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Measuring User Influence in Twitter: The Million Follower Fallacy
Open accessAbstract
Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.
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“artificially inflating the number of followers can also be finalized to make an account more trustworthy and influential, in order to stand from the crowd and to attract other genuine followers [5].”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [5]
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“Linking biases impact the study of, e.g., social networks structure and evolution, social influence, and information diffusion phenomena”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Cha et al., 2010
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“Different graph construction methods can lead to different structural graph properties in the various kinds of networks that can be constructed from social data”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Cha et al., 2010
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Cites
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Do Diffusion Protocols Govern Cascade Growth?
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BackgroundResponses to heat waves: what can Twitter data tell us?
Zander, K. K., Rieskamp, J., Mirbabaie, M., Alazab, M., & Nguyen, D. (2023) · Natural Hazards · doi:10.1007/s11069-023-05824-2 · Cited by 33
“Measuring the popularity of Twitter accounts was done using the in-degree metric (Cha et al. 2010), as well as the FollowerRank (FR) (Nagmoti et al.”
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Choi, M., Sang, Y., & Park, H. W. (2014) · Aslib Journal of Information Management · doi:10.1108/ajim-11-2012-0089 · Cited by 31
“Given that the following relationship is more ritual than the mention relationship, which requires more commitment (Yoon and Park, 2014), previous studies have argued that the former may not accurately explain the user’s influence (Cha et al., 2010; Leavitt et al., 2009).”
ResultWhy Do I Retweet It? An Information Propagation Model for Microblogs
Pezzoni, F., An, J., Passarella, A., Crowcroft, J., & Conti, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03260-3_31 · Cited by 31
“These results are inline with other analysis in literature that have shown that the size of information cascades and the user influence tend to be highly skewed [1, 2, 12].”
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BackgroundDiscovering filter keywords for company name disambiguation in twitter
Spina, D., Gonzalo, J. A., & Amigó, E. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.03.001 · Cited by 31
“[14] built a dataset comprising 54,981,152 users, connected to each other by 1,963,263,821 social links and including a total of 1,755,925,520 tweets to analyze users’ influence and how to measure it on the Twittersphere.”
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“Intuitively, the more a user is followed, mentioned and retweeted, the more he seems influential [13].”
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Lee, M., Kim, H., & Kim, O. (2015) · PSYCHOLOGIA · doi:10.2117/psysoc.2015.189 · Cited by 31
“A user retweets a tweet if it is perceived as valuable information worth sharing with others (Cha, Haddadi, Benevenuto, & Gummadi, 2010).”
BackgroundUser emotion for modeling retweeting behaviors
Chen, J., Liu, Y., & Zou, M. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.08.006 · Cited by 31
What they do in shadows: Twitter underground follower market
Aggarwal, A., & Kumaraguru, P. (2015) · Conference on Privacy, Security and Trust · doi:10.1109/pst.2015.7232959 · Cited by 30
“With the help of these differentiating features, we build a supervised learning model to predict suspicious following behaviour with an accuracy of 89.2%.”
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Štajner, T., Thomée, B., Popescu, A.-M., Marco Pennacchiotti, B., & Jaimes, A. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487659 · Cited by 30
“Number of retweetss The total number of retweets of the user’s posts over the time of data gathering, signaling user authority [34, 7].”
BackgroundUnderstand Instant Video Clip Sharing on Mobile Platforms
Zhang, L., Wang, F., & Liu, J. (2014) · International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV) · doi:10.1145/2578260.2578278 · Cited by 30
“In the traditional social networks, the most followed users can span a wide variety of public figures and news sources [1], which often include mass media, politicians, athletes, as well as other celebrities like actors, writers, musicians, etc.”
BackgroundJacovi, M., Guy, I., Kremer-Davidson, S., Porat, S., & Aizenbud-Reshef, N. (2014) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2531602.2531667 · Cited by 33
“Other systems use implicit indicators such as number of followers, retweets and mentions in Twitter [3,18], or number of comments made on content authored by the user.”
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Ali, A. E., Stratmann, T. C., Park, S., Schöning, J., Heuten, W., & Boll, S. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174130 · Cited by 30
“Despite earlier research that showed a high number of followers does not always mean an influential user [8], we used follower count as a simple heuristic to gather prominent news accounts.”
BackgroundMethodBroadcast Versus Interaction: Environmental Groups’ Use of Twitter
Merry, M. K. (2014) · Journal of Information Technology & Politics · doi:10.1080/19331681.2014.933723 · Cited by 30
Social influence analysis in microblogging platforms – A topic-sensitive based approach
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TRCM: A Methodology for Temporal Analysis of Evolving Concepts in Twitter
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“The research of [7] was based on influencer on the network.”
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The Role of Multimedia Content in Determining the Virality of Social Media Information
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“On Twitter, the construction and propagation of authority does not so much derive from a user’s own tweets, or even simply from the number of followers a user has, as it does from others tweeting and retweeting about that user (Bakshy et al., 2011; Cha et al., 2010; Freelon and Karpf, 2015).”
BackgroundUser clustering in a dynamic social network topic model for short text streams
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Fusing pattern discovery and visual analytics approaches in tweet propagation
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“Prominent examples of such features include asymmetry between the numbers of followers and accounts being followed for many accounts [41], automated accounts (‘bots’) that may retweet at very high frequencies [46], and heterogeneous retweeting properties across different accounts [9].”
BackgroundExploring public engagement with missing person appeals on Twitter
Solymosi, R., Petcu, O., & Wilkinson, J. (2020) · Policing & Society · doi:10.1080/10439463.2020.1782409 · Cited by 29
“Other templates or key phrases that seemed to make a difference were expressing gratitude or asking explicitly to be shared (‘please RT’) (Lopez 2014), and including a hyperlink/URL (Zarrella 2009, Cha et al. 2010, Suh et al. 2010, Rowe, Angeletou, et al. 2011, Rowe, Angeletous, et al. 2011, Van de Velde et al. 2015,…”
BackgroundInfluentialRadicioni, T., Squartini, T., Pavan, E., & Saracco, F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256705 · Cited by 29
“Self-reference indexes proxy the degree of internal and external influence of users, within and across communities, via their retweeting and mentioning activities, as in [51] where a slightly different definition of μr and μm is employed.”
BackgroundLarge-Scale Graph Processing on FPGAs with Caches for Thousands of Simultaneous Misses
Asiatici, M., & Ienne, P. (2021) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca52012.2021.00054 · Cited by 29
Finding and Analyzing Principal Features for Measuring User Influence on Twitter
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“In recent years, researchers started to utilize users’ activities on Twitter to measure user influence [4], [15], [16].”
BackgroundInfluentialLearning Semantic Relationships between Entities in Twitter
Celik, I., Abel, F., & Houben, G. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22233-7_12 · Cited by 28
“A considerable amount of research on micro-blogging and especially on Twitter focused on examining the user behavior and motivation for using Twitter [13,6], as well as user intentions [9].”
BackgroundLUMOS: Dependency-Driven Disk-based Graph Processing.
Vora, K. (2019) · USENIX Annual Technical Conference · Cited by 29
Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images
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“, retweets, followers) [20] or applying additional features, such as URL links [21].”
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“Prior to the 2016 presidential election, several studies [8, 18] stated that the number of followers (they coined them as indegree) was a sign that revealed the popularity of a user but not necessary related to his/her ability to influence voters.”
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Ghosh, R., Lerman, K., Surachawala, T., Voevodski, K., & Teng, S. (2023) · Journal of Complex Networks · doi:10.1093/comnet/cnae006 · Cited by 39
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“As reported in [4], the number of followers of a user u is an indication of the popularity of u, and it is considered a measure of influence, named indegree influence.”
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“As a result, several scholars have found that retweets capture a Twitter user’s influence in getting messages to spread (Kwak et al. 2010; Cha et al. 2010).”
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Slota, G. M., Berry, J. W., Hammond, S. D., Olivier, S. L., Phillips, C. A., & Rajamanickam, S. (2019) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3295500.3356206 · Cited by 28
Information Diffusion on Twitter: Everyone Has Its Chance, But All Chances Are Not Equal
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“[6], who found that users with high indegrees are usually influential in term of new information, but some of them are not influential in term of retweeting.”
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Tsagkias, M. (2012) · UvA-DARE (University of Amsterdam) · Cited by 30
Social recommendation based on trust and influence in SNS environments
Li, W., Ye, Z., Xin, M., & Jin, Q. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2732-0 · Cited by 27
“User influence is also an important feature of directed social networks (such as microblogging network) [3, 17], which affects users’ decisions when they purchase or evaluate a product.”
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Xu, T., Chen, Y., Jiao, L., Zhao, B. Y., Hui, P., & Fu, X. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25821-3_2 · Cited by 27
“The separation is based on the observations of previous studies [4, 17, 27] which report that there are two kinds of users in microblogging: social network users and news media outlets.”
BackgroundRanking Radically Influential Web Forum Users
Anwar, T., & Abulaish, M. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2407313 · Cited by 27
“The proposed method starts with crawling and preprocessing the forum data, followed by user radicalness identification, user collocation identification, and finally ranking the users based on a customized PageRank algorithm, as shown in figure 1.”
MethodQuery-biased learning to rank for real-time twitter search
Zhang, X., He, B., Luo, T., & Li, B. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398543 · Cited by 27
Schiöberg, D., Schneider, F., Schiöberg, H., Schmid, S., Uhlig, S., & Feldmann, A. (2012) · ACM Web Science Conference (WebSci) · doi:10.1145/2380718.2380753 · Cited by 27
“We obtained these data sets from the authors of [4, 6].”
BackgroundMethodInfluentialStuttgart’s Black Thursday on Twitter: Mapping Political Protests with Social Media Data
Jungherr, A. M., & Jürgens, P. (2014) · Palgrave Macmillan UK eBooks · doi:10.1057/9781137276773_7 · Cited by 27
“…2009; Marwick and Boyd, 2011); research interested in network structures on Twitter and information flows through these networks (for example Cha et al, 2010; Jürgens, Jungherr and Schoen, 2011); and research interested in using Twitter data to analyse or predict human behaviour and events…”
BackgroundAn Adaptive Parallel Algorithm for Computing Connected Components
Jain, C., Flick, P., Pan, T. C., Green, O., & Aluru, S. (2017) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2017.2672739 · Cited by 27
Defining and evaluating Twitter influence metrics: a higher-order approach in Neo4j
Δρακόπουλος, Γ., Kanavos, A., Phivos Mylonas, B., & Sioutas, S. (2017) · Social Network Analysis and Mining · doi:10.1007/s13278-017-0467-9 · Cited by 27
“Quoting (Cha et al. 2010) social influence is the ability of a person to influence the thoughts or actions of others.”
BackgroundUsefulness, funniness, and coolness votes of viewers
Lee, I. (2018) · Industrial Management & Data Systems · doi:10.1108/imds-04-2017-0151 · Cited by 28
Scalable, Multi-Constraint, Complex-Objective Graph Partitioning
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Demystifying the Messaging Platforms' Ecosystem Through the Lens of Twitter
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Self-citationInformation Diffusion Model in Twitter: A Systematic Literature Review
Firdaniza, F., Ruchjana, B. N., Chaerani, D., & Radianti, J. (2021) · Information · doi:10.3390/info13010013 · Cited by 27
A gender based study of tagging behavior in twitter
Cunha, E. L. T. P., Magno, G., Almeida, V. A. F., Gonçalves, M. A., & Benevenuto, F. (2012) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2309996.2310055 · Cited by 27
“Datasets MJ and SF have been created from data collected by the Twitter Project at Max Planck Institute [1].”
MethodSelf-citationWords on the Web: Noninvasive Detection of Emotional Contagion in Online Social Networks
Coviello, L., Fowler, J. H., & Franceschetti, M. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2366052 · Cited by 27
“Studies have included the diffusion of news and ‘‘memes’’ [1]; cascades in communication platforms, networked games, microblogging services [2]; health-related phenomena such as obesity and smoking [3], [4]; emotional states like happiness and depression [5], [6]; purchase of online products [7],…”
BackgroundDefending against large-scale crawls in online social networks
Mondal, M., Viswanath, B., Clement, A., Druschel, P., Gummadi, K. P., Mislove, A. E., & Post, A. (2012) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/2413176.2413214 · Cited by 27
Self-citationAutomatic Summarization of Real World Events Using Twitter
Alsaedi, N., Burnap, P., & RANA, O. F. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14766 · Cited by 27
“Third, number of retweets is an indication of popularity (Cha et al. 2010), so in a way we are summarizing the cluster using the highest degree of agreement from users themselves.”
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“Similar results were found in another study by Cha et al. (2010).”
BackgroundResultAn Image Is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures
Schifanella, R., Реди, М., & Aiello, L. M. (2015) · Institutional Research Information System University of Turin (University of Turin) · Cited by 27
Polarity trend analysis of public sentiment on YouTube
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User Engagement with Interactive Media: A Communication Perspective
Oh, J., & Sundar, S. (2016) · Why Engagement Matters · doi:10.1007/978-3-319-27446-1_8 · Cited by 34
Micro-Participation: The Role of Microblogging in PlanningPreprint
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Information Diffusion and External Influence in NetworksPreprint
Myers, S. A., Zhu, C., & Leskovec, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.1331 · Cited by 27
“Incorporating our model into methods for identifying “influencers” in networks [18, 5, 9] might be fruitful.”
MethodBargoni, A., Giachino, C., Gutuleac, R., & Štreimikienė, D. (2024) · Psychology and Marketing · doi:10.1002/mar.21969 · Cited by 29
Crowd-Powered TV Viewing Rates: Measuring Relevancy between Tweets and TV Programs
Wakamiya, S., Lee, R., & Sumiya, K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20244-5_37 · Cited by 26
“As initial work, the usage and role of Twitter in creating a social community on the basis of its basic functions were examined by Java et al. [6], Zhao et al. [10], Krishnamurthy et al. [7], and Cha et al. [ 1 ].”
BackgroundIdentifying infection sources in large tree networks
Luo, W., & Tay, W. P. (2012) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon.2012.6275788 · Cited by 26
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“Determining the infection weights in the diffusion models is itself a hard problem and have been studied in various researches [11], [12].”
BackgroundJiang, Y., & Park, H. (2021) · Public Relations Review · doi:10.1016/j.pubrev.2021.102143 · Cited by 26
A Text Cube Approach to Human, Social and Cultural Behavior in the Twitter Stream
Liu, X., Tang, K., Hancock, J. T., Han, J., Song, M., Xu, R., & Pokorny, B. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37210-0_35 · Cited by 25
“, political sentiment analysis [2], user influence [3], and spread of news [4]).”
BackgroundSopan, A., Rey, P., Butler, B. S., & Shneiderman, B. (2012) · International Conference on Social Informatics (SocialInformatics) · doi:10.1109/socialinformatics.2012.20 · Cited by 26
“We hypothesized about the network growth among the people who tweeted about TtW12 and tested those hypotheses with the data set.”
Servi, L. D., & Elson, S. B. (2014) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2014.2384216 · Cited by 26
“These studies have focused on items including directed links, indegree, retweets, and mentions [7].”
BackgroundOnline Topic-based Social Influence Analysis for the Wimbledon Championships
Embar, V. R., Bhattacharya, I., Pandit, V., & Vaculín, R. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788593 · Cited by 26
“A users acquires followers because of various reasons, but mostly because she is either a celebrity or an expert, whose tweets are of interest to others.”
BackgroundLycarião, D., & dos Santos, M. A. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1168469 · Cited by 29
“It is argued that a node having a high score in a single metric like indegree or outdegree does not necessarily mean that this is an in fl uential user (Cha et al., 2010; Razis & Anagnostopoulos, 2014; Romero et al., 2011).”
BackgroundInformation diffusion in social networks
Agrawal, D., Budak, C., & Abbadi, A. E. (2011) · Proceedings of the VLDB Endowment · doi:10.14778/3402755.3402811 · Cited by 26
“At the end of this section, our goal is to formally identify the characteristics of social networks and their uses.”
BackgroundComputational modeling of Weibo user influence based on information interactive network
Yu, D., Chen, N., & Xu, R. (2016) · Online Information Review · doi:10.1108/oir-12-2015-0391 · Cited by 26
Monitoring Twitter strategies to discover resonating topics: The case of the UNDP
Thelwall, M. A., & Cugelman, B. (2017) · El Profesional de la Informacion · doi:10.3145/epi.2017.jul.09 · Cited by 26
Link Prediction With Personalized Social Influence
Huo, Z., Huang, X. X., & Hu, X. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11892 · Cited by 26
“• R&M (Cha et al. 2010): The directed links are inferred by the counts of retweet and mention of each pair of users.”
BackgroundMeasuring Influence on Instagram
Segev, N., Avigdor, N., & Avigdor, E. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210134 · Cited by 26
“This correlates with previous findings by Cha et al.[15].”
MethodResultNowcasting commodity prices using social media
Kim, J., Cha, M., & Lee, J. G. (2017) · PeerJ Computer Science · doi:10.7717/peerj-cs.126 · Cited by 25
Self-citationJabeur, L. B., Tamine, L., & Boughanem, M. (2012) · ACM Symposium on Applied Computing · doi:10.1145/2245276.2245459 · Cited by 25
Understanding Social Causalities Behind Human Action Sequences
Cai, R., Zhang, Z., Hao, Z., & Winslett, M. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2556724 · Cited by 24
Organizational microblogging for event marketing: a new approach to creative placemaking
Li, X., & Duan, B. (2017) · International Journal of Urban Sciences · doi:10.1080/12265934.2017.1343155 · Cited by 25
“For example, Cha et al. (2010) found that mainstream news organizations have been successful in spreading news over social medias.”
BackgroundRumormongering of genetically modified (GM) food on Chinese social network
Ji, J., Chao, N.-P., & Ding, J. (2019) · Telematics and informatics · doi:10.1016/j.tele.2019.01.005 · Cited by 27
Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks
Mishra, S., Rizoiu, M.-A., & Xie, L. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.15030 · Cited by 26
“(Cha et al. 2010; Romero et al. 2011) showed in their work that influence differs for users with topics and high number of followers is not related to high influence in a network.”
BackgroundDiscover Micro-Influencers for Brands via Better Understanding
Wang, S., Gan, T., Liu, Y.-A., Zhang, L., Wu, J., & Nie, L. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3087038 · Cited by 31
Measuring engagement on twitter using a composite index: An application to social media influencers
Muñoz, M. M., Rojas-de-Gracia, M.-M., & Navas-Sarasola, C. (2022) · Journal of Informetrics · doi:10.1016/j.joi.2022.101323 · Cited by 25
Image Attributes and Diffusion via Twitter: The Case of #guncontrol
Stefanone, M. A., Saxton, G. D., Egnoto, M. J., Wei, W., & Fu, Y. (2015) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2015.216 · Cited by 25
“created by Twitter users who are higher in prestige or status should exhibit higher levels of diffusion [9].”
BackgroundInfluenceRank: An Efficient Social Influence Measurement for Millions of Users in Microblog
Chen, W., Cheng, S., He, X., & Jiang, F. (2012) · International Conference on Cloud and Green Computing · doi:10.1109/cgc.2012.31 · Cited by 25
“R(vi, vj) which represents the ratio of retweet user vj to vi can be calculated as: ,( ) i j i j j Retweets(v ,v ) R v v Retweets(v ) = (6)”
BackgroundMethodIdentification of (near) Real-time Traffic Congestion in the Cities of Australia through Twitter
Gong, Y., Deng, F., & Sinnott, R. (2015) · doi:10.1145/2811271.2811276 · Cited by 25
“Other users have explored real-time global trending information [17] and identification of relationships between individuals and organizations based around the follower relationships [18], [19].”
BackgroundAiello, L. M., Barrat, A., Cattuto, C., Schifanella, R., & Ruffo, G. (2012) · EPJ Data Science · doi:10.1140/epjds12 · Cited by 24
Ap-FSM: A parallel algorithm for approximate frequent subgraph mining using Pregel
Bhatia, V., & Rani, R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.04.010 · Cited by 26
CrowdCorrect: A Curation Pipeline for Social Data Cleansing and Curation
Beheshti, A., Vaghani, K., Benatallah, B., & Tabebordbar, A. (2018) · Lecture notes in business information processing · doi:10.1007/978-3-319-92901-9_3 · Cited by 25
Structure and Evolution of Online Social Networks
Ghosh, S., & Ganguly, N. (2014) · Intelligent systems reference library · doi:10.1007/978-3-319-05164-2_2 · Cited by 25
Mirbabaie, M., Bunker, D., Stieglitz, S., & Deubel, A. (2019) · Information Systems Frontiers · doi:10.1007/s10796-019-09917-x · Cited by 25
“In previous research, Cha et al. (2010) has identified three different influence measures to understand what different roles users play in social media.”
BackgroundInfluence Maximization in Signed Social Networks With Opinion Formation
Liang, W., Shen, C., Li, X., Nishide, R., Piumarta, I., & Takada, H. (2019) · IEEE Access · doi:10.1109/access.2019.2918810 · Cited by 25
“[26]–[28] try to distinguish influence and homophily in social networks, [29], [30] quantifying influence and selection, [31]–[33]measure social influence quantitatively.”
BackgroundIdentifying Vaccine Hesitant Communities on Twitter and their Geolocations: A Network Approach
Ruiz, J. B., Featherstone, J. D., & Barnett, G. A. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.480 · Cited by 25
“We used information diffusion, a widely used method [6] where the reaction to influencer tweets is assessed to define social media influencers.”
MethodWhere are my followers? Understanding the Locality Effect in TwitterPreprint
Gonzalez, R., Cuevas, R., Cuevas, √., & Guerrero, C. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1105.3682 · Cited by 25
“[4] use a large dataset in order to analyze the dynamics of user influence across topic and time in Twitter.”
BackgroundMethodTwitter als Basis wissenschaftlicher Studien
Pfaffenberger, F. (2016) · Springer eBooks · doi:10.1007/978-3-658-14414-2 · Cited by 24
Inferring topic-dependent influence roles of Twitter users
Chen, C., Gao, D., Li, W., & Hou, Y. (2014) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2600428.2609545 · Cited by 24
“Most previous work on influential user identification exploits the features like the numbers of followers and/or retweets and/or mentions [4, 5].”
BackgroundMethodRealtime analysis of information diffusion in social media
Taxidou, I., & Fischer, P. M. (2013) · Proceedings of the VLDB Endowment · doi:10.14778/2536274.2536328 · Cited by 24
“Collection of metrics that target specific users, in our case identifying influentials, in terms of structural factors like attribution of influence edges [7] and indegree [12] and temporal factors like past influence [7].”
BackgroundTwitter Data Mining for Events Classification and Analysis
Azam, N., Jahiruddin, Abulaish, M., & Haldar, N. A. H. (2015) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi.2015.33 · Cited by 24
“In [6], the authors analyzed the role of structural features like indegrees, retweets, and mentions for influence tracking and investigated the dynamic nature of user influence across topic and time.”
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.
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