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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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Cites
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Cited by
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Items 101–200 of 1,007
Saying ‘sorry’: Corporate apologies posted on Twitter
Page, R. (2014) · Journal of Pragmatics · doi:10.1016/j.pragma.2013.12.003 · Cited by 214
Characterizing Urban Landscapes Using Geolocated Tweets
Frías-Martínez, V., Soto, V., Hohwald, H., & Frı́as-Martı́nez, E. (2012) · International Conference on Social Computing · doi:10.1109/socialcom-passat.2012.19 · Cited by 202
“Researchers have explored how information propagates geographically [1], [ 2 have quantified influence across geographical areas [3], [4] , have modelled trending topics in specific urban environment s [5], and have studied the topological characteristics of th e social networks that location-based services might crea…”
BackgroundInformation transfer in social media
Steeg, G. V., & Galstyan, A. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187906 · Cited by 203
“[5] Meeyoung Cha, Hamed Haddadi, Fabrcio Benevenuto, and Krishna P.”
BackgroundDeterminants of information retweeting in microblogging
Liu, Z., Liu, L., & Li, H. (2012) · Internet Research · doi:10.1108/10662241211250980 · Cited by 207
Using Twitter for breast cancer prevention: an analysis of breast cancer awareness month
Thackeray, R., Burton, S. H., Giraud-Carrier, C. G., Rollins, S. J., & Draper, C. R. (2013) · BMC Cancer · doi:10.1186/1471-2407-13-508 · Cited by 196
“In assessing the degree of influence among Twitter users, as measured by indegree (number of followers), retweets and mentions, researchers found that persons with many followers, such as celebrities, were not as influential as others who are mentioned or retweeted [39].”
BackgroundA new direction in social network analysis: Online social network analysis problems and applications
Can, Ü., & Alataş, B. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122372 · Cited by 207
Gatekeeping Twitter: message diffusion in political hashtags
Bastos, M. T., Raimundo, R. L. G., & Travitzki, R. (2013) · Media Culture & Society · doi:10.1177/0163443712467594 · Cited by 192
“Moreover, Twitter literature found the amount of retweets to be the most effective technique for identifying intermediaries that transmit information to other users (Boyd et al., 2010; Cha et al., 2010; Kwak et al., 2010; Mendoza et al., 2010; Suh et al., 2010).”
BackgroundPredicting Successful Memes Using Network and Community Structure
Weng, L., Menczer, F., & Ahn, Y. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14530 · Cited by 197
“As many studies on social influence have assumed, the neighbors of an individual in the network can be considered as their potential audience (Kitsak et al. 2010; Cha et al. 2010; Suh et al. 2010; Bakshy et al. 2011).”
BackgroundInfluentialGhosh, S., Sharma, N. K., Benevenuto, F., Ganguly, N., & Gummadi, K. P. (2012) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2348283.2348361 · Cited by 190
“There have been several attempts to measure the influence of Twitter users and hence to identify influential users or experts [3, 4, 8, 12].”
BackgroundMethodSelf-citationIncorporating Query Expansion and Quality Indicators in Searching Microblog Posts
Massoudi, K., Tsagkias, M., de Rijke, M., & Weerkamp, W. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20161-5_36 · Cited by 189
“Finally, [2] study the types and degrees of influence within the Twitter network and find that the number of followers represents a user’s popularity, but it is not related to retweets and mentions; the latter two indicate the content value of a microblog post and the value of a user’s name.”
BackgroundUsher, N., Holcomb, J., & Littman, J. (2018) · The International Journal of Press/Politics · doi:10.1177/1940161218781254 · Cited by 187
Levina, N., & Arriaga, M. (2014) · Information Systems Research · doi:10.1287/isre.2014.0535 · Cited by 201
“For example, it was observed that in-degree (number of users linking to the focal user’s profile) on Twitter does not correlate well with re-tweets (forwarding of messages), suggesting that something other than network position may be driving influence (Cha et al. 2010).”
BackgroundDRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks
Wang, B., Chen, G., Fu, L., Song, L., & Wang, X. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2728064 · Cited by 183
Deriving market intelligence from microblogs
Li, Y.-M., & Li, T.-Y. (2013) · Decision Support Systems · doi:10.1016/j.dss.2013.01.023 · Cited by 182
“In [7], the authors introduce three indicators: mention influence, follow influence, and retweet influence.”
BackgroundEmoticon Style: Interpreting Differences in Emoticons Across Cultures
Park, J., Barash, V., Fink, C., & Cha, M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14437 · Cited by 181
“Methodology Twitter Data We use a corpus of the Twitter data in (Cha et al. 2010) from 2006 to 2009, which contains information about 54 million users and all of their public posts.”
MethodSelf-citationSocial influence and unfollowing accelerate the emergence of echo chambers
Sasahara, K., Chen, W., Peng, H., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-020-00084-7 · Cited by 211
“This value is within the range observed in the literature [11, 17].”
Prediction of retweet cascade size over time
Kupavskii, A. B., Prokhorenkova, L., Umnov, A. V., Usachev, S., Serdyukov, P., Gusev, G., & Kustarev, A. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398634 · Cited by 178
“Suppose you spread an advertisement and you want to get a sufficient volume (e.g. 1000 retweets) within a day.”
BackgroundQuantifying Information Overload in Social Media and Its Impact on Social Contagions
Gomez-Rodriguez, M., Gummadi, K. P., & Schölkopf, B. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14549 · Cited by 203
“We use data gathered from Twitter as reported in previous work (Cha et al. 2010), which comprises the following three types of information: profiles of 52 million users, 1 .”
MethodSelf-citationDissecting a Social Botnet: Growth, Content and Influence in Twitter
Abokhodair, N., Yoo, D., & McDonald, D. W. (2015) · Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing · doi:10.1145/2675133.2675208 · Cited by 162
“We relied on retweet data to begin a social network analysis because that action is more indicative of attention to content of the tweet [4].”
MethodÖztamur, D., & Karakadılar, İ. S. (2014) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2014.09.067 · Cited by 176
“Second, the power-law trend in the difference among influence of individuals indicates that it is substantially more effective to target the top influentials than to employ a massive number of non-popular users in order to kick start a viral campaign (Cha et al., 2010).”
BackgroundHustle and Brand: The Sociotechnical Shaping of Influence
Carter, D. (2016) · Social Media + Society · doi:10.1177/2056305116666305 · Cited by 189
Yoo, E., Rand, W. M., Eftekhar, M., & Rabinovich, E. (2016) · Journal of Operations Management · doi:10.1016/j.jom.2016.05.007 · Cited by 178
“To measure a cascade originator's in fl u-ence, we followed Cha et al. (2010), who explained that an agent is in fl uential when it acts as an information channel to a large audience.”
BackgroundOh, J., Bellur, S., & Sundar, S. (2018) · Communication Research · doi:10.1177/0093650215600493 · Cited by 161
“For instance, marketing and advertising professionals have used the concept of user engagement to illustrate how electronic word-of-mouth (e-WoM) campaigns have the potential to spawn widespread discussions and debates in social media settings such as Facebook and Twitter (Falls, 2013; Hanna, Rohm, & Crittenden, 2011).”
BackgroundFollow the green: growth and dynamics in twitter follower markets
Stringhini, G., Wang, G., Egele, M., Kruegel, C., Vigna, G., Zheng, H., & Zhao, B. Y. (2013) · Proceedings of the 2013 conference on Internet measurement conference · doi:10.1145/2504730.2504731 · Cited by 169
“As Twitter use grows, the influence and reputation of a person or business entity are increasingly associated with their number of Twitter followers [8,18].”
BackgroundDissecting a Social Botnet: Growth, Content and Influence in TwitterPreprint
Abokhodair, N., Yoo, D., & McDonald, D. W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.03627 · Cited by 169
“We relied on retweet data to begin a social network analysis because that action is more indicative of attention to content of the tweet [4].”
MethodAddressing cold-start in app recommendation
Lin, J., Sugiyama, K., Kan, M., & Chua, T. (2013) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2484028.2484035 · Cited by 166
“We note that by following an app’s Twitter handle, the Twitter-followers subscribe to the tweets that are related to the particular app, which can be seen as an indicator of interest [9].”
BackgroundResultWilson, C., Sala, A., Puttaswamy, K. P. N., & Zhao, B. Y. (2012) · ACM Transactions on the Web · doi:10.1145/2382616.2382620 · Cited by 171
An Advanced Systematic Literature Review on Spatiotemporal Analyses of T witter Data
Steiger, E., de Albuquerque, J. P., & Zipf, A. (2015) · Transactions in GIS · doi:10.1111/tgis.12132 · Cited by 165
“Cha et al. (2010) measure individual user influences on topics by analyzing user tweet and retweet behavior. Weng et al. (2010) also study on estimating influence of distinct user calculating and ranking topic similarities with LDA and the relationship structure (friend, follower etc.”
BackgroundMethodInfluentialA Bayesian approach for predicting the popularity of tweets
Zaman, T. R., Fox, E. B., & Bradlow, E. T. (2014) · The Annals of Applied Statistics · doi:10.1214/14-aoas741 · Cited by 164
“Based on previous empirical studies [Zhou et al. (2010), Cha et al. (2010)], this maximum number of retweets covers a large fraction of tweets in Twitter and does not represent a significant limitation.”
BackgroundResultKwon, S. J., Park, E., & Kim, K. J. (2014) · The Social Science Journal · doi:10.1016/j.soscij.2014.04.005 · Cited by 180
Hopcroft, J. E., Lou, T., & Tang, J. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2063740 · Cited by 163
“Work of this category mainly focus on identifying influential users in Twitter [30, 3, 13] or examining and predicting tweeting behaviors of users [10, 25].”
BackgroundAl‐Rawi, A. (2017) · Digital Journalism · doi:10.1080/21670811.2017.1387062 · Cited by 162
“It is important to note here that previous research shows that having a high number of online followers does not necessarily secure a large number of retweets or mentions (Cha et al. 2010).”
ResultLiu, Y., Tang, M., Zhou, T., & Do, Y. (2015) · Scientific Reports · doi:10.1038/srep13172 · Cited by 165
Weibo network, information diffusion and implications for collective action in China
Huang, R., & Sun, X. (2013) · Information Communication & Society · doi:10.1080/1369118x.2013.853817 · Cited by 161
Mourad, A., Srour, A., Harmanani, H., Jenainati, C., & Arafeh, M. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3031034 · Cited by 162
Understanding news sharing in social media
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Dai, E., Zhao, T., Zhu, H., Xu, J., Guo, Z., Liu, H., Tang, J., & Wang, S. (2024) · Machine Intelligence Research · doi:10.1007/s11633-024-1510-8 · Cited by 257
Styles of Social Media Campaigning and Influence in a Hybrid Political Communication System
Karlsen, R., & Enjolras, B. (2016) · The International Journal of Press/Politics · doi:10.1177/1940161216645335 · Cited by 158
“For example, Cha et al. (2010) compared three measures of influence: in-degree (number followers), retweets (number of retweets containing the user’s name), and mentions (the degree of engagement with others).”
BackgroundCross-Cultural Comparison of Nonverbal Cues in Emoticons on Twitter: Evidence from Big Data Analysis
Park, J., Baek, Y. M., & Cha, M. (2014) · Journal of Communication · doi:10.1111/jcom.12086 · Cited by 156
“Methodologically, our study clearly shows the potential problems of conventional big data analyses that focused on descriptive and bivariate statistics such as correlation coefficients (e.g., Cha et al., 2010; Kwak, Lee, Park, & Moon, 2010; Miller, 2011; Thelwall, Buckley, & Paltoglou, 2011).”
BackgroundSelf-citationRisk perception and communication in vaccination decisions: A fuzzy-trace theory approach
Reyna, V. F. (2011) · Vaccine · doi:10.1016/j.vaccine.2011.11.070 · Cited by 167
A diffusion mechanism for social advertising over microblogs
Li, Y.-M., & Shiu, Y.-L. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.02.012 · Cited by 158
“[8] indicate that the content value of one's posts is an important measure for evaluating influence.”
BackgroundMeasuring User Credibility in Social Media
Abbasi, M.-A., & Liu, H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37210-0_48 · Cited by 155
“[7] use three approaches (indegree, retweet, and mention) to measure users’ importance in Twitter.”
MethodBode, L., & Dalrymple, K. E. (2014) · Journal of Political Marketing · doi:10.1080/15377857.2014.959686 · Cited by 178
“This finds support in other studies which have suggested that actual influence on Twitter does not only come from users with the highest number of followers (Cha et al. 2010).”
BackgroundResultInfluentialThe Anatomy of Large Facebook Cascades
Dow, P. A., Adamic, L. A., & Friggeri, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14431 · Cited by 155
“The modest correlation between a node’s audience and its cascade influence is consistent with prior findings on Twitter (Cha et al. 2010; Romero et al. 2011).”
ResultInfluentialThe World of Connections and Information Flow in Twitter
Cha, M., Benevenuto, F., Haddadi, H., & Gummadi, K. P. (2012) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2012.2183359 · Cited by 154
“We have used this data set in a number of recent efforts, such as to study user influence [2], information propagation [10], and spam [11].”
MethodSelf-citationPredicting Opinion Leaders in Twitter Activism Networks
Xu, W. W., Sang, Y., Blasiola, S., & Park, H. W. (2014) · American Behavioral Scientist · doi:10.1177/0002764214527091 · Cited by 169
“This ability has two dimensions: the ability to contribute information (Weimann, Tustin, Van Vuuren, & Joubert, 2007) and to lead others in disseminating information (Cha et al., 2010; Phelps, Lewis, Mobilio, Perry, & Raman, 2004).”
BackgroundDigital Methods: Five Challenges
Rieder, B. (2012) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230371934_4 · Cited by 154
The Geospatial Characteristics of a Social Movement Communication Network
Conover, M., Davis, C. A., Ferrara, E., McKelvey, K., Menczer, F., & Flammini, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055957 · Cited by 155
“Among others, it has been used to study influence and credibility [22–26], social structure [27–29] and to monitor users’ sentiment [30–33].”
BackgroundGeographic Dissection of the Twitter Network
Kulshrestha, J., Kooti, F., Nikravesh, A., & Gummadi, K. P. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14280 · Cited by 151
“Twitter dataset: We used the Twitter dataset described in (Cha et al. 2010).”
BackgroundMethodInfluentialSelf-citationHogg, T., & Lerman, K. (2012) · EPJ Data Science · doi:10.1140/epjds5 · Cited by 151
Cascading behaviour in complex socio-technical networks
Borge‐Holthoefer, J., Baños, R. A., González‐Bailón, S., & Moreno, Y. (2013) · Journal of Complex Networks · doi:10.1093/comnet/cnt006 · Cited by 157
“A first remarkable finding is that influence should not be simply mapped to connectivity [89]; authority is gained through specialization and concerted efforts to limit communication activity to a single topic.”
BackgroundZhong, E., Fan, W., Wang, J., Xiao, L., & Li, Y. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339641 · Cited by 149
“Most previous works of social influence focus on how to measure the social influence [2] or maximize the user influence [4, 8].”
BackgroundAraujo, T., Neijens, P. C., & Vliegenthart, R. (2016) · International Journal of Advertising · doi:10.1080/02650487.2016.1173765 · Cited by 149
Finding Credible Information Sources in Social Networks Based on Content and Social Structure
Canini, K. R., Suh, B., & Pirolli, P. L. T. (2011) · International Conference on Social Computing · doi:10.1109/passat/socialcom.2011.91 · Cited by 148
Social science in the era of big data
González‐Bailón, S. (2013) · Policy & Internet · doi:10.1002/1944-2866.poi328 · Cited by 139
“…who are in a better position to spread their messages regardless of content, are not necessarily the most important users when it comes to spreading domain-specific information, as in a protest (Cha, Haddadi, Benevenuto, & Gummadi, 2010; González-Bail ó n, Borge-Holthoefer, & Moreno, forthcoming).”
On word-of-mouth based discovery of the web
Rodrigues, T., Benevenuto, F., Cha, M., Gummadi, K. P., & Almeida, V. A. F. (2011) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2068816.2068852 · Cited by 141
Self-citationCultural Evolution in the Digital Age
Acerbi, A. (2019) · doi:10.1093/oso/9780198835943.001.0001 · Cited by 143
TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems
Cao, N., Shi, C., Lin, S. C.-C., Lu, J., Lin, Y., & Lin, C. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467196 · Cited by 144
“[7] introduced a formal measurements of a user’s influence in Twitter.”
BackgroundCascading outbreak prediction in networks
Cui, P., Jin, S., Yu, L., Wang, F., Zhu, W., & Yang, S. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487639 · Cited by 139
“Recently, Cha et al. [3] empirically analyzed the social in fl uence in Twitter from di ff erent angles such as network topology and user behaviors, and drew the conclusion that topological measures alone reveal very little about in fl uence of a user.”
BackgroundAnalyzing the Dynamic Evolution of Hashtags on Twitter: a Language-Based Approach
Cunha, E. L. T. P., Magno, G., Comarela, G., Almeida, V. A. F., Gonçalves, M. A., & Benevenuto, F. (2011) · Cited by 139
Self-citationCao, C. C., She, J., Tong, Y., & Chen, L. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2350229.2350264 · Cited by 139
“Previous work [5] has adopted retweet measurement for influence analysis on Twitter.”
MethodTwitter financial community sentiment and its predictive relationship to stock market movement
Yang, S. Y., Mo, S. Y. K., & Liu, A. (2015) · Quantitative Finance · doi:10.1080/14697688.2015.1071078 · Cited by 136
“all users are equally influential in the social media, and those influential social media users will certainly have higher impact than the societal mood or sentiment (Cha et al. 2010).”
Reverse Engineering Socialbot Infiltration Strategies in Twitter
Freitas, C. A., Benevenuto, F., Ghosh, S., & Veloso, A. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2809292 · Cited by 138
“(1) Followers acquired by the socialbot: We count the number of followers acquired by the socialbot, which is a standard metric for estimating the popularity / influence of users in the Twitter social network [6].”
Self-citationVora, K., Gupta, R., & Xu, G. H. (2017) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3037697.3037748 · Cited by 138
“Friendster (FT) [13] 2.5B 68.3M Twitter (TT) [6] 2.0B 52.6M Twitter (TTW) [20] 1.5B 41.7M UKDomain (UK) [5] 1.0B 39.5M LiveJournal (LJ) [3] 69M 4.8M Combining Case 1 and 2, it is clear to see that there does not exist any such vertex c whose value can become unsafe and flow to v under our algorithm.”
BackgroundBenthaus, J., Risius, M., & Beck, R. (2016) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2015.12.001 · Cited by 143
“Influencing the perception of the public about a company can be achieved by identifying most influential members of a SMP and positively influence them to gain them subsequently as a public advocate for the company (Cha et al., 2010).”
BackgroundUsing Twitter to Predict the Stock Market
Nofer, M., & Hinz, O. F. (2015) · Business & Information Systems Engineering · doi:10.1007/s12599-015-0390-4 · Cited by 136
“The number of Twitter followers has frequently been used as a measure for influence and popularity within the community (e.g., Cha et al. 2010; Kwak et al. 2010).”
MethodTopic-based content and sentiment analysis of Ebola virus on Twitter and in the news
Kim, H.-J., Jeong, Y. K., Kim, Y., Kang, K. Y., & Song, M. (2015) · Journal of Information Science · doi:10.1177/0165551515608733 · Cited by 137
“Statistical approaches for measuring parameters such as number of tweets, number of followers and number of retweets are adopted in most studies [19, 20].”
Iosup, A., Hegeman, T., Ngai, W. L., Heldens, S., Prat-Pèrez, A., Manhardto, T., Chafio, H., Capotă, M., Sundaram, N., Anderson, M. C., et al. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/3007263.3007270 · Cited by 136
Analyzing user behavior of the micro-blogging website Sina Weibo during hot social events
Guan, W., Gao, H., Yang, M., Li, Y., Ma, H., Qian, W., Cao, Z., & Yang, X. (2013) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2013.09.059 · Cited by 135
“factors: the total numbers of user’s followers, followees and weibos. It is shown that the Spearman correlation coefficients are extremely low. This finding is different from previous conclusions [24][31, 32, 33, 34]. Table 3. Correlation coefficients between reposting times and three other variables. Guo 723 Yao Liby…”
ResultInfluentialYuan, X., Schuchard, R., & Crooks, A. T. (2019) · Social Media + Society · doi:10.1177/2056305119865465 · Cited by 154
“…research to explore whether “influential” users are more of those with high degree centrality or those who they agree, and thus understand what defines a user to be influential in the context of anti-vaccination or other highly divided online discussions (Cha, Haddadi, Benevenuto, & Gummadi, 2010).”
BackgroundTwitter reciprocal reply networks exhibit assortativity with respect to happiness
Bliss, C. A., Kloumann, I. M., Harris, K. D., Danforth, C. M., & Dodds, P. S. (2012) · Journal of Computational Science · doi:10.1016/j.jocs.2012.05.001 · Cited by 135
“Keywords: Social networks, Sentiment tracking, Collective mood, Emotion, Hedonometrics”
BackgroundToward A Model of Meme Diffusion (M 3 D)
Spitzberg, B. H. (2014) · Communication Theory · doi:10.1111/comt.12042 · Cited by 129
Who creates Trends in Online Social Media: The Crowd or Opinion Leaders?
Zhang, L., Zhao, J., & Xu, K. (2015) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12145 · Cited by 131
“With the help of influentials, large-scale popularity might be achieved at an extremely low cost (Katz, 1957; Gladwell, 2002; Flynn et al., 1996; Dholakia et al., 2004; Iyengar et al., 2011; Cha et al., 2010; Wu et al., 2014; Kempe et al., 2005; Trusov et al., 2010).”
BackgroundInfluentialTwitter as a tool for forecasting stock market movements: A short-window event study
Nisar, T. M., & Yeung, M. W. (2018) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2017.11.002 · Cited by 131
Media Landscape in Twitter: A World of New Conventions and Political Diversity
An, J., Cha, M., Gummadi, K. P., & Crowcroft, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14118 · Cited by 130
“The Twitter dataset We used the Twitter data gathered from our previous work (Cha et al. 2010), which comprises the following three types of information: profiles of 54M users, 1.”
MethodSelf-citationAnalyzing and predicting news popularity on Twitter
Wu, B., & Shen, H. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2015.07.003 · Cited by 132
Vaccari, C., & Valeriani, A. (2013) · New Media & Society · doi:10.1177/1461444813511038 · Cited by 128
“Even ordinary users sometimes manage to gain niches of authority (Cha et al., 2010) and acquire “micro-celebrity” status (see Page, 2012; Senft, 2008).”
BackgroundRandom Sampling over Joins Revisited
Zhao, Z., Christensen, R. R., Li, F., Hu, X., & Yi, K. (2018) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3183713.3183739 · Cited by 128
“The second data set is a collection of twitter friendship links and user profiles used in [7].”
BackgroundHow to Identify an Infection Source With Limited Observations
Luo, W., Tay, W. P., & Leng, M. (2014) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2014.2315533 · Cited by 127
The Social World of Twitter: Topics, Geography, and Emotions
Quercia, D., Capra, L., & Crowcroft, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14254 · Cited by 126
“We find that social brokers in Twitter are opinion leaders who take the risk of tweeting about different topics (influential Twit-terers tend to be specialized in specific topics instead (Cha et al. 2010)).”
BackgroundCombating Rumor Spread on Social Media: The Effectiveness of Refutation and Warning
Ozturk, P., Li, H., & Sakamoto, Y. (2015) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2015.288 · Cited by 126
“There are many factors that influence the spread of information on social media, including social network structure [31, 32, 33, 34, 35, 36, 37, 38], the content of the information [39, 40, 41], and how people perceive the information [2, 7, 8, 9, 10, 42, 43, 44].”
BackgroundAnalyzing temporal dynamics in Twitter profiles for personalized recommendations in the social web
Abel, F., Gao, Q., Houben, G., & Tao, K. (2011) · ACM Web Science Conference (WebSci) · doi:10.1145/2527031.2527040 · Cited by 125
“Lately, researchers study the network structures that evolve from those user interactions on Twitter [4, 13] and investigate how information propagates through the Twitter network [9, 10].”
BackgroundThe tweets they are a-changin': Evolution of twitter users and behavior
Liu, Y., Kliman-Silver, C., & Mislove, A. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14508 · Cited by 126
“, 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.”
BackgroundMethodInfluentialStarbucks’ marketing communications strategy on Twitter
Taecharungroj, V. (2016) · Journal of Marketing Communications · doi:10.1080/13527266.2016.1138139 · Cited by 126
“…content; other metrics, such as sales and brand performance, are arguably equally, if not more, important. nevertheless, scholars have used the number of retweets to represent influence, popularity, and interactivity (Cha et al. 2010; kwak et al. 2010; Bae and lee 2011; Burton and Soboleva 2011).”
BackgroundGilbert, É., Bakhshi, S., Chang, S., & Terveen, L. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2481336 · Cited by 134
“For example, Cha [10] computed three different measures of influence in Twitter—in-degree in the Twitter social network, retweets of content, and mentions in tweets—and then examined the extent to which the different measures were correlated.”
BFS and Coloring-Based Parallel Algorithms for Strongly Connected Components and Related Problems
Slota, G. M., Rajamanickam, S., & Madduri, K. (2014) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2014.64 · Cited by 124
“Friendster, LiveJournal, Orkut, and Twitter are crawls of social networks [30], [31], [32].”
BackgroundContent or context: Which matters more in information processing on microblogging sites
Zhang, L., Peng, T., Zhang, Y.-P., Wang, X.-H., & Zhu, J. J. H. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.10.031 · Cited by 132
How social information can improve estimation accuracy in human groups
Jayles, B., Kim, H.-R., Escobedo, R., Cezera, S., Blanchet, A., Kameda, T., Sire, C., & Théraulaz, G. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1703695114 · Cited by 120
Understanding latent interactions in online social networks
Jiang, J., Wilson, C., Wang, X., Sha, W., Huang, P., Dai, Y., & Zhao, B. Y. (2013) · ACM Transactions on the Web · doi:10.1145/2517040 · Cited by 128
“Previous work has observed similar artifacts when analyzing all users in a large OSN dataset [Cha et al. 2010].”
MethodResultInfluentialIdentifying topical influencers on twitter based on user behavior and network topology
Alp, Z. Z., & Öğüdücü, Ş. G. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.11.021 · Cited by 129
Poblete, B., García, R. A. D. F., Mendoza, M., & Jaimes, A. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2063724 · Cited by 125
What's congress doing on twitter?
Hemphill, L., Otterbacher, J., & Shapiro, M. A. (2013) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2441776.2441876 · Cited by 123
“Using social network analysis to examine Twitter relationships and behaviors is increasingly common [4, 12, 51, 52], and our future work will explore public officials’ social media networks.”
BackgroundDoughty, M., Rowland, D., & Lawson, S. W. (2012) · European Conference on Interactive TV and Video (EuroITV) · doi:10.1145/2325616.2325635 · Cited by 120
Chhabra, S., Aggarwal, A., Benevenuto, F., & Kumaraguru, P. (2011) · Conference on Email and Anti-Spam · doi:10.1145/2030376.2030387 · Cited by 120
“using tweets collected during 2008-2009 [2, 5], we filtered 3,692 tweets containing shortened phishing URLs using the PhishTank data 20082009.”
MethodSelf-citationSocial Media Polarization and Echo Chambers in the Context of COVID-19: Case Study
Jiang, J., Ren, X., & Ferrara, E. (2021) · JMIRx Med · doi:10.2196/29570 · Cited by 162
“Retweeting is the best indication of active endorsement [22] and is commonly used as the best proxy for gauging popularity and virality on Twitter [57].”
BackgroundWho will lead and who will follow: Identifying Influential Users in Online Social Networks
Probst, F., Grosswiele, L., & Pfleger, R. (2013) · Business & Information Systems Engineering · doi:10.1007/s12599-013-0263-7 · Cited by 123
Looking for the perfect tweet. The use of data mining techniques to find influencers on twitter
Lahuerta-Otero, E., & Cordero-Gutiérrez, R. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.07.035 · Cited by 118
Ben‐Nun, T., Sutton, M. M., Pai, S., & Pingali, K. K. (2017) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/3018743.3018756 · Cited by 118
FERRARI: Flexible and efficient reachability range assignment for graph indexing
Seufert, S., Anand, A., Bedathur, S., & Weikum, G. (2013) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2013.6544893 · Cited by 115
Al-Garadi, M. A., Varathan, K. D., & Ravana, S. D. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.11.002 · Cited by 115
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.
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