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Measuring User Influence in Twitter: The Million Follower Fallacy
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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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Measuring and profiling the topical influence and sentiment contagion of public event stakeholders
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“It has been shown that there are regularities in the use of these usage practices (Huberman et al., 2009; Cha et al., 2010).”
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Retweet or like? That is the question
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Users structure and behavior on an online social network during a political protest
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Validating viral marketing strategies in Twitter via agent-based social simulation
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Discovery, Retrieval, and Analysis of the 'Star Wars' Botnet in Twitter
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“Twitter has been the subject of intensive study in recent years, including the graph structure of Twitter users [3] and ways to measure the influence of a Twitter user [4].”
BackgroundPredicting emerging social conventions in online social networks
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“We obtained the Twitter data set from [14], which comprises the following three types of information: profiles of 54 million users, 1.”
MethodSelf-citationUnderstanding Information Credibility on Twitter
Sikdar, S., Kang, B., O’Donovan, J., Höllerer, T., & Adah, S. (2013) · International Conference on Social Computing · doi:10.1109/socialcom.2013.9 · Cited by 55
“Prior work has used retweets as a proxy of the sender’s influence on the retweeter [11], and of credibility based on the number of retweets [7].”
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Chen, Y., Tang, K., Wu, C., & Jheng, R.-Y. (2014) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2014.10.001 · Cited by 55
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Visualizing Media Bias through Twitter
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“We obtained the Twitter data published in a previous work (Cha et al. 2010), which comprises the following three types of information: profiles of 54M users, 1.”
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“Existing studies (Cassell et al., 2006; Cha et al., 2010; Huffaker, 2010; Kim et al., 2014) indicate that greater retweetability of messages signifies social influence because these messages drive, at a minimum, public responses on social media.”
BackgroundMining the interests of Chinese microbloggers via keyword extraction
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“Many traditional or new research topics are explored based on Twitter, such as event detection and tracking [34,38–42], measuring user in fl uence in Twitter [43], sentiment analysis [44–48], recommendations [49], and Hashtag analysis [50,51].”
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“Just as mentioned above, even though various theories and models have been proposed [23, 12, 13, 3, 26, 30, 21, 9, 24, 33, 15, 14, 6, 2, 27], improvement is still needed.”
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BackgroundPhishing through social bots on Twitter
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Twitter, un nouveau « baromètre de l'opinion publique » ?
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Wisdom of the Crowd or Wisdom of a Few?
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BackgroundSocial influence promotes cooperation in the public goods game
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PuLP: Scalable multi-objective multi-constraint partitioning for small-world networks
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“While the roles of sources, opinion leaders, and audience are dynamic in the new media environment, empirical findings show that mainstream news organizations can hold considerable influence across a variety of topics (Cha et al., 2010).”
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BackgroundResultInfluentialMore Accounts, Fewer Links: How Algorithmic Curation Impacts Media Exposure in Twitter Timelines
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“Other studies have used similar approximations to influence, alongside alternatives such as retweets and mentions [7, 18].”
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“23], Twitter [37, 26] and other popular OSNs [42] have been carefully analyzed.”
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“[24] performed a comparison of three different measures of influence: indegree, retweet, and mention.”
BackgroundLai, C., Lee, S., & Huang, H.-L. (2018) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2018.04.002 · Cited by 50
Identifying Top- k Nodes in Social Networks
Bian, R., Koh, Y. S., Dobbie, G., & Divoli, A. (2019) · ACM Computing Surveys · doi:10.1145/3301286 · Cited by 50
“Cha et al. (2010) compares three different measures of influence in Twitter (number of followers, number of retweets, and number of mentions) and finds that the most followed users do not necessarily score highest on the other two measures.”
BackgroundAspects of Rumor Spreading on a Microblog Network
Kwon, S., Cha, M., Jung, K., Chen, W., & Wang, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03260-3_26 · Cited by 48
“We use data crawled from Twitter as explained in previous work [5].”
MethodSelf-citationHa, S., & Ahn, J. (2011) · International Conference on Information Systems · Cited by 49
An Image Is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures
Schifanella, R., Реди, М., & Aiello, L. M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14612 · Cited by 49
“On Twitter, users who generate viral posts are those who limit their tweets to a single topic (Cha et al. 2010).”
BackgroundTrueTop: A Sybil-Resilient System for User Influence Measurement on Twitter
Zhang, J., Zhang, R., Sun, J., Zhang, Y., & Zhang, C. (2015) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2015.2494059 · Cited by 48
“Common to these research proposals [1]–[4] and commercial tools is to capture the qualitative feature of online influence as “the ability to cause effect, change behavior, and drive measurable outcomes online” [5] and to quantify a twitterer’s online influence based on his/her interactions with others.”
BackgroundMining Cross-network Association for YouTube Video Promotion
Yan, M., Sang, J., & Xu, C. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654920 · Cited by 50
“Previous analysis on Twitter has found that popular users with high in-degree are not necessarily influencers for propagation [14], which calls for research onto the problem of influencer mining.”
BackgroundWhat do academic libraries tweet about, and what makes a library tweet useful?
Stvilia, B., & Gibradze, L. (2014) · Library & Information Science Research · doi:10.1016/j.lisr.2014.07.001 · Cited by 49
“Cha et al. (2010) found that having a high number of followers did not necessarily mean the tweeter was influential in terms of the number of mentions and retweets generated.”
BackgroundIdentification of influence in social media communities
Vollenbroek, W., Vries, S., Constantinides, E., & Kommers, P. (2014) · Int. J. Web Based Communities · doi:10.1504/ijwbc.2014.062943 · Cited by 44
MISNIS: An intelligent platform for twitter topic mining
Carvalho, J. P., Rosa, H., Brogueira, G., & Batista, F. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.08.001 · Cited by 49
User influence analysis for Github developer social networks
Hu, Y. Z., Wang, S., Ren, Y., & Choo, K. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.05.002 · Cited by 52
Chmait, N., Westerbeek, H. M., Eime, R., Robertson, S. J., Sellitto, C., & Reid, M. M. (2020) · Telematics and Informatics · doi:10.1016/j.tele.2020.101381 · Cited by 49
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning
Sawhney, R., Joshi, H., Shah, R. R., & Flek, L. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.176 · Cited by 51
“can be influenced by a few "known" users (Cha et al., 2010).”
BackgroundResultProliferation in live streaming commerce, and key opinion leader selection
Lyu, W., Ye, Q., & Liu, J. (2022) · Electronic Commerce Research · doi:10.1007/s10660-022-09605-0 · Cited by 56
“For instance, prior literature uses emotion dictionaries or machine learning methods to identify KOL [77 ].”
An artificial intelligence analysis of climate‐change influencers' marketing on Twitter
Ballestar, M. T., Llaguno, M. M., & Sáinz, J. (2022) · Psychology and Marketing · doi:10.1002/mar.21735 · Cited by 48
Predicting Discussions on the Social Semantic Web
Rowe, M., Angeletou, S., & Alani, H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21064-8_28 · Cited by 46
“al [5] regards attention towards a user as an indicator of influence, they study users’ influence on Twitter by measuring the number of followers, number of retweets and mentions.”
BackgroundRethinking centrality: The role of dynamical processes in social network analysis
Ghosh, R., & Lerman, K. (2014) · Discrete and Continuous Dynamical Systems - B · doi:10.3934/dcdsb.2014.19.1355 · Cited by 55
“Some studies used activity-based measures, such as the number of mentions or re-tweets [14, 3] to identify important social media users.”
BackgroundMethodInfluentialMaruyama, M., Robertson, S. P., Douglas, S., Semaan, B., & Faucett, H. A. (2014) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2531602.2531719 · Cited by 55
Assessing the ripple effects of online opinion leaders with trust and distrust metrics
Kim, Y. S., & Tran, V. L. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.12.058 · Cited by 48
“Noting that no single topological measure alone sufficiently represents the influence of a user (Cha et al., 2010), we present a new hybrid measure of in- and out-degree with varying weights to identify influential opinion leaders and observe its impact on the diffusion process and success of SNM…”
BackgroundPluralisme et infomédiation sociale de l'actualité : le cas de Twitter
Rieder, B., & Smyrnaios, N. (2012) · Réseaux · doi:10.3917/res.176.0105 · Cited by 48
Zhang, N., Campo, S., Janz, K. F., Eckler, P., Yang, J. G., Snetselaar, L. G., & Signorini, A. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2870 · Cited by 48
“Popularity, in turn, is an indicator of potential influence [41].”
BackgroundThe impact of visual attributes on online image diffusion
Totti, L., Costa, F. A., Avila, S., Junior, E. A. D. V., Meira, W., & Almeida, V. A. F. (2014) · ACM Web Science Conference (WebSci) · doi:10.1145/2615569.2615700 · Cited by 48
“Information diffusion on online services is a vastly re-searched topic [3, 4, 8, 9, 27].”
BackgroundSreenivasan, N. D., Lee, C. S., & Goh, D. H. (2012) · Data Technologies and Applications · doi:10.1108/00330331211204548 · Cited by 48
Characterizing and Countering Communal Microblogs During Disaster Events
Rudra, K., Sharma, A., Ganguly, N., & Ghosh, S. (2018) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2018.2802942 · Cited by 48
“Popularity of a user works as a major driving force in determining the popularity of tweets [30].”
BackgroundJournalistic Homophily on Social Media
Hanusch, F., & Nölleke, D. (2018) · Digital Journalism · doi:10.1080/21670811.2018.1436977 · Cited by 48
“Mentions are mostly introduced as a way to directly interact with other users (Russell et al. 2015), engage them in a conversation (Cha et al. 2010), or merely to refer to another user (Conover et al. 2011).”
BackgroundPark, S., & Hoffner, C. A. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106353 · Cited by 48
Adversarial Directed Graph Embedding
Zhu, S., Li, J., Peng, H. M., Wang, S., & He, L. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16605 · Cited by 51
“Specifically, we remove 50% of edges for Cora, Epinions and Google datasets, and 40% of edges for Twitter dataset.”
BackgroundInfluentialTwitter-based user modeling for news recommendations
Abel, F., Gao, Q., Houben, G., & Tao, K. (2013) · Cited by 48
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.
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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