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 be used to make an account more trustworthy and influential, in order to attract other genuine followers.
“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]
The Introduction (page unavailable) says follower count alone reveals little about influence and that even a million followers does not necessarily mean much; it identifies retweets and mentions from an active audience as more meaningful. This runs contrary to the claim that inflating follower count makes an account influential. The paper does not address whether artificial inflation increases perceived trustworthiness or attracts genuine followers. (LLM, conf=0.76)
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Experimental — accuracy not yet measuredLinking biases impact the study of social networks structure and evolution, social influence, and information diffusion phenomena.
“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
In “Related work on Twitter” (page unavailable), the paper contrasts a 72% link-reciprocity estimate from a nonrandom sample with 10% in near-complete Twitter data, supporting the point that sampling can affect conclusions about network structure. The Abstract also cautions that link-based measures alone reveal little about influence. Our copy does not establish the broader claim that linking biases affect studies of network evolution or information diffusion. (LLM, conf=0.76)
Experimental — accuracy not yet measuredDifferent graph construction methods can lead to different structural graph properties in networks constructed from social data.
“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
In Related work on Twitter (page unavailable), the paper contrasts 72% link reciprocity in a nonrandom sample with 10% in near-complete Twitter data. That supports the narrower point that data selection can change an observed graph property, but it does not compare different methods of constructing various kinds of social-data networks. (LLM, conf=0.82)
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Trends in Social Media: Persistence and DecayPreprint
Asur, S., Huberman, B. A., Szabo, G., & Wang, C. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1755748 · Cited by 308 · see citing works
“As to the study of influence within Twitter, Cha et al. [3] performed a comparison of three different measures of influence - indegree, retweets, and user mentions.”
BackgroundComparing and combining sentiment analysis methods
Gonçalves, P., Araújo, M., Benevenuto, F., & Cha, M. (2013) · Cited by 304 · see citing works
Self-citationYang, C., Harkreader, R., Zhang, J., Shin, S., & Gu, G. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187847 · Cited by 299 · see citing works
Open challenges for data stream mining research
Krempl, G., Žliobaitė, I., Brzeziński, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., et al. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2674026.2674028 · Cited by 295 · see citing works
Kim, E., Sung, Y., & Kang, H. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.04.020 · Cited by 281 · see citing works
“Retweets can be more powerful in reinforcing a message because they are repeated among groups of users who are strongly connected (Cha et al., 2010; Hung and Li, 2007).”
BackgroundRumor Detection over Varying Time Windows
Kwon, S., Cha, M., & Jung, K. (2017) · PLOS ONE · doi:10.1371/journal.pone.0168344 · Cited by 352 · see citing works
Self-citationSemantic Enrichment of Twitter Posts for User Profile Construction on the Social Web
Abel, F., Gao, Q., Houben, G., & Tao, K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21064-8_26 · Cited by 269 · see citing works
“studied information propagation patterns in the global Twitter network [4,5] and exploited network structures to identify influentual users [6,7] as well as malicious users [8,9].”
BackgroundA survey of Twitter research: Data model, graph structure, sentiment analysis and attacks
Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114006 · Cited by 279 · see citing works
Measuring influence on Twitter
Anger, I., & Kittl, C. (2011) · International Conference on Knowledge Management and Knowledge Technologies · doi:10.1145/2024288.2024326 · Cited by 263 · see citing works
The Multiple Facets of Influence
Dubois, E., & Gaffney, D. (2014) · American Behavioral Scientist · doi:10.1177/0002764214527088 · Cited by 263 · see citing works
“This sample is large enough to provide variety but small enough to conduct meaningful qualitative analysis and is in line with past studies (e.g., Cha & Gummadi, 2010; Wu et al., 2011).”
BackgroundMethodResultInfluentialLee, K. K., Oh, W., & Kim, N. (2013) · Journal of Business Ethics · doi:10.1007/s10551-013-1961-2 · Cited by 254 · see citing works
Mining Brand Perceptions from Twitter Social Networks
Culotta, A., & Cutler, J. (2016) · Marketing Science · doi:10.1287/mksc.2015.0968 · Cited by 254 · see citing works
“For example, Das and Chen (2007) compared five sentiment classification algorithms and reported accuracy rates ranging from 25–40% (up to 67% when ambiguous messages are pre-filtered) for out-of-sample validation.”
BackgroundLearning about Social Learning in MOOCs: From Statistical Analysis to Generative Model
Brinton, C. G., Chiang, M., Jain, S., Lam, H., Liu, Z., & Wong, F. M. F. (2014) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2014.2337900 · Cited by 250 · see citing works
“Categories and Subject Descriptors G.1.6 [Mathematics of Computing]: Least squares methods; I.2.6 [Computing Methodologies]: Concept Learning”
MethodInfluentialMeasuring Message Propagation and Social Influence on Twitter.com
Ye, S., & Wu, S. F. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16567-2_16 · Cited by 246 · see citing works
Assessing the bias in samples of large online networks
González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., & Moreno, Y. (2014) · Social Networks · doi:10.1016/j.socnet.2014.01.004 · Cited by 238 · see citing works
“In addition, these studies focus on different layers of their ata depending on the information domain that is relevant to heir research question; for instance, some focus on the subset of essages about trending topics (Cha et al., 2010), others analyse ontent related to commercial products (Jansen et al.”
BackgroundResultInfluentialDiffusion of Lexical Change in Social Media
Eisenstein, J., O'Connor, B., Smith, N. A., & Xing, E. P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0113114 · Cited by 245 · see citing works
“Such work has generally focused on tracking the spread of discrete behaviors, such as using a piece of software [16], reposting duplicate or nearduplicate content [10,20,21], voting in political elections [17], or”
BackgroundDengue surveillance based on a computational model of spatio-temporal locality of Twitter
Gomide, J., Veloso, A., Meira, W., Almeida, V. A. F., Benevenuto, F., Ferraz, F., & Teixeira, M. M. (2011) · ACM Web Science Conference (WebSci) · doi:10.1145/2527031.2527049 · Cited by 243 · see citing works
“The first set of tweets was obtained from a previous measurement study that collected the complete history of tweets posted by all users as of July 2009 [6].”
MethodSelf-citationDo CSR Messages Resonate? Examining Public Reactions to Firms’ CSR Efforts on Social Media
Saxton, G. D., Gomez, L., Ngoh, Z., Lin, Y.-P., & Dietrich, S. (2017) · Journal of Business Ethics · doi:10.1007/s10551-017-3464-z · Cited by 250 · see citing works
“…general approval of the message and commenting on and replying to messages reflects dialogic engagement and the ability to generate conversation (Cha et al. 2010; Saxton and Waters 2014), these two actions are more ‘‘personal,’’ as the reactions do not substantially widen the message…”
Detecting misinformation in online social networks using cognitive psychology
Kumar, K. P. K., & Geethakumari, G. (2014) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-014-0014-x · Cited by 242 · see citing works
Die Free or Live Hard? Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers
Yang, C., Harkreader, R. C., & Gu, G. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-23644-0_17 · Cited by 235 · see citing works
An evolutionary algorithm approach to link prediction in dynamic social networks
Bliss, C. A., Frank, M. R., Danforth, C. M., & Dodds, P. S. (2014) · Journal of Computational Science · doi:10.1016/j.jocs.2014.01.003 · Cited by 235 · see citing works
“This index was previously used to establish equal amplitude groups in plant sociology based on the similarity of species [16].”
MethodSymeonidis, S., Effrosynidis, D., & Arampatzis, A. (2018) · Expert systems with applications · doi:10.1016/j.eswa.2018.06.022 · Cited by 214 · see citing works
Bae, Y., & Lee, H. (2012) · J. Assoc. Inf. Sci. Technol. · doi:10.1002/asi.22768 · Cited by 224 · see citing works
Characterizing and Detecting Hateful Users on Twitter
Ribeiro, M. H., Calais, P., Santos, Y., Almeida, V. A. F., & Meira, W. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.15057 · Cited by 260 · see citing works
“Previous work suggests that retweets are better than followers to judge users’ influence (Cha et al. 2010).”
BackgroundRanking in evolving complex networks
Liao, H., Mariani, M. S., Medo, M., Zhang, Y., & Zhou, M.-Y. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.05.001 · Cited by 234 · see citing works
Liu, S., Jiang, C., Lin, Z., Ding, Y., Duan, R. X., & Xu, Z. (2015) · Information Sciences · doi:10.1016/j.ins.2015.01.034 · Cited by 221 · see citing works
“[8] p p Network theory Dynamics of user influence across topics and time Wu and Hofman [40] p p Network theory Classifying users using Twitter lists into ‘‘elite’’ and ‘‘ordinary’’ users Paul et al.”
BackgroundMethodGabielkov, M., Ramachandran, A., Chaintreau, A., & Legout, A. (2016) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/2896377.2901462 · Cited by 219 · see citing works
“…exposure benefits from a set of information intermediaries [29, 20], proposed multiple metrics to quantify influence on social media like Twitter [4, 3], proposed models to predict its long term effect [14, 16], and designed algorithms to leverage inherent influence to maximize the success of…”
Poell, T., & Borra, E. (2011) · Journalism · doi:10.1177/1464884911431533 · Cited by 216 · see citing works
Ranking in evolving complex networks
Liao, H., Mariani, M. S., Medo, M., Zhang, Y., & Zhou, M. (2017) · reroDoc Digital Library · Cited by 221 · see citing works
A review on social spam detection: Challenges, open issues, and future directions
Rao, S., Verma, A. K., & Bhatia, T. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115742 · Cited by 225 · see citing works
Yang, L., Sun, T., Zhang, M., & Mei, Q. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187872 · Cited by 211 · see citing works
“Indeed, it has been proved in literature that while the number of followers sheerly represents a user’s popularity, the following relationship reveals little about the social influence of a user [4].”
BackgroundInfluentialAnalyzing and predicting viral tweets
Jenders, M., Kasneci, G., & Naumann, F. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2487788.2488017 · Cited by 206 · see citing works
“Another work analyzes the influence of users by measuring three different parameters: the number of followers, the number of retweets, and the number of mentions [5].”
BackgroundThe bursty dynamics of the Twitter information network
Myers, S. A., & Leskovec, J. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2566486.2568043 · Cited by 206 · see citing works
“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…”
BackgroundUnderstanding latent interactions in online social networks
Jiang, J., Wilson, C., Wang, X., Huang, P., Sha, W., Dai, Y., & Zhao, B. Y. (2010) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1879141.1879190 · Cited by 205 · see citing works
“Our Renren dataset is larger than most previously studied OSN datasets, the exceptions being recent measurements of the Twitter network [Cha et al. 2010; Kwak et al. 2010].”
MethodResultInfluentialSocial influence modeling using information theory in mobile social networks
Peng, S., Yang, A., Cao, L., Yu, S., & Xie, D. (2017) · Information Sciences · doi:10.1016/j.ins.2016.08.023 · Cited by 200 · see citing works
Predicting Information Spreading in Twitter
Zaman, T. R., Herbrich, R., Gael, J. V., & Stern, D. (2010) · Cited by 203 · see citing works
Social Media Impact on Holiday Travel Planning
Fotis, J., Buhalis, D., & Rossides, N. (2011) · International Journal of Online Marketing · doi:10.4018/ijom.2011100101 · Cited by 203 · see citing works
Understanding the influence of all nodes in a network
Lawyer, G. (2015) · Scientific Reports · doi:10.1038/srep08665 · Cited by 201 · see citing works
“No comparison is made to pagerank because its rankings are highly dependent on the damping factor, including frequent rank reversals after small changes [15, 16], and as it is also unstable in the face of small pertubations in network topology [17].”
Twitter sentiment analysis: Capturing sentiment from integrated resort tweets
Philander, K. S., & Zhong, Y. (2016) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2016.02.001 · Cited by 201 · see citing works
Identifying Infection Sources and Regions in Large Networks
Luo, W., Tay, W. P., & Leng, M. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2256902 · Cited by 200 · see citing works
“A modern social network like Twitter have millions of active users [1].”
Background
- 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 › Sociology and Political Science › 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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