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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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“[5] proposed a method to measure the user influence in Twitter used the directed links information, and present the comparison of three static measures of influence.”
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“Despite its simplicity, follower number is considered as a rational indicator of user influence since following links determine the flow of information [63] and more following links means more opportunities to influence others.”
BackgroundResultUnsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks
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“Existing work has considered a variety of social features including metadata features such as the number of followers and followees [9, 19], the number of retweets and mentions [8], semantic features such as the topics of a candidate influencer’s microposts [32, 46], or features derived from user behavioral data such…”
BackgroundMishra, R. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00334 · Cited by 24
“Interpersonal relationships among users are the key factor in determining the influence [5].”
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“For i , her utility will depend on the size of the existing follower network for s ( k isr ) as higher follower counts are a measure of influence and signal popularity (Cha et al., 2010).”
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“Similar representations (with invitations, ‘follows’, or ‘retweets’ as directed edges) have been used for identifying influencers in social networks [4, 7, 22].”
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“Weng et al. (2010) proposed an algorithm called TwitterRank to measure the influence taking both the topical similarity between users and the link structure into account.”
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TWITTER EVENT NETWORKS AND THE SUPERSTAR MODEL
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Social Media Influence and Electoral Competition
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Identifying influential users of micro-blogging services: A dynamic action-based network approach
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BackgroundUnderstanding Cancer-Based Networks in Twitter Using Social Network Analysis
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“In this natural experiment we control for retweets, user popularity, user characteristics, the million follower fallacy (Cha et al 2010) and new mentions.”
BackgroundResultInfluentialAnalysis of the use of Twitter as a learning strategy in master’s studies
Murias, T. F., Ricoy, C., & Ricoy, S. F. (2013) · Open Learning The Journal of Open Distance and e-Learning · doi:10.1080/02680513.2013.870029 · Cited by 22
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MethodTemporal TF-IDF: A High Performance Approach for Event Summarization in Twitter
Alsaedi, N., Burnap, P., & RANA, O. F. (2016) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1109/wi.2016.0087 · Cited by 22
“Third, number of retweets is an indication of popularity [32] so in a way we are summarizing the cluster by the highest degree of agreement from users themselves.”
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Influencers in Multiplayer Online Shooters
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“The first group of influencers are often referred to as opinion leaders [21], prestigious innovators [12], key-players [8] and spreaders [31].”
BackgroundAn Exploratory Data Analysis of the #Crowdfunding Network on Twitter
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“Specifically, we performed word analysis and hashtag analysis on the tweets generated by the most influential users as they have the ability to drive discussions and influence the opinion of other users [56,77,88].”
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“In the classical dichotomy between who and what, Cha et al. (2010) posited that retweets are motivated by the content value of the tweet (what), while mentions are motivated by the identity of the Twitter user (who).”
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Blogging for ICT4D: reflecting and engaging with peers to build development discourse
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“These seven bloggers were very active in their blogging practices, as reflected in their frequency of posting, in the comments they received from their posts and in the mentions they received on the ICT4D online mailing list, on blogs and on Twitter (comments and re-tweets) (Cha et al., 2010).”
BackgroundRecommending Users and Communities in Social Media
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“In social media services, users typically have numerous connections to other site members through a virtual friendship; however, studies show that only a fraction of those so-called friends may affect a user’s site usage [Cha et al. 2010].”
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Jaho, E., Karaliopoulos, M., & Ioannis Stavrakakis, A. (2012) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.166 · Cited by 21
“Worst of all, the node N has to fetch the content items ranking at positions ½ðC þ 1Þ; ðM ðN 1ÞkÞ in its own preference distribution at cost ts, whereas he can get access to content objects in positions ½M ðN 1Þkþ 1;M through the storage of the other group nodes.”
Background# Living/minimum wage: Influential citizen talk in twitter
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“These include limiting one‘s tweets to a single topic / hashtag and keeping high levels of personal involvement (Cha et al 2010), maintaining high levels of activity (Romero et al 2011) and posting tweets that express a negative mood and a sense of community (Quercia et al 2011).”
BackgroundInfluentialAnticipating Discussion Activity on Community Forums
Rowe, M., Angeletou, S., & Alani, H. (2011) · International Conference on Social Computing · doi:10.1109/passat/socialcom.2011.215 · Cited by 21
“Cha et. al [10] measured user influence in Twitter from their number of followers, retweets and mentions.”
BackgroundRead This, Please? The Role of Politeness in Customer Service Engagement on Social Media
Hu, Y., Tafti, A., & Gal, D. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.541 · Cited by 20
“The recent work by [9] on users’ social influence on Twitter indicates that popular users who have high in-degree (number of followers) are not necessarily influential in terms of spawning retweets or mentions but can hold significant influence over a variety of topics.”
BackgroundKosnik, A. T. D., & Feldman, K. (2018) · University of Michigan Press eBooks · doi:10.3998/mpub.9697041 · Cited by 20
Attacking the Core Structure of Complex Network
Zhou, B., Lv, Y., Wang, J., Zhang, J., & Xuan, Q. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3188522 · Cited by 21
Twitter as Political Communication Space: Publics, Prominent Users, and Politicians
Jungherr, A. M. (2015) · Contributions to political science · doi:10.1007/978-3-319-20319-5_4 · Cited by 21
Los Twindignados: The Rise of the Indignados Movement on Twitter
Vallina-Rodríguez, N., Scellato, S., Haddadi, H., Forsell, C., Crowcroft, J., & Cecilia Mascolo, C. (2012) · International Conference on Social Computing · doi:10.1109/socialcom-passat.2012.120 · Cited by 21
“In particular, Twitter data have been used to validate theories of social influence [3], and it has been claimed that sentiment analysis and counting of tweets can be used to predict the outcomes of elections [4]; 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on…”
BackgroundMethodSelf-citationIdentifying Peer Influence in Online Social Networks Using Transfer Entropy
He, S., Zheng, X., Zeng, D. D., Cui, K., Zhang, Z., & Luo, C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39693-9_6 · Cited by 21
“suggest that structural measures alone reveals very little about the influence of a user [16].”
BackgroundA study on the influential neighbors to maximize information diffusion in online social networks
Kim, H., Beznosov, K., & Yoneki, E. (2015) · Computational Social Networks · doi:10.1186/s40649-015-0013-8 · Cited by 21
“For example, in real-world online social network services such as Twitter or Facebook, each user has a distinct propagation rate for her neighbors on spreading information according to the user’s reputation and/or role, such as opinion formers, leaders, or followers [9]; (2) their experimental results were limited to…”
InfluentialRamírez-de-la-Rosa, G., Villatoro-Tello, E., Jiménez-Salazar, H., & Sánchez-Sánchez, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13647-9_23 · Cited by 21
The roles of network communities in social information diffusion
Li, C., Lin, Y.-J., & Yeh, M.-Y. (2015) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2015.7363780 · Cited by 21
Being prepared in a sparse world: The case of KNN graph construction
Boutet, A., Kermarrec, A.-M., Mittal, N., & Taı̈ani, F. (2016) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2016.7498244 · Cited by 21
“The long-tailed curves are consistent with earlier observation [20], [21], [22] and show that most users have very few ratings.”
ResultLangi, P. P. I., Widyawan, W., Najib, W., & Aji, T. B. (2015) · International Conference on Information & Communication Technology and System (ICTS) · doi:10.1109/icts.2015.7379895 · Cited by 21
Big Data Analytics on Aviation Social Media: The Case of China Southern Airlines on Sina Weibo
Chen, S., Huang, Y., & Huang, W. (2016) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice.2016.51 · Cited by 21
“Based on the idea of PageRank [9] , we use PeopleRank to define the hierarchy of social networks for a user.”
MethodMobile Contextual Recommender System for Online Social Media
Wu, C., Zhang, Y., Jia, J., & Zhu, W. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2694830 · Cited by 21
“In addition, a number of recent studies, such as [41], [42], address the problem of computing influ-”
BackgroundNonlinear Dynamics of Information Diffusion in Social Networks
Matsubara, Y., Sakurai, Y., Prakash, B. A., Li, L., & Faloutsos, C. (2017) · ACM Transactions on the Web · doi:10.1145/3057741 · Cited by 21
“Analysis of information diffusion and influence propagation in social networks have also attracted considerable interest [Lou and Tang 2013; Weng et al. 2010a; Kwon et al. 2013; Cha et al. 2010; Kempe et al. 2003b; Chen et al. 2009; Leskovec et al. 2007a].”
BackgroundTowards fast and lightweight spam account detection in mobile social networks through fog computing
Zhang, J., Li, Q., Wang, X., Feng, B., & Guo, D. (2017) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-017-0559-3 · Cited by 21
“The datasets of their projects can be downloaded for free from the website [28].”
BackgroundChen, S., Chen, S., Wang, Z., Liang, J., Ya, W., & Yuan, X. (2018) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3183347 · Cited by 21
“Cha et al. [14] measured a user’s influence on Twitter based on the indegree, the number of retweets and mentions.”
BackgroundXu, C., Vora, K., & Gupta, R. (2019) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3297858.3304012 · Cited by 21
“, TwitterTT [5] used in this paper) and although it can handle small graphs (e.”
MethodA Systems Dynamics Simulation Study of Network Public Opinion Evolution Mechanism
Gao, G., Wang, T., Zheng, X., Chen, Y., & Xu, X. (2019) · Journal of Global Information Management · doi:10.4018/jgim.2019100110 · Cited by 21
Curiel, C. P., & García-Gordillo, M. (2020) · El Profesional de la Informacion · doi:10.3145/epi.2020.jul.05 · Cited by 24
A named entity topic model for news popularity prediction
Yang, Y., Liu, Y., Lu, X., Xu, J., & Wang, F. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106430 · Cited by 21
Graph Colouring as a Challenge Problem for Dynamic Graph Processing on Distributed Systems
Sallinen, S., Iwabuchi, K., Poudel, S., Gokhale, M. B., Ripeanu, M., & Pearce, R. (2016) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2016.29 · Cited by 21
“Name #Vertices #Edges OnDiskSpace Friendster [22] 65,608,366 3,612,134,270 61 GB Twitter [23] 41,652,230 2,936,729,768 49 GB SK2005 [24] 50,636,059 3,860,585,896 65 GB Webgraph [25] 3,563,602,686 257,473,828,334 5.1 TB RMAT(SCALE) 2(SCALE) 2(SCALE) ∗ 32 e.g. RMAT32 4,294,967,296 137,438,953,472 For this experiment we…”
InfluentialExperimental Analysis of Rumor Spreading in Social Networks
Doerr, B., Fouz, M., & Friedrich, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34862-4_12 · Cited by 21
“We performed our experiments on a snap-shot of the Twitter network that was crawled in September 2009 by Cha, Had-dadi, Benevenuto, and Gummadi [13], available from [4].”
MethodThe Impact of Emotion: A Blended Model to Estimate Influence on Social Media
Chang, W. (2018) · Information Systems Frontiers · doi:10.1007/s10796-018-9824-0 · Cited by 21
“Certain researchers have attempted to quantify social influence in terms of ideas, opinions, comments, and thoughts (Romero et al. 2011; Cha et al. 2010; Chen et al. 2012).”
BackgroundMethodInfluentialMethods for Sentiment Analysis of Twitter Messages
Barhan, A., & Shakhomirov, A. (2012) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 21
White, Man, and Highly Followed: Gender and Race Inequalities in TwitterPreprint
Messias, J., Vikatos, P., & Benevenuto, F. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.08619 · Cited by 21
“[5], our strategy to identify U.S. Twitter users was based on part of the methodology used in previous efforts [6, 20].”
BackgroundMethodSelf-citationACQR: A Novel Framework to Identify and Predict Influential Users in Micro-Blogging.
Chai, W., Xu, W. X., Zuo, M., & Wen, X. (2013) · Pacific Asia Conference on Information Systems · Cited by 20
Accelerating reachability query processing based on $$\varvec{DAG}$$ DAG reduction
Zhou, J., Yu, J. X., Li, N., Wei, H., Chen, Z., & Tang, X. (2018) · The VLDB Journal · doi:10.1007/s00778-018-0495-8 · Cited by 20
“Each italic number denotes the best result on its row social network obtained from a crawl of twitter.com [4]. web-uk 11 is a DAGof a web graph dataset [3].”
BackgroundDiscover Your Social Identity from What You Tweet: A Content Based Approach
Huang, B., & Carley, K. M. (2020) · Lecture notes in social networks · doi:10.1007/978-3-030-42699-6_2 · Cited by 20
COVID-19 Health Communication Networks on Twitter: Identifying Sources, Disseminators, and Brokers
Kim, I., & Valente, T. W. (2020) · Connections · doi:10.21307/connections-2019.018 · Cited by 20
“…reported that influential users are those who have an active audience who mentions or retweets the users, instead of the large number of followers (Cha et al., 2010) and the number of followers/ followees does not fully explain users’ actual activities (Hamzehei et al., 2017); however, it may be…”
BackgroundBlond, S. L., Choffnes, D., Caldwell, W., Druschel, P., & Merritt, N. (2015) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2829988.2787491 · Cited by 20
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.
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