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Measuring User Influence in Twitter: The Million Follower Fallacy
Open accessAbstract
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.
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“artificially inflating the number of followers can also be finalized to make an account more trustworthy and influential, in order to stand from the crowd and to attract other genuine followers [5].”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [5]
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“Linking biases impact the study of, e.g., social networks structure and evolution, social influence, and information diffusion phenomena”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Cha et al., 2010
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“Different graph construction methods can lead to different structural graph properties in the various kinds of networks that can be constructed from social data”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Cha et al., 2010
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Influencia de los medios de comunicación en la conversación política en Twitter
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Make Tweets Great Again: Who Are Opinion Leaders, and What Did They Tweet About Donald Trump?
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Infection Spreading and Source Identification: A Hide and Seek Game
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Jiang, J., Ren, X., & Ferrara, E. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22160 · Cited by 44
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“Other works include the detection of influential users [26], influence measurement in Twitter [4], etc. Outbreak detection.”
BackgroundPredicting the Audience Size of a Tweet
Kupavskii, A. B., Umnov, A. V., Gusev, G., & Serdyukov, P. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14454 · Cited by 38
“2010) and (Cha et al. 2011) authors analyzed different user rankings and found that rankings of users based on the number of followers and on the number of retweeted messages differ greatly.”
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Measuring user influence in GitHub
Badashian, A. S., & Stroulia, E. (2016) · doi:10.1145/2897659.2897663 · Cited by 40
“We are motivated to consider these three metrics inspired by Cha et al.’s study [9].”
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Soares, F. B., Recuero, R., & Zago, G. D. S. (2019) · International Conference on Social Media and Society (SMSociety) · doi:10.1145/3328529.3328546 · Cited by 47
“Among the different types of influencers [6, 9, 14, 24], news outlets are normally associated with the idea of informational influencers [24].”
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“The most efficient and widely used method is through information diffusion [12], where reaction to influencer posts, in this case, tweets, on specific topics are assessed.”
BackgroundMethodDatingSec: Detecting Malicious Accounts in Dating Apps Using a Content-Based Attention Network
He, X., Gong, Q., Chen, Y., Zhang, Y., Wang, X., & Fu, X. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3068307 · Cited by 40
“For a given activity set A , we have A ¼ f (cid:2); g g . denotes the original user-generated contents (original UGCs) like publishing a tweet on Twitter or sharing a post on Face-book. g denotes the interaction user-generated contents (interaction UGCs) like receiving comments from others.”
MethodInfluentialMultiple leaders on a multilayer social media
Borondo, J., Morales, A. J., Benito, R. M., & Losada, J. C. (2015) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2014.12.023 · Cited by 37
“However, it does not make a user influential in the active layers, as this depends on the value of the tweets content (retweets), or the name value of the user (mentions) [14].”
BackgroundThe Bursty Dynamics of the Twitter Information NetworkPreprint
Myers, S. A., & Leskovec, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.2732 · Cited by 38
“Many works have focused on the different factors that affect the spread of information, including the network structure [16, 27, 31], influence between members of the social network [4, 5, 7], out-ofnetwork influences such as other forms of media [9, 26], competing pieces of information [15, 25], and the nature of the…”
BackgroundCasero-Ripollés, A., Alonso-Muñoz, L., & Marcos-García, S. (2021) · American Behavioral Scientist · doi:10.1177/00027642211003159 · Cited by 37
“However, when we talk about Twitter, some previous research (Cha et al., 2010) has highlighted that having a very high number of followers does not necessarily imply influencing the conversation.”
BackgroundReliable Online Social Network Data Collection
Abdesslem, F. B., Parris, I., & Henderson, T. (2012) · Springer eBooks · doi:10.1007/978-1-4471-4054-2_8 · Cited by 37
“[7] collect 2 billion links among 54 million users to study people’s influence patterns on the OSN Twitter.”
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“…information flow on Twitter, the world ’ s first microblogging service, especially in computer science (e.g., Bakshy, Hofman, Mason, & Watts, 2011; Cha, Haddadi, Benevenuto, & Gummadi, 2010), few empirical studies have assessed how users are connected on Sina Weibo (Weibo, hereafter), the most…”
BackgroundRetweeting: A study of message-forwarding in twitter
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How Are Social Influencers Connected in Instagram?
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“Furthermore, this is exacerbated as much social media research highlights the actions and implications of social media ‘‘influen-cers’’ or ‘‘opinion leaders’’ (e.g., Cha et al., 2010; Xu et al., 2014).”
BackgroundMental Health Information Seeking Online: A Google Trends Analysis of ADHD
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“The internet is an especially important source for health information among individuals who have difficulty accessing timely services (Chen & Zhu, 2016) and parents (Khoo et al., 2008; Kubb & Foran, 2020).”
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“Cha et al. (2010) discussed three metrics aiming to quantify users’ influence in OSNs: number of connections (nodes degree), number of mentions, and number of messages reshared (retweets) by other users.”
Knowledge discovery in social networks by using a logic-based treatment of implications
Cordero, P., Enciso, M., Mora, Á., Ojeda‐Aciego, M., & Rossi, C. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.07.018 · Cited by 36
You unlocked the Mt. Everest badge on foursquare! Countering location fraud in Geosocial Networks
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“[14].”
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“Influence in Twitter is also shown to be the result of long-term reputation building in a network of individuals [9].”
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Mero, J., Ohtonen, R., & Karjaluoto, H. (2016) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2016.279 · Cited by 40
“User indegree was measured by the number of people who follow a user on Instagram [22].”
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Armentano, M. G., Godoy, D. L., & Amandi, A. (2011) · Cited by 36
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“Taking Twitter communication as a paragon of exchanging crisis-related information via social media, the impact of single users and messages, to a large extent, is reflected by their retweet ratios (Cha et al. 2010; Starbird and Palen 2012).”
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“In addition, Cha et al. [19] exploited the number of followers of users for discovering a given user’s influence in the Twitter networks.”
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BackgroundEquity crowdfunding platforms and social media: a Twitter analysis
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“[21] found that an popular tweet has either an interesting context or it is produced (retweeted) by an influencer.”
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TRank: Ranking Twitter users according to specific topics
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“Cha et al. [16] compared three different measures of influence (number of followers, number of retweets, and number of mentions) and found that the most followed users did not necessarily score highest on the other measures.”
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“[1] examined the usage and role of Twitter in creating a social community on the basis of its basic functions.”
BackgroundCollecting, organizing, and sharing pins in pinterest
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“Research shows users with many followers or retweets often are not the most influential users (Cha et al. 2010), so we followed Kwak et al. and used a version of the PageRank algorithm to identify important accounts in this network (2010).”
MethodUsers’ participation and social influence during information spreading on Twitter
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Positional analysis in cross-media information diffusion networks
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“The implemented data collection procedure starts with an initial search query based on hashtags, usernames, and keywords that is issued to the Twitter streaming API1.”
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Park, C. S., & Kaye, B. K. (2019) · Social Media + Society · doi:10.1177/2056305119834595 · Cited by 35
Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks
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“Moreover, state-of-the-art methods [13], [14], [23] relying on network topology need to directly compute the influence of each node to identify & rank the influen-tial users; this requires complete information of the network topology, which is typically difficult and expensive to obtain for…”
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Chen, F., & Tan, W. H. (2018) · The Annals of Applied Statistics · doi:10.1214/18-aoas1148 · Cited by 35
“We further assume that the impact function is linear in the number of followers on a log scale, rather than on the original scale as in [32], because of the high degree of right skewness for the distribution of the number of followers [4, 6, 17].”
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Al-Yazidi, S. A., Berri, J., Al‐Qurishi, M., & Alrubaian, M. (2020) · IEEE Access · doi:10.1109/access.2020.2999033 · Cited by 38
“It was established in 2006 and since 2010 has been studied extensively in the contexts of social network analysis, computer science, and sociology [51], [77], [80], [85], [94].”
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Marcos-García, S., Alonso-Muñoz, L., & López-Meri, A. (2021) · Cuadernos info · doi:10.7764/cdi.48.27679 · Cited by 35
Economic uncertainty spillover and social networks
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Predicting information diffusion on social networks with partial knowledge
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Does Opinion Leadership Increase the Followers on Twitter
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An Empirical Study on the Relationship between Twitter Sentiment and Influence in the Tourism Domain
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“One of the most interesting studies on microblogging is The Million Follower Fallacy, by (Benevenuto et al., 2010).”
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Gharaibeh, A., Reza, T., Santos‐Neto, E., Costa, L. B., Sallinen, S., & Ripeanu, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.3018 · Cited by 35
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“(Cha et al, 2010).”
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“…by dividing the first tweeter’s follower count by the sum of their 7 Using follower counts as a measure of power may be helpful in this regard; [8] suggest that high-follower users are more likely to be influentials who directly connect to their audience through one-on-one interactions, whereas…”
BackgroundAnalyzing and modeling dynamics of information diffusion in microblogging social network
Zhou, Y., Zhang, B., Sun, X., Zheng, Q., & Liu, T. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.09.011 · Cited by 34
“[13] compared three measures of influence: in-degree, retweets and mentions on Twitter, and indicated that users with high in-degree were not necessarily influential.”
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Topic detection and tracking on heterogeneous information
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“Another important factor is topic importance, which is first proposed in Cha et al. (2010).”
BackgroundBenito, S. M., Illera, A. E., & Fernández, E. O. (2020) · Communication & Society · doi:10.15581/003.33.3.149-166 · Cited by 34
“Whereas some perceive a clear link between follower numbers and opinion leadership (Yoganarasimhan, 2012; Feng, 2016; Hwang, 2015), others assert that follower numbers can be taken as indicators of an endorser’s popularity, but not necessarily of his or her power to persuade (Cha et al., 2010; Romero et al., 2011).”
BackgroundJust Another Day on Twitter: A Complete 24 Hours of Twitter Data
Pfeffer, J., Matter, D., Jaidka, K., Varol, O., Mashhadi, A., Lasser, J., Assenmacher, D., Wu, S., Yang, D., Brantner, C., et al. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22215 · Cited by 41
An author-reader influence model for detecting topic-based influencers in social media
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“Therefore, a common approach taken by such works is to first estimate the topic relevance of individuals [8, 19, 4] or their relationships [21, 24, 7, 3, 4] and then incorporating such estimates within “similar” (topic-oblivious) influence models.”
BackgroundMethodInfluentialThe Effect of Bad News and CEO Apology of Corporate on User Responses in Social Media
Kim, H., Park, J., Cha, M., & Jeong, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126358 · Cited by 34
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Pourazad, N., Stocchi, L., & Narsey, S. (2023) · Journal of Advertising Research · doi:10.2501/jar-2023-008 · Cited by 43
Expanding the presidential debate by tweeting: The 2012 presidential election debate in South Korea
Park, S. J., Park, J. Y., Lim, Y. S., & Park, H. W. (2015) · Telematics and Informatics · doi:10.1016/j.tele.2015.08.004 · Cited by 33
Impact of Multimedia in Sina Weibo: Popularity and Life Span
Xun, Z., Zhu, F., Qian, W., & Zhou, A. (2013) · Springer proceedings in complexity · doi:10.1007/978-1-4614-6880-6_5 · Cited by 34
“Follower count could be used as a measure for user popularity [1].”
BackgroundMethodInfluentialCheng, D., Bahadori, M. T., & Liu, Y. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623709 · Cited by 33
Zarezade, A., Upadhyay, U., Rabiee, H. R., & Gomez-Rodriguez, M. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018684 · Cited by 33
Bot stamina: examining the influence and staying power of bots in online social networks
Schuchard, R., Crooks, A. T., Stefanidis, A., & Croitoru, A. (2019) · Applied Network Science · doi:10.1007/s41109-019-0164-x · Cited by 39
“Learning from Cha et al. (2010) that relative influence in Twitter by users is not necessarily gained through popularity (i.e. associated follower volume), we can look to SNA techniques to derive influence in OSNs (Kwak et al. 2010; Weng et al. 2010; Bakshy et al. 2011; Riquelme and…”
MethodMohbey, K. K. (2019) · Journal of Data Information and Management · doi:10.1007/s42488-019-00013-y · Cited by 32
“It provides TwitterAPI to crawl on tweets according to a particular hashtag (Cha et al., 2010; Trusov et al., 2010; Messias et al., 2013) (#IndianElection2019, #2019Election).”
BackgroundTop influencers can be identified universally by combining classical centralities
Bucur, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-77536-7 · Cited by 33
“For example, the degree centrality was found a weak predictor in early studies, over both simulated and measured diffusion 2,3 .”
An Evaluation of the Effect of Spam on Twitter Trending Topics
Stafford, G., & Yu, L. L. (2013) · International Conference on Social Computing · doi:10.1109/socialcom.2013.58 · Cited by 33
“[9] explored user influences on Twitter trends and also found several interesting results.”
ResultThe 10 million follower fallacy: audience size does not prove domain-influence on Twitter
Cataldi, M., & Aufaure, M.-A. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-014-0773-8 · Cited by 33
“[12], even if the users are widely known and read within the community, it does not automatically mean that they are able to influence their activity and/or their interests.”
BackgroundResultInfluentialRed, Purple and Pink: The Colors of Diffusion on Pinterest
Bakhshi, S., & Gilbert, E. (2015) · PLoS ONE · doi:10.1371/journal.pone.0117148 · Cited by 42
“also considered an additional measure, number of mentions on Twitter, to quantify diffusion [19].”
BackgroundArularasan, A. N., Suresh, A., & Koteeswaran, S. (2018) · Cluster Computing · doi:10.1007/s10586-018-2616-y · Cited by 33
“An approach [18] determines the influential spreaders based on the wide popularity of the social users and the influence on multiple topics over the social network.”
BackgroundWill Admins Cope? Decentralized Moderation in the Fediverse
Anaobi, I. H., Raman, A., Castro, I., Zia, H. B., Ibosiola, D., & Tyson, G. (2023) · Proceedings of the ACM Web Conference 2023 · doi:10.1145/3543507.3583487 · Cited by 33
Fair Influence Maximization in Social Networks: A Community-Based Evolutionary Algorithm
Ma, K., Xu, X., Yang, H., Cao, R., & Zhang, L. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2024.3403891 · Cited by 32
Determining Influential Users with Supervised Random Walks
Katsimpras, G., Vogiatzis, D., & Παλιούρας, Γ. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2740908.2742472 · Cited by 32
“In [4], they made a comparison of some simple measures of influence, expressly number of followers, number of re-tweets, and number of mentions.”
BackgroundThe spread of media content through blogs
Cha, M., Navarro-Pérez, J. A., & Haddadi, H. (2011) · Social Network Analysis and Mining · doi:10.1007/s13278-011-0040-x · Cited by 32
“The practice of information sharing and the new journalism conventions have now spread to other social media such as Twitter and Facebook, and such a culture has become a significant part of today’s Internet.1 In this paper, we study the trends in the use of blogs as social media to share and exchange information.”
InfluentialSelf-citationSocial capitalists on Twitter: detection, evolution and behavioral analysis
Dugué, N., & Perez, A. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0178-4 · Cited by 34
“Context In the last decade, large and complex data have been produced through the study of Internet (Cha et al. 2010), business intelligence (Thusoo et al. 2010) or bioinformatics (Schatz et al. 2010).”
InfluentialMeasuring message propagation and social influence on Twitter.com
Ye, S., & Wu, F. (2013) · International Journal of Communication Networks and Distributed Systems · doi:10.1504/ijcnds.2013.054835 · Cited by 32
“[1], the most retweeted users were content aggregation services, businessmen and news sites.”
BackgroundInfluentialClassifying Short Text in Social Media: Twitter as Case Study
Kateb, F., & Kalita, J. K. (2015) · International Journal of Computer Applications · doi:10.5120/19563-1321 · Cited by 32
Sampling Content from Online Social Networks
Zafar, M. B., Bhattacharya, P., Ganguly, N., Gummadi, K. P., & Ghosh, S. (2015) · ACM Transactions on the Web · doi:10.1145/2743023 · Cited by 37
“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].”
BackgroundSelf-citationResearch on the Motives Affecting the Behavior of Short Video’s Creators
Bi, X., & Tang, C. (2020) · IEEE Access · doi:10.1109/access.2020.3028392 · Cited by 32
“…behaviors, and to predict possible behaviors [27], [28], usually taking Twitter, Facebook and other social media users as the research object, so that the dynamic changes of user behavior are dynamically studied through variables such as access degree, network density, and intermediary [29]–[31].”
BackgroundInfluenceTracker: Rating the Impact of a Twitter Account
Razis, G., & Anagnostopoulos, I. E. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44722-2_20 · Cited by 35
Multimedia Semantics: Interactions Between Content and Community
Sundaram, H., Xie, L., Choudhury, M. D., Lin, Y., & Natsev, A. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2191529 · Cited by 32
“In Facebook and Twitter, for example, communities emerge due to users' topical interests and collaboration on projects.”
BackgroundMethodInfluentialBelieve it or Not? Analyzing Information Credibility in Microblogs
Kang, B., Höllerer, T., & O’Donovan, J. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2809379 · Cited by 32
“Other researchers have studies influence at the network level, identifying trends and patterns that lead to tipping points of credibility and popularity [18] and surges in influence within the network [19].”
BackgroundDugué, N., & Perez, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4098768 · Cited by 32
A Literature Review on Detecting, Verifying, and Mitigating Online Misinformation
Bodaghi, A., Schmitt, K., Watine, P., & Fung, B. C. M. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3289031 · Cited by 43
Detecting Arabic spammers and content polluters on Twitter
El-Mawass, N., & Alaboodi, S. S. (2016) · International Conference on Digital Information Processing and Communications · doi:10.1109/icdipc.2016.7470791 · Cited by 29
“A tweet acquires a reputation if its content is relevant to a lot of people, or if the account that generated it is famous/important [19].”
BackgroundA Case Study of Complex Graph Analysis in Distributed Memory: Implementation and Optimization
Slota, G. M., Rajamanickam, S., & Madduri, K. (2016) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2016.93 · Cited by 31
“We also use the LiveJournal and Google graphs from SNAP [15], [32] and a Twitter crawl [2].”
MethodSemi-Supervised Network Embedding
Li, C., Li, Z., Wang, S., Yang, Y., Zhang, X., & Zhou, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-55753-3_9 · Cited by 31
“However, according to literature [6], the influence of the direct neighbor nodes on a specific node can be significantly larger than those that are farther from it.”
Background
Metadata
- Authors
- Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K.
- Affiliations
- Max Planck Society (DE)
- University of London (GB)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2010-05-16
- Volume
- 4
- Issue
- 1
- Pages
- 10-17
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W1814023381
- Semantic Scholar
- 1ad8410d0ded269af4a0116d8b38842a7549f0ae
- Cited by
- 3,022
- References
- 21
- Citations by year
- 2026: 44 · 2025: 74 · 2024: 61 · 2023: 83 · 2022: 130 · 2021: 212 · 2020: 184 · 2019: 198 · 2018: 257 · 2017: 266 · 2016: 253 · 2015: 287 · 2014: 329 · 2013: 310 · 2012: 193
- FWCI
- 142.70
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- user influence, Twitter, influence measures, in-degree, retweets, mentions, directed links, viral marketing, information flow, topic dynamics, influential users
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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