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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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Mapping networks of influence: tracking Twitter conversations through time and space
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Microblogging Content Propagation Modeling Using Topic-Specific Behavioral Factors
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An identification method for important nodes based on k-shell and structural hole
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Using Twitter Mentions and a Graph Database to Analyse Social Network Centrality
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Simple parallel biconnectivity algorithms for multicore platforms
Slota, G. M., & Madduri, K. (2014) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.1109/hipc.2014.7116914 · Cited by 20
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BackgroundDesai, T., Patwardhan, M., & Coore, H. (2014) · F1000Research · doi:10.12688/f1000research.4283.1 · Cited by 20
I Wish I Didn't Say That! Analyzing and Predicting Deleted Messages in TwitterPreprint
Petrović, S., Osborne, M., & Lavrenko, V. P. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.3107 · Cited by 27
“While it is certainly not the only one or the “best” one (Cha et al., 2010), it is still fairly indicative of the user’s popularity/influence and much easier to collect than other ones (e.g., number of mentions).”
BackgroundMadero, V., & Morris, N. (2015) · Journal of Environmental Planning and Management · doi:10.1080/09640568.2015.1085841 · Cited by 19
“It is known that microblogging systems such as Twitter can be used as a means to influence people’s decisions (Cha et al. 2010), especially for political purposes.”
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A neighbour scale fixed approach for influence maximization in social networks
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Inferring mechanisms of response prioritization on social media under information overload
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BackgroundWorkload Balancing via Graph Reordering on Multicore Systems
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“Graphs live , twitter and mpi are collected from social networks to depict user relationships [21], [23], [24].”
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Wang, S., Gan, T., Liu, Y.-A., Wu, J., Cheng, Y., & Nie, L. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3151029 · Cited by 21
Liang, F., & Lu, S.-N. (2023) · Social Media + Society · doi:10.1177/20563051231177946 · Cited by 25
“Yet, this approach has been found to be ineffective, as the size of followers does not necessarily imply influence on information diffusion (Cha et al., 2010).”
BackgroundMethodInfluentialTwitter event networks and the Superstar model
Bhamidi, S., Steele, J. M., & Zaman, T. R. (2015) · The Annals of Applied Probability · doi:10.1214/14-aap1053 · Cited by 19
“Correlations between the degree in retweet graphs and the Twitter follower graph for different users was studied in [7].”
BackgroundTopical Influential User Analysis with Relationship Strength Estimation in Twitter
Liu, X., Shen, H., Ma, F., & Liang, W. (2014) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2014.11 · Cited by 19
“According to [25] and [7], topical influence is better preserved over retweeting relationships, and retweeting a user is a significantly stronger indicator of topic interest than following him.”
BackgroundDeriving Marketing Intelligence over Microblogs
Li, Y.-M., & Li, T.-Y. (2011) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2011.143 · Cited by 19
“In [1], the authors mentioned three indicators: mention influence, follow influence and retweet influence.”
BackgroundRecommending Information Sources to Information Seekers in Twitter
Armentano, M. G., Godoy, D. L., Amandi, A., & Seco, P. A. (2011) · Cited by 19
He, J., Hong, L., Frías-Martínez, V., & Torrens, P. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27433-1_5 · Cited by 19
“Existing studies have utilized different measurements to evaluate users’ influence and activity considering following and retweeting relationships [3, 19].”
BackgroundAnalysis of Tweets Related to Cyberbullying
Alim, S. (2015) · International Journal of Cyber Behavior Psychology and Learning · doi:10.4018/ijcbpl.2015100103 · Cited by 19
Proactive Engagement of Opinion Leaders and Organization Advocates on Social Networking Sites
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“Virtual opinion leaders are of particular importance to digital campaigns because of the large size of online social networks (Cha et al., 2010), the swift distribution of messages (Li & Du, 2011), and available digital metrics to measure campaign success (Nisbet & Kotcher, 2009).”
BackgroundSpreading the news: how can journalists gain more engagement for their tweets?
Orellana-Rodriguez, C., Greene, D., & Keane, M. T. (2016) · Web Science Conference · doi:10.1145/2908131.2908154 · Cited by 24
“A considerable amount of Twitter literature has examined “socialnetwork features” such as number of followers/followees or number of times the account has been listed by other users [3, 24].”
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Graph colouring as a challenge problem for dynamic graph processing on distributed systems
Sallinen, S., Iwabuchi, K., Poudel, S., Gokhale, M. B., Ripeanu, M., & Pearce, R. (2016) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.5555/3014904.3014945 · Cited by 19
User behavior mining on social media: a systematic literature review
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“Similar to [18, 33] they computed user influence using the average number of reposts from a user’s tweets, but their research was complementary to [18].”
BackgroundGerman Political Journalists and the Normalization of Twitter
Degen, M., & Olgemöller, M. (2021) · Journalism Studies · doi:10.1080/1461670x.2021.1952472 · Cited by 19
“Journalists with more than one account or those without a public account were not considered in the sample (Cha et al. 2010; Leavitt 2009).”
BackgroundSocial Media and Microblogs Credibility: Identification, Theory Driven Framework, and Recommendation
Qureshi, K. A., Malick, R. A. S., & Sabih, M. (2021) · IEEE Access · doi:10.1109/access.2021.3114417 · Cited by 23
“Unfortunately, it also welcome some other issues like: user’s coordinated behavior [18], follower’s fallacy [19], etc.”
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Zhang, H., Alim, M. A., Thai, M. T., & Nguyen, H. P. N. (2015) · International Conference on Communications · doi:10.1109/icc.2015.7248478 · Cited by 19
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Rowe, M. (2011) · Open Research Online - ORO (The Open University) · Cited by 19
Understanding the Behavior of Filipino Twitter Users during Disaster
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Semantifying Twitter: The Influence Tracker Ontology
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“The work described in [3] proposes a methodology where for each Twitter user three different types of influence are introduced.”
MethodParlamentares, representação política e redes sociais digitais:
Marques, F. P. J., de Aquino, J. A., & Miola, E. (2014) · Opinião Pública · doi:10.1590/1807-01912014202178 · Cited by 19
On Modeling Virality of Twitter Content
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“Several researchers have studied different ways of calibrating influential users [10,2].”
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“On social media, promoting the same content multiple times, has been found to increase the attention of the audience (Benevenuto et al. 2010 ; Cha et al. 2010).”
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“There exist several works that have studied the graph and connectivity properties of Facebook, [12], [13], [14], Twitter [15], [16], and Google+ [17], [18].”
BackgroundUnderstanding Communities via Hashtag Engagement: A Clustering Based Approach
DeMasi, O., Mason, D., & Ma, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14746 · Cited by 19
“Previous approaches that employed unsupervised methods targeted finding one type of dynamic class rather than a global categorization that we create (Cha et al. 2010; Lehmann et al. 2012; Crane and Sornette 2008; Lin et al. 2013; Shamma, Kennedy, and Churchill 2011; Cook, Kenthapadi, and Mishra…”
BackgroundMethodInfluentialAutomatic Fake Followers Detection in Chinese Micro-blogging System
Shen, Y., Yu, J., Dong, K., & Nan, K. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06605-9_49 · Cited by 19
Probabilistic Approaches to Recommendations
Barbieri, N., Manco, G., & Ritacco, E. (2014) · Synthesis lectures on data mining and knowledge discovery · doi:10.1007/978-3-031-01906-7 · Cited by 18
Real Time Analytics for Measuring User Influence on Twitter
Zamparas, V., Kanavos, A., & Makris, C. H. (2015) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2015.91 · Cited by 18
“The topic of influence, which has been described in [6] as “the ability of a person to influence the thoughts or actions of others”, has been featured in many scientific manuscripts and studies even from the last century.”
BackgroundMethodWhat Counts? How to use Different Sources of NGO Data
Bloodgood, E. A., Stroup, S. S., & Wong, W. H. (2021) · VOLUNTAS International Journal of Voluntary and Nonprofit Organizations · doi:10.1007/s11266-021-00396-w · Cited by 18
“Information might move virally, that is, with many users responding and distributing the tweet, or it can be broadcast, that is, sent from influencers to their audiences (Cha et al., 2010; Goel et al., 2016).”
BackgroundSuper-amplifiers! The role of Twitter extended party networks in political elections
Yoon, N., Hemsley, J., Smith, A., Simpson, E., & Eakins, J. (2022) · Policy & Internet · doi:10.1002/poi3.295 · Cited by 18
Social Media Impact on Holiday Travel Planning
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“…2016; Williamson et al., 2018; Yuan et al., 2014), we found unpaid, personalized tweets to be the least effective recruitment method, and we can relate to the observation (Gu et al., 2016, p. 93) that message content may be a stronger indicator of virality than follower count (Cha et al., 2010).”
MethodFinding influential neighbors to maximize information diffusion in twitter
Kim, H., Beznosov, K., & Yoneki, E. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2579358 · Cited by 18
“Clearly, in the most popular online social network services such as Twitter or Facebook, each user has a different propagation rate for her neighbors on spreading information in a network according to the user’s reputation or role such as opinion formers, leaders or fol-lowers [4].”
BackgroundInfluentialTweets and retweets: Twitter takes wing in government
Wigand, F. D. L. (2011) · Information Polity · doi:10.3233/ip-2011-0241 · Cited by 18
Roth, C. (2013) · Advances in Complex Systems · doi:10.1142/s0219525913500136 · Cited by 18
“…in socio-technical d Note also the work of Aiello et al. [3] on aNobii (a book rating/tagging social network site) and principally focused on homophily, in terms of book library similarity (and geographic proximity) with respect to structure (principally topological distance in the social network).”
BackgroundQuantifying Discrepancies in Opinion Spectra from Online and Offline Networks
Lee, D., Hahn, K. S., Yook, S., & Park, J. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124722 · Cited by 19
“be the fraction of the number of legislators from party ifollowed by Twitter user jamong all legislators followed by j. Then the KL divergence D j of Twitter user jis D j = XN i=1 p ij log p ij P i ; (4) where N is the number of parties. KL divergence is non-negative. A large D j means that user j’s followership patte…”
BackgroundSocial Connections in User-Generated Content Video Systems: Analysis and Recommendation
Li, Z., Lin, J., Salamatian, K., & Xie, G. (2012) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2012.100512.120233 · Cited by 18
“The correlation decreases with the growth of popularity due to the tied ranks among the less popular users [30], i.”
BackgroundDiscovering Information Propagation Patterns in Microblogging Services
Yu, Z., Wang, Z., He, H., Tian, J., Lu, X., & Guo, B. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2742801 · Cited by 18
“Cha et al. [2010] developed a framework to measure and model the individual’s influence on Twitter, and found that a high follower count does not necessarily lead to a large number of retweets.”
BackgroundCredibility in Information Retrieval
Ginsca, A., Popescu, A., & Lupu, M. (2015) · now publishers, Inc. eBooks · doi:10.1561/9781680830873 · Cited by 18
Feature Extraction and Analysis for Identifying Disruptive Events from Social Media
Alsaedi, N., & Burnap, P. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2808867 · Cited by 18
“Popular tweets can propagate multiple hops away from the source as they are retweeted throughout the network [7].”
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Wang, T., Chen, Y., Wang, Y., Wang, B., Wang, G., Li, X., Zheng, H.-T., & Zhao, B. Y. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-016-5198-y · Cited by 18
“Subsequent efforts have studied Twitter from different perspectives, ranging from information diffusion [42, 43], user influence [44, 45], to opinion mining [46] and user demo-”
BackgroundMeasuring Click and Share Dynamics on Social Media: A Reproducible and Validated Approach
Wang, L., Ramachandran, A., & Chaintreau, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i2.14841 · Cited by 18
“A common motivation of our work and several others is to study propagations to quantify influence online (Cha et al. 2010; Bakshy et al. 2011), how news sharing is affected by social networks (An et al.”
BackgroundUnderstanding Twitter use by major LIS professional organisations in the United States
Zhang, M., Sheu, F.-R., & Zhang, Y. (2017) · Journal of Information Science · doi:10.1177/0165551516687701 · Cited by 18
“‘Influential’ and highly visible accounts are identified through others mentioning and retweeting them [29,30].”
BackgroundResultMariconti, E., Onaolapo, J. O., Ahmad, S. S., Nikiforou, N., Egele, M., Nikiforakis, N., & Stringhini, G. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052589 · Cited by 18
“…attracted significant interest from the research community, who studied its general characteristics [15], how reputation on the network works [5], investigated peculiar traits of the service, in particular its microblogging features [16], and looked at the unfollow patterns of Twitter users…”
A Machine Learning Based Approach for Opinion Mining on Social Network Data
Arif, F., & Dulhare, U. N. (2017) · Lecture notes in networks and systems · doi:10.1007/978-981-10-3226-4_13 · Cited by 18
Mining the Web to approximate university rankings
McCoy, C. G., Nelson, M. L., & Weigle, M. C. (2018) · Information Discovery and Delivery · doi:10.1108/idd-05-2018-0014 · Cited by 13
“approaches define different types of Twitter influence, namely indegree, retweet and mention influence [6].”
BackgroundStrategies to find audience segments on Twitter for e-cigarette education campaigns
Chu, K., Allem, J.-P., Unger, J. B., Cruz, T. B., Akbarpour, M., & Kirkpatrick, M. G. (2018) · Addictive Behaviors · doi:10.1016/j.addbeh.2018.11.015 · Cited by 18
Opinion Formation in Online Social Networks: Exploiting Predisposition, Interaction, and Credibility
Das, R., Kamruzzaman, J., & Karmakar, G. (2019) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2019.2914264 · Cited by 18
“Content quality in Twitter indicates a user’s influence level on others [40].”
BackgroundManaging Big Data Using Fuzzy Sets by Directed Graph Node Similarity
(2017) · Acta Polytechnica Hungarica · doi:10.12700/aph.14.2.2017.2.10 · Cited by 18
“There is a vast number of researches that try to determine the measure of influence in various social media and social networks [2], [21], [22].”
BackgroundJin, X. (2020) · Journal of International Crisis and Risk Communication Research · doi:10.30658/jicrcr.3.2.3 · Cited by 18
“Contrary to previous research in which a large number of followers was not related to successful information dissemination (Cha et al., 2010), this study finds that when a Twitter user has more followers, the user is more likely to be seen as an information provider (reflected by showing more…”
BackgroundMethodResultInfluentialA Topological Approach for Detecting Twitter Communities with Common Interests
Lim, K. H., & Datta, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-45392-2_2 · Cited by 18
“for the entire Twitter population [3, 4].”
Profiling the followers of the most influential and verified users on Sina Weibo
Wang, H., Lei, K., & Xu, K. (2015) · International Conference on Communications · doi:10.1109/icc.2015.7248479 · Cited by 18
“[4] presents a large-scale and empirical analysis of influence patterns of Twitter users, and analyzes the dynamics of user influence measured by in-degree, retweets, and mentions, while [1] introduces a set of influence measures”
BackgroundModeling and evaluating information propagation in a microblogging social network
Li, C. T., Kuo, T.-T., Ho, C.-T., Hong, S.-C., Lin, W.-S., & Lin, S.-D. (2012) · Social Network Analysis and Mining · doi:10.1007/s13278-012-0082-8 · Cited by 18
“On the other hand, some works about information propagation attempt to identify influential users from real data (Cha et al. 2010; Song et al. 2007; Tang et al. 2010; Yang et al. 2010), select an initial user set with maximum influences given a diffusion model (Kempe et al.”
BackgroundOptimistic Recovery for Iterative Dataflows in Action
Dudoladov, S., Xu, C., Schelter, S., Katsifodimos, A., Ewen, S., Tzoumas, K., & Markl, V. (2015) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2723372.2735372 · Cited by 18
How "big vs" dominate chinese microblog
Wang, N., She, J. P. M., & Chen, J. (2014) · ACM Web Science Conference (WebSci) · doi:10.1145/2615569.2615683 · Cited by 18
“Some research has been done on analysing user behaviour, influence and topology on Weibo and Twitter [3], [4], [5].”
BackgroundThe early-adopter graph and its application to web-page recommendation
Mele, I., Bonchi, F., & Gionis, A. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398497 · Cited by 18
“, twitter data) to better understand the phenomenon of viral propagation of information in social networks and micro-blogging platforms [1, 5, 7, 12, 15].”
BackgroundA Economia do Retweet: Redes, Difusão de Informações e Capital Social no Twitter
Recuero, R., & Zago, G. D. S. (2012) · Revista Contracampo · doi:10.22409/contracampo.v1i24.180 · Cited by 18
“Para muitos autores, essa visibilidade é também influência (Cha et al., 2010; Romero et al, 2010 e Suh et al, 2010).”
BackgroundInfluentialAn empirical study of socialbot infiltration strategies in the Twitter social network
Freitas, C. A., Benevenuto, F., Veloso, A., & Ghosh, S. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0331-3 · Cited by 18
“(1) Number of followers acquired: This is a standard metric for estimating the popularity of users in Twitter (Cha et al. 2010).”
BackgroundSelf-citationFang, Y., Chen, X., Song, Z., Wang, T., & Cao, Y. (2016) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.2017010102 · Cited by 18
Kohl, C., Mostafa, D. S., Böhm, M., & Krcmar, H. (2017) · Journal of the Association for Information Systems · Cited by 17
Social constructivist approach of motivation: social media messages recommendation system
Louvigné, S., Uto, M., Kato, Y., & Ishii, T. (2017) · Behaviormetrika · doi:10.1007/s41237-017-0043-7 · Cited by 21
“Twitter has been one of the most visited websites on the internet in the past years and many researchers have studied its usage and influence among its users (Cha et al. 2010).”
BackgroundMultiLyra: Scalable Distributed Evaluation of Batches of Iterative Graph Queries
Mazloumi, A., Jiang, X., & Gupta, R. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006359 · Cited by 18
“Twitter (TT) [3], [6] 2.”
Using Twitter to Predict Sales: A Case StudyPreprint
Dijkman, R., Ipeirotis, P. G., Aertsen, F., & van Helden, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.04599 · Cited by 18
“This principle is often exploited and, for example, the reason why research is done towards finding ‘influential people’ on social media (Cha et al. 2010; Bakshy et al. 2011).”
A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods.Preprint
Ribeiro, F. N., Araújo, M., Gonçalves, P., Benevenuto, F., & Gonçalves, M. A. (2015) · arXiv (Cornell University) · Cited by 18
Self-citationA Total Error Framework for Digital Traces of HumansPreprint
Sen, I., Flöck, F., Weller, K., Weiß, B., & Wagner, C. (2019) · arXiv (Cornell University) · Cited by 20
O Discurso Desinformativo sobre a Cura do COVID-19 no Twitter: Estudo de casoPreprint
Recuero, R., & Soares, F. B. (2020) · doi:10.1590/scielopreprints.84 · Cited by 18
Bartal, A., Pliskin, N., & Tsur, O. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230811 · Cited by 17
“However, contagion can also be triggered by non-local mechanisms [4] like content promotion [5], exposure to external sources as the mainstream media [6, 7], browsing for information [8], or recommender systems integrated into the social platform [9, 10].”
BackgroundGraphFly: Efficient Asynchronous Streaming Graphs Processing via Dependency-Flow
Chen, D., Gui, C., Zhang, Y., Jin, H., Zheng, L., Huang, Y., & Liao, X. (2022) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41404.2022.00050 · Cited by 17
“#Edges #Vertices Friendster (FT) [29] 2.5B 68.3M Twitter MPI (TT) [30] 2.0B 52.6M Twitter (TW) [31] 1.5B 41.7M UKDomain (UK) [32] 1.0B 39.5M LiveJournal (LJ) [33] 69M 4.8M we vary the proportional number of deleted edges and the size of the batch to evaluate the effectiveness of GraphFly.”
MethodGunarathne, P., Rui, H., & Seidmann, A. A. (2015) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2015.397 · Cited by 17
“The traditional view of influence diffusion assumes that a minority of members in a society possesses qualities that make them exceptionally persuasive in spreading ideas to others [14].”
A Weighted Multi-factor Algorithm for Microblog Search
Zhao, L., Zeng, Y., & Zhong, N. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23620-4_19 · Cited by 17
“Follower influence is equivalent to indegree influence [ 14 ].”
BackgroundMethodInfluentialThe Influence in Twitter: Are They Really Influenced?
Sung, J., Moon, S., & Lee, J.-G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-04048-6_9 · Cited by 17
Identifying the community roles of social capitalists in the Twitter network
Labatut, V., Dugué, N., & Perez, A. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921612 · Cited by 17
“[13].”
Information diffusion in social networks
Agrawal, D., Budak, C., & Abbadi, A. E. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2064036 · Cited by 17
Behavioral clusters in dynamic graphs
Fairbanks, J., Kannan, R., Park, H., & Bader, D. A. (2015) · Parallel Computing · doi:10.1016/j.parco.2015.03.002 · Cited by 17
“Indegree, retweets, and mentions are first-order measures, but popular users with high indegree do not necessarily generate retweets or mentions [5].”
BackgroundDynamic inference of social roles in information cascades
Choobdar, S., Ribeiro, P., Parthasarathy, S., & Silva, F. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-015-0402-5 · Cited by 17
“number of followers, mentions, retweets (Lee et al. 2010; Cha et al. 2010), or the size of the information cascades (Bakshy et al.”
BackgroundZhao, K., Greer, G. E., Yen, J., Mitra, P., & Portier, K. M. (2014) · Information Systems and e-Business Management · doi:10.1007/s10257-014-0260-5 · Cited by 23
“As the classification results in Table 2 suggest, adding neighborhood-based features improved the performance for three out of the five classifiers.”
BackgroundMethodAggregating content and network information to curate twitter user lists
Greene, D., Sheridan, G., Smyth, B., & Cunningham, P. (2012) · doi:10.1145/2365934.2365941 · Cited by 17
User spread influence measurement in microblog
Zhuang, K., Shen, H., & Zhang, H. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3818-z · Cited by 17
“Although the microblog system use the followers count to represent and rank the users’ popularity, previous studies [2] found it is not related to other important notions of influence such as retweets or mentions, which means that followers count alone reveals very little about the user’s influence.”
BackgroundInfluencers on the Russian Twitter
Bodrunova, S. S., Litvinenko, A., & Blekanov, I. S. (2016) · International Conference on Electronic Governance and Open Society: Challenges in Eurasia (EGOSE) · doi:10.1145/3014087.3014106 · Cited by 17
“Most of the works (see [53]; for earlier accounts, see [56; 68; 69; 70; 71] share the opinion that the number of retweets is the metric that is sufficient to tell who are the influencers in a given discussion.”
BackgroundInfluence Maximization in Social Media Networks Using Hypergraphs
Amato, F., Moscato, V., Picariello, A., & Sperlí, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57186-7_17 · Cited by 17
“Moving from theory into practice, in social networks influence of a user is usually correlated to her/his popularity that is usually measured considering “user-to-user” relationships [2]: as an example in Twitter the most diffused measures of influence are indegree, retweets, and mentions [3].”
BackgroundIntegrating Ego, Homophily, and Structural Factors to Measure User Influence in Online Community
Zhang, C., Lu, T., Chen, S., & Zhang, C. (2017) · IEEE Transactions on Professional Communication · doi:10.1109/tpc.2017.2703038 · Cited by 17
“[14] posit that one of the key factors determining influence is”
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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