The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“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]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Granovetter, M. S. (1973) · American Journal of Sociology · doi:10.1086/225469 · Cited by 40,491 · cited 1×
“Furthermore , because of the tight connection between users as suggested in the triadic closure (Granovetter 1973), retweeting in a social network can serve as a powerful tool to reinforce a message—for instance, the probability of adopting an innovation increases when not one but a group of users…”
BackgroundRogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · Routledge eBooks · doi:10.4324/9780203710753-35 · Cited by 37,670
Stanton, B. F., & Rogers, E. M. (1963) · Journal of Farm Economics · doi:10.2307/1235767 · Cited by 6,726
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295 · cited 1×
“Google similarly normalizes the data when analyzing their search trends (Ginsberg et al. 2009).”
The Tipping Point: How Little Things Can Make a Big Difference
Meadows-Klue, D. (2004) · Interactive Marketing · doi:10.1057/palgrave.im.4340262 · Cited by 3,064 · cited 3×
“The notion of influence plays a vital role in how businesses operate and how a society functions—for instance, see observations on how fashion spreads (Gladwell 2002) and how people vote (Berry and Keller 2003).”
Mining the network value of customers
Domingos, P., & Richardson, M. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502525 · Cited by 2,884 · cited 2×
“Instead, it posits that the key factors determining influence are (i) the interpersonal relationship among ordinary users and (ii) the readiness of a society to adopt an innovation (Watts and Dodds 2007; Domingos and Richardson 2001).”
BackgroundPersonal Influence: The Part Played by People in the Flow of Mass Communications
Willigan, W. L., Katz, E., & Lazarsfeld, P. F. (1956) · The American Catholic Sociological Review · doi:10.2307/3708755 · Cited by 2,173
The dynamics of viral marketing
Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007) · ACM Transactions on the Web · doi:10.1145/1232722.1232727 · Cited by 2,065
Book Review: Personal Influence: The Part Played by People in the Flow of Mass Communications
Roshwalb, I., Katz, E., & Lazarsfeld, P. F. (1956) · Journal of Marketing · doi:10.2307/1247718 · Cited by 1,900
Influentials, Networks, and Public Opinion Formation
Watts, D. J., & Dodds, P. S. (2007) · Journal of Consumer Research · doi:10.1086/518527 · Cited by 1,864
Research works printed but not linked: 1
- Levy, M., and Windhal, S. 1985. Media Gratications Research, Chapter The Concept of Audience Activity. Sage.
Cited by
Sorted by citations
The Culture of Connectivity: A Critical History of Social Media
van Dijck, J. (2013) · doi:10.1093/acprof:oso/9780199970773.001.0001 · Cited by 2,688
Everyone's an influencer: quantifying influence on twitter
Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935845 · Cited by 1,689
“Consistent with previous work [7, 18, 35], both the in-degree (‘followers”) and out-degree (“friends”) distributions are highly skewed, but the former much more so—whereas the maximum # of followers was nearly 4M, the maximum # of friends was only about 760K—reflecting the passive and one-way nature…”
BackgroundResultEmotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior
Stieglitz, S., & Dang-Xuan, L. (2013) · Journal of Management Information Systems · doi:10.2753/mis0742-1222290408 · Cited by 1,604
Who says what to whom on twitter
Wu, S., Hofman, J. M., Mason, W., & Watts, D. J. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963504 · Cited by 969
“[3] compared three measures of influence—number”
Prominent Features of Rumor Propagation in Online Social Media
Kwon, S., Cha, M., Jung, K., Chen, W., & Wang, Y. (2013) · 2013 IEEE 13th International Conference on Data Mining · doi:10.1109/icdm.2013.61 · Cited by 859
“We used the Twitter data reported in [4], which comprise the profile information of 54 million users, the 1.”
MethodSelf-citationThelwall, M. A., Buckley, K. A., & Paltoglou, G. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21462 · Cited by 817
Cheng, J., Adamic, L. A., Dow, P. A., Kleinberg, J. M., & Leskovec, J. (2014) · Cited by 771
Understanding the Demographics of Twitter Users
Mislove, A. E., Lehmann, S., Ahn, Y., Onnela, J.-P. “., & Rosenquist, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14168 · Cited by 761
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
“structural graph properties in the various kinds of networks that can be constructed from social data (Cha et al., 2010).”
BackgroundJin, S. A., & Phua, J. (2014) · Journal of Advertising · doi:10.1080/00913367.2013.827606 · Cited by 900
How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter?
Smith, A. N., Fischer, E., & Yongjian, C. (2012) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2012.01.002 · Cited by 1,044
Lasorsa, D. L., Lewis, S. C., & Holton, A. E. (2011) · Journalism Studies · doi:10.1080/1461670x.2011.571825 · Cited by 833
Tufekci, Z. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14517 · Cited by 780
“Another important social media study, based on Twitter, speaks of in-degree (number of followers) as a user’s popularity, and retweets as influence (Cha et al., 2010).”
BackgroundImagining Twitter as an Imagined Community
Gruzd, A. A., Wellman, B., & Takhteyev, Y. (2011) · American Behavioral Scientist · doi:10.1177/0002764211409378 · Cited by 677
“…has been shown that strict counting of followers by itself is not a good indicator of user’s influence on others in terms of engaging the audience (Cha, Haddadi, Benevenuto, & Gummadi, 2010), we decided to use retweeting behavior as an indicator of influence in this community and count how many…”
MethodMagno, G., Rodrigues, T., & Almeida, V. A. F. (2010) · Cited by 649
Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”
Zhang, X., Fuehres, H., & Gloor, P. A. (2011) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2011.10.562 · Cited by 643
Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number
Gonçalves, B., Perra, N., & Vespignani, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022656 · Cited by 638
“Previous studies have mostly focused on the network induced by this follower-friend relationship [15], [18–20].”
BackgroundInfluence and Passivity in Social Media
Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23808-6_2 · Cited by 606
Measuring social media influencer index- insights from facebook, Twitter and Instagram
Arora, A., Bansal, S., Kandpal, C. S., Aswani, R., & Dwivedi, Y. K. (2019) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2019.03.012 · Cited by 602
Zafarani, R., Abbasi, M. A., & Liu, H. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139088510 · Cited by 620
The Revolutions Were Tweeted: Information Flows During the 2011 Tunisian and Egyptian Revolutions
Lotan, G., Graeff, E., Ananny, M., Gaffney, D., Pearce, I., & boyd, D. (2011) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 593
A lightweight infrastructure for graph analytics
Nguyen, D. D., Lenharth, A., & Pingali, K. K. (2013) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/2517349.2522739 · Cited by 588
“The twitter40 [16] and twitter50 [6] graphs are real-world social network graphs.”
BackgroundNetworked Gatekeeping and Networked Framing on #Egypt
Meraz, S., & Papacharissi, Z. (2013) · The International Journal of Press/Politics · doi:10.1177/1940161212474472 · Cited by 584
“Prior work has revealed differential user influence across the three addressivity markers (Cha et al. 2010); hence, these queries sought to locate and compare prominent individuals across the RT, via, and @ signs.”
BackgroundInfluentialSentiment Knowledge Discovery in Twitter Streaming Data
Bifet, A., & Frank, E. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16184-1_1 · Cited by 577
“[5] show that popular users who have high indegree are not necessarily influential in terms of retweets or mentions, and that influence is gained through concerted effort such as limiting tweets to a single topic.”
BackgroundThe dynamics of protest recruitment through an online network
González-Bailón, S., Borge-Holthoefer, J., Rivero, A., & Moreno, Y. (2011) · Scientific Reports · doi:10.1038/srep00197 · Cited by 571
“ty has as cascade generators – or as the ‘percolating cluster’ (16) – derives from their location at the core of the network; this suggests that, contrary to what has been argued in previous research (4), centrality in the network of followers is still a meaningful measure of influence in online networks – at least in…”
ResultModeling Information Diffusion in Implicit Networks
Yang, J., & Leskovec, J. (2010) · Proceedings · doi:10.1109/icdm.2010.22 · Cited by 563
“Even though the diffusion of information has been an active research area recently [7], [12], [26], [28], modeling the diffusion in social networks has proven to be a challenging task.”
BackgroundTweets and Trades: the Information Content of Stock Microblogs
Sprenger, T. O., Tumasjan, A., Sandner, P., & Welpe, I. M. (2013) · European Financial Management · doi:10.1111/j.1468-036x.2013.12007.x · Cited by 563
Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1866307.1866311 · Cited by 558
Network analysis reveals open forums and echo chambers in social media discussions of climate change
Williams, H. T. P., McMurray, J. R., Kurz, T., & Hugo Lambert, F. (2015) · Global Environmental Change · doi:10.1016/j.gloenvcha.2015.03.006 · Cited by 631
“The two-step flow model has been examined in the context of climate change campaigns by Nisbet and Kotcher (2009), while various studies (e.g. (Cha et al., 2010, 2012; Wu et al., 2011)) have given empirical support for two-step flow as a general model for media dissemination on Twitter.”
MethodCheng, J., Adamic, L., Dow, P. A., Kleinberg, J. M., & Leskovec, J. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2566486.2567997 · Cited by 547
“Still, [8] found that various measures of a user’s popularity are not very correlated with his or her influence.”
BackgroundJun, S., Yoo, H. S., & Choi, S. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.11.009 · Cited by 587
Information diffusion and external influence in networks
Myers, S. A., Zhu, C., & Leskovec, J. (2012) · Knowledge Discovery and Data Mining · doi:10.1145/2339530.2339540 · Cited by 548
“Incorporating our model into methods for identifying “influencers” in networks [18, 5, 9] might be fruitful.”
MethodIntrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?
Toubia, O., & Stephen, A. T. (2013) · Marketing Science · doi:10.1287/mksc.2013.0773 · Cited by 484
“In order to execute our treatment, we created and managed 100 synthetic Twitter users (50 males, 50 females) and created one link from each synthetic user to each treated user (i.e., followed or sent a follow request) between days 57 and 106.”
BackgroundMethodInfluentialHow Social Media Facilitates Political Protest: Information, Motivation, and Social Networks
Jost, J. T., Barberá, P., Bonneau, R., Langer, M., Metzger, M., Nagler, J., Sterling, J., & Tucker, J. A. (2018) · Political Psychology · doi:10.1111/pops.12478 · Cited by 492
Ghiassi, M., Skinner, J., & Zimbra, D. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.057 · Cited by 485
The Dynamics of Protest Recruitment through an Online Network
González‐Bailón, S., Borge‐Holthoefer, J., Rivero, A., & Moreno, Y. (2011) · ScholarlyCommons (University of Pennsylvania) · Cited by 466
Fame for sale: Efficient detection of fake Twitter followers
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 475
“However, 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].”
BackgroundThe Stratosphere platform for big data analytics
Alexandrov, A., Bergmann, R., Ewen, S., Freytag, J.-C., Hueske, F., Heise, A., Kao, O., Leich, M., Leser, U., Markl, V., et al. (2014) · The VLDB Journal · doi:10.1007/s00778-014-0357-y · Cited by 434
“For ourexperiments,werantheconnectedcomponentsalgorithm proposed by Kang et al. in [49] on the crawled Twitter graph used by Cha et al. in [17], using DOP from 80 to 200.”
MethodMeasuring user influence on Twitter: A survey
Riquelme, F., & González-Cantergiani, P. (2016) · Information Processing & Management · doi:10.1016/j.ipm.2016.04.003 · Cited by 449
“The influential users tend to be active [20,66], but only a few active users are influential.”
BackgroundTsur, O., & Rappoport, A. (2012) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2124295.2124320 · Cited by 423
Tweeting is believing?: understanding microblog credibility perceptions
Morris, M. R., Counts, S., Roseway, A., Hoff, A., & Schwarz, J. (2012) · Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work · doi:10.1145/2145204.2145274 · Cited by 411
“For user names, those who plan to tweet exclusively on a specific topic (an advisable strategy for building a large follower base [4]), should adopt a topically-aligned user name as those generated high levels of credibility.”
BackgroundDoes Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies
Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0388-3 · Cited by 434
“Empirical findings indicate that such information is of limited relevance (Cha et al. 2010); however, we must find out more about this metric’s role.”
Measuring Large-Scale Social Networks with High Resolution
Stopczynski, A., Sekara, V., Sapieżyński, P., Cuttone, A., Madsen, M. M., Larsen, J. E., & Lehmann, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095978 · Cited by 411
“detail records (CDR) studies [4] or Twitter analysis [5].”
BackgroundAttractiveness, trustworthiness and expertise – social influencers’ winning formula?
Wiedmann, K., & von Mettenheim, W. (2020) · Journal of Product & Brand Management · doi:10.1108/jpbm-06-2019-2442 · Cited by 561
Difallah, D. E., Pavlo, A., Curino, C., & Cudré-Mauroux, P. (2013) · Proceedings of the VLDB Endowment · doi:10.14778/2732240.2732246 · Cited by 396
“In order to provide a realistic benchmark, we obtained an anonymized snapshot of the Twitter social graph from August 2009 that contains 51 million users and almost 2 billion “follows” relationships [14].”
BackgroundDynamical classes of collective attention in twitter
Lehmann, J., Gonçalves, B., Ramasco, J. J., & Cattuto, C. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187871 · Cited by 388
“Several aspects of Twitter have been extensively investigated in the literature, including its network topology [18, 19, 20], the relations and types of messages between users [21, 22], the internal information propagation [23], the credibility of information [24, 25], and even its potential as an indicator of the sta…”
BackgroundDigital Footprints: Opportunities and Challenges for Online Social Research
Golder, S. A., & Macy, M. (2014) · Annual Review of Sociology · doi:10.1146/annurev-soc-071913-043145 · Cited by 321
Understanding the Political Representativeness of Twitter Users
Barberá, P., & Rivero, G. (2014) · Social Science Computer Review · doi:10.1177/0894439314558836 · Cited by 394
Towards more systematic Twitter analysis: metrics for tweeting activities
Bruns, A., & Stieglitz, S. (2013) · International Journal of Social Research Methodology · doi:10.1080/13645579.2012.756095 · Cited by 367
“A small but growing number of studies explicitly analyse social media networks (Cha et al., 2010; Wu et al., 2011).”
BackgroundSocial Network Analysis and Mining for Business Applications
Bonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961194 · Cited by 374
Naveed, N., Gottron, T., Kunegis, J., & Alhadi, A. C. (2011) · ACM Web Science Conference (WebSci) · doi:10.1145/2527031.2527052 · Cited by 358
“The question of what causes a message to be retweeted has frequently been addressed, but mainly in a scenario of retweet prediction for a given user and with a focus on the structure of the social network [4, 9, 13].”
BackgroundMethodEpidemiological modeling of news and rumors on Twitter
Jin, F., Dougherty, E., Saraf, P., Cao, Y., & Ramakrishnan, N. (2013) · Social Network Mining and Analysis · doi:10.1145/2501025.2501027 · Cited by 357
Online Influence? Social Media Use, Opinion Leadership, and Political Persuasion
Weeks, B. E., Ardèvol‐Abreu, A., & de Zúñiga, H. G. (2015) · International Journal of Public Opinion Research · doi:10.1093/ijpor/edv050 · Cited by 356
Rumor Detection over Varying Time Windows
Kwon, S., Cha, M., & Jung, K. (2017) · PLOS ONE · doi:10.1371/journal.pone.0168344 · Cited by 352
Self-citationEmpirical evaluation and new design for fighting evolving Twitter spammers
Yang, C., Harkreader, R., & Gu, G. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2267732 · Cited by 346
“Cha et al. [9] designed diverse metrics to measure Twitter accounts.”
BackgroundResultAusserhofer, J., & Maireder, A. (2013) · Information Communication & Society · doi:10.1080/1369118x.2012.756050 · Cited by 252
Understanding and combating link farming in the twitter social network
Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., & Gummadi, K. P. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187846 · Cited by 355
“For a detailed description of this dataset we refer the user to reference [9].”
BackgroundMethodInfluentialSelf-citationSentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
Ribeiro, F. N., Araújo, M., Gonçalves, P., André Gonçalves, M., & Benevenuto, F. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0085-1 · Cited by 468
“Emoticons DS (Distant Supervision) Provides polarity score for lexicons Validation with unlabeled Twitter data [51] 1,162,894”
BackgroundSelf-citationAnalyzing User Modeling on Twitter for Personalized News Recommendations
Abel, F., Gao, Q., Houben, G., & Tao, K. (2011) · Cited by 338
COVID-19: fear appeal favoring purchase behavior towards personal protective equipment
Addo, P. C., Fang, J., Kulbo, N. B., & Li, L. (2020) · Service Industries Journal · doi:10.1080/02642069.2020.1751823 · Cited by 328
“Several studies are available on the importance and the role of followers (Badashian & Stroulia, 2016; Cha et al., 2010; De Veirman et al., 2017).”
BackgroundInfluence analysis in social networks: A survey
Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.01.005 · Cited by 312
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
“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
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
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
Kim, E., Sung, Y., & Kang, H. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.04.020 · Cited by 281
“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).”
BackgroundSemantic 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
“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 290
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
The Multiple Facets of Influence
Dubois, E., & Gaffney, D. (2014) · American Behavioral Scientist · doi:10.1177/0002764214527088 · Cited by 301
“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
Mining Brand Perceptions from Twitter Social Networks
Culotta, A., & Cutler, J. (2016) · Marketing Science · doi:10.1287/mksc.2015.0968 · Cited by 270
“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 271
“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 253
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
“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 296
“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
“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
“…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
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
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 239
“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
Bae, Y., & Lee, H. (2012) · J. Assoc. Inf. Sci. Technol. · doi:10.1002/asi.22768 · Cited by 224
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
“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 239
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
“[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.”
BackgroundMethodSocial Clicks: What and Who Gets Read on Twitter?
Gabielkov, M., Ramachandran, A., Chaintreau, A., & Legout, A. (2016) · Measurement and Modeling of Computer Systems · doi:10.1145/2896377.2901462 · Cited by 320
“…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 234
Ranking in evolving complex networks
Liao, H., Mariani, M. S., Medo, M., Zhang, Y., & Zhou, M. (2017) · reroDoc Digital Library · Cited by 221
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 237
Yang, L., Sun, T., Zhang, M., & Mei, Q. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187872 · Cited by 211
“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
“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
“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
“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
Predicting Information Spreading in Twitter
Zaman, T. R., Herbrich, R., Gael, J. V., & Stern, D. (2010) · Cited by 203
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
Understanding the influence of all nodes in a network
Lawyer, G. (2015) · Scientific Reports · doi:10.1038/srep08665 · Cited by 214
“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 219
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
“A modern social network like Twitter have millions of active users [1].”
Background
Metadata
- Authors
- Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K.
- Affiliations
- Max Planck Society (DE)
- University of London (GB)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2010-05-16
- Volume
- 4
- Issue
- 1
- Pages
- 10-17
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W1814023381
- Semantic Scholar
- 1ad8410d0ded269af4a0116d8b38842a7549f0ae
- Cited by
- 3,022
- References
- 21
- Citations by year
- 2026: 44 · 2025: 74 · 2024: 61 · 2023: 83 · 2022: 130 · 2021: 212 · 2020: 184 · 2019: 198 · 2018: 257 · 2017: 266 · 2016: 253 · 2015: 287 · 2014: 329 · 2013: 310 · 2012: 193
- FWCI
- 142.70
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- user influence, Twitter, influence measures, in-degree, retweets, mentions, directed links, viral marketing, information flow, topic dynamics, influential users
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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 ›
Are you an author, or do you see a problem with this page? Request a review
