; Ghosh et al. 2012a; Kwak et al. 2010].”
The Tweets They Are a-Changin: Evolution of Twitter Users and Behavior (2014, methodology): “, most users have many more friends that followers) indicating that Twitter is disassortative; similar observations have been made about Twitter (Cha et al. 2010) and other social networks (Mislove et al.”
Predicting emerging social conventions in online social networks (2012, methodology): “We obtained the Twitter data set from [14], which comprises the following three types of information: profiles of 54 million users, 1.”
Combating Attacks and Abuse in Large Online Communities (2016, background): “To begin with, we examine the possibility to buildmachine learning classifiers to detect workers who perform crowdturfing tasks (on Weibo, Chinese Twitter).”
Searching for superspreaders of information in real-world social media (2014, methodology): “To approximate the social network, we use an alternative way - the mention network, which has been studied in many previous works on Twitter [48, 56, 57].”
Influence and passivity in social media (2010, background): “The number of times a user has been credited in a retweet has been assumed to be a good measure of influence [ 7 ].”
Tweeting is believing?: understanding microblog credibility perceptions (2012, background): “For user names, those who plan to tweet exclusively on a specific topic (an advisable strategy for building a large follower base [4]), should adopt a topically-aligned user name as those generated high levels of credibility.”
Searching microblogs: coping with sparsity and document quality (2011, background): “[4] discovered that a very large number of followers do not necessarily have an impact on a user being retweeted more often.”
Procedia Social and Behavioral Sciences (neutral): “Cha et al. (2010) compared three different measures of influence − indegree, retweets and user mentions.”
Can cascades be predicted? (2014, background): “Still, [8] found that various measures of a user’s popularity are not very correlated with his or her influence.”