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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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Data-Driven Cyber Security in Perspective—Intelligent Traffic Analysis
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“In contrast, our work uses a univariate Hawkes process focused on modeling the impact of Twitter on individual Youtube videos and a power law kernel.”
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Rastogi, V., Machanavajjhala, A., Chitnis, L., & Sarma, A. D. (2013) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2013.6544813 · Cited by 110
Crowdsourcing, Sharing Economies and Development
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“…of followers, number of retweets or mentions the organisation garners and popularity of the followers and their level of reach in turn, along with the content posted, are a small subset of the many factors that influence the level with which crowds might engage with the open call (Cha et al. 2010).”
BackgroundUnderstanding the efficiency of social media based crisis communication during hurricane Sandy
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“Without any node removals, Twitter’s LCC contained 95% of users [10]; re-”
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Blincoe, K., Sheoran, J., Goggins, S., Petakovic, E., & Damian, D. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2015.10.002 · Cited by 111
“While much work has been done to understand influence on purely social platforms like5 Twitter and Facebook [1, 2, 3], influence on social software development environments like GitHub has been understudied.”
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Narbona, J. (2016) · Church Communication and Culture · doi:10.1080/23753234.2016.1181307 · Cited by 109
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“Examples include descriptive characterizations [12, 6], sentiment analysis [10, 17], entity tagging [15], influence modeling [22, 3, 4], topic modeling [19], conversation modeling [20], and modeling the diffusion of information [13, 21].”
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“The importance of retweeting has been noted in other studies (Cha et al., 2010; Hughes and Palen, 2010; Poell and Borra, 2011; Segerberg and Bennett, 2011) as well, but their focus was mostly on its ability to distribute information and amplify messages.”
BackgroundLearning to predict reciprocity and triadic closure in social networks
Lou, T., Tang, J., Hopcroft, J. E., Fang, Z., & Ding, X. (2013) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2499907.2499908 · Cited by 107
“Work of this category mainly focus on identifying influential users in Twitter [Weng et al. 2010; Cha et al. 2010; Kwak et al. 2010] or examining and predicting tweeting behaviors of users [Huberman et al.”
BackgroundMaking friends and communicating on Facebook: Implications for the access to social capital
Bohn, A., Buchta, C., Hornik, K., & Mair, P. (2014) · Social Networks · doi:10.1016/j.socnet.2013.11.003 · Cited by 106
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“The study of the retweets cascades has served to characterize user profiles (Galuba et al., 2010), measure influence (Cha et al., 2010) and propose spreading models (Xiong et al., 2012).”
BackgroundSerafini, M., Taft, R., Elmore, A. J., Pavlo, A., Aboulnaga, A., & Stonebraker, M. R. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/3025111.3025125 · Cited by 106
“To make the benchmark’s access patterns more realistic, we defined the “follows” relationships between users according to a subset of the real Twitter social graph from August 2009 that contains 51m users and 2b “follows” relationships [4].”
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Trends in Social Media: Persistence and Decay
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A Survey of Prediction Using Social MediaPreprint
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“Both the in-degree and out-degree alone indicate litter about the user’s influence [14][40].”
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Wong, F. M. F., Tan, C. W., Sen, S., & Chiang, M. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2553667 · Cited by 103
“Partisanship also increases with vocalness of the population.”
BackgroundAnalysis of Online Social Network Connections for Identification of Influential Users
Al-Garadi, M. A., Varathan, K. D., Ravana, S. D., Ahmed, E., Mujtaba, G., Khan, M. U. S., & Khan, S. U. (2018) · ACM Computing Surveys · doi:10.1145/3155897 · Cited by 107
“Average tweets/retweets does not always indicate the spreading power of a user as it only reflects the direct propagation of the post, ignoring the cascade spreading of the post (Cha et al. 2010).”
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Cascade-based community detection
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“As seen in the Twitter example one user can belong to more than one community , but a link is usually explainable because of a unique topic.”
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Lin, J.-S., & Peña, J. F. (2011) · Journal of Interactive Advertising · doi:10.1080/15252019.2011.10722188 · Cited by 101
“Finally, it will be valuable for television networks to identify those influentials (Cha et al. 2010; Kwak et al. 2010) who retweet program-related messages and encourage them to become more influential market members (Phelps et al.”
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A computational study of mental health awareness campaigns on social media
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“Note that, these two measures have been situated in prior literature as reliable indicators of content engagement on Twitter [31].”
BackgroundPredicting Responses to Microblog Posts
Artzi, Y., Pantel, P., & Gamon, M. (2012) · Cited by 100
Stance and influence of Twitter users regarding the Brexit referendum
Grćar, M., Cherepnalkoski, D., Mozetič, I., & Novak, P. K. (2017) · Computational Social Networks · doi:10.1186/s40649-017-0042-6 · Cited by 104
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People Are Strange When You're a Stranger: Impact and Influence of Bots on Social Networks
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“4 Influence Previous studies on the interdependence between popularity and influence in social media show that popularity does not imply the ability of being influent and vice-versa (Cha et al. 2010).”
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“However, both Cha et al. (2010) and Kwak et al. (2010), with a near-complete dataset of Twitter, found low rates of reciprocal linking and a lack of homophily on Twitter.”
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Xu, Z., Ru, L., Xiang, L., & Yang, Q. (2011) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2011.47 · Cited by 95
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Asur, S., Yu, L. L., & Huberman, B. A. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1888779 · Cited by 95
“[8] have performed a comparison of three different measures of influence - indegree, retweets and user mentions.”
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Mapping collective behavior in the big-data era
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Bastos, M., & Mercea, D. (2015) · New Media & Society · doi:10.1177/1461444815584764 · Cited by 91
“Departing from this line of research, Cha et al. (2010) compared measures of influence on Twitter and reported that users with high indegree were not necessarily influential in terms of spawning retweets or mentions.”
Fake news outbreak 2021: Can we stop the viral spread?
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Finding Influential Users in Social Media Using Association Rule Learning
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“Consequently, discovering trending topics or influential users is of interest for many researchers interested in areas such as marketing [1].”
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Hannák, A., Anderson, E., Barrett, L. F., Lehmann, S., Mislove, A. E., & Riedewald, M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14322 · Cited by 90
“We obtained Twitter data from 2006–2009, covering 54,981,152 accounts and a total of 1,516,115,233 tweets (Cha et al. 2010).”
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Predicting Influential Users in Online Social NetworksPreprint
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“Though some research suggests that people are more affected by the opinions of their peers than influentials [15, 16], recent studies of online social networks [9] support the hypothesis that influentials exert disproportionate amount of influence.”
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“[4] discovered that a very large number of followers do not necessarily have an impact on a user being retweeted more often.”
BackgroundMeasuring and Modeling the Label Dynamics of Online Anti-Malware Engines
Zhu, S., Shi, J., Yang, L., Qin, B., Zhang, Z., Song, L., & Wang, G. (2020) · USENIX Security Symposium · Cited by 87
The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans
Zannettou, S., Sirivianos, M., Blackburn, J., & Kourtellis, N. (2019) · Journal of Data and Information Quality · doi:10.1145/3309699 · Cited by 86
“By analyzing 1.7B tweets, obtained from [51], they find that: 1) users that spread rumors and non-rumors have similar registration age and number of followers; 2) rumors have a clearly different writing style; 3) sentiment in news depends on the topic and not on the credibility of the post; and 4)…”
BackgroundMethodInferring who-is-who in the Twitter social network
Sharma, N. K., Ghosh, S., Benevenuto, F., Ganguly, N., & Gummadi, K. P. (2012) · doi:10.1145/2342549.2342563 · Cited by 86
Self-citationInfluentials, novelty, and social contagion
Harrigan, N., Achananuparp, P., & Lim, E. (2012) · Social Networks · doi:10.1016/j.socnet.2012.02.005 · Cited by 93
The Emergence of Conventions in Online Social Networks
Kooti, F., Yang, H., Cha, M., Gummadi, K., & Mason, W. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14267 · Cited by 80
“We used data gathered from Twitter as reported in previous work (Cha et al. 2010), which comprises the following three types of information: profiles of 52 million users, 1.”
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A novel sentiment analysis of social networks using supervised learning
Anjaria, M., & Guddeti, R. M. R. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0181-9 · Cited by 83
Discurso Desinformativo sobre a Cura do COVID-19 no Twitter
Recuero, R., & Soares, F. B. (2020) · E-Compós · doi:10.30962/ec.2127 · Cited by 90
Magno, G., Comarela, G., Sáez-Trumper, D., Cha, M., & Almeida, V. A. F. (2012) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2398776.2398794 · Cited by 82
“Accordingly, a number of researchers have tried to understand user behaviors and characteristics of various online social networks, where Twitter and Facebook have been the two most popularly examined platforms [26, 7, 39, 3].”
BackgroundResultSelf-citationCultural Dimensions in Twitter: Time, Individualism and Power
García-Gavilanes, R., Quercia, D., & Jaimes, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14419 · Cited by 82
“The number of a user’s followers (indegree) does not necessarily reflect influence but does reflect popularity (Bakshy et al. 2011; Cha et al. 2010; Kwak et al. 2010)).”
BackgroundTurds, traitors and tossers: the abuse of UK MPs via Twitter
Ward, S. J. A., & McLoughlin, L. (2020) · Journal of Legislative Studies · doi:10.1080/13572334.2020.1730502 · Cited by 87
“On the assumption that a more popular MP will be liked more, and therefore abused less, this finding should not be surprising (Chen et al, 2010).”
BackgroundThe role of hidden influentials in the diffusion of online information cascades
Baños, R. A., Borge-Holthoefer, J., & Moreno, Y. (2013) · EPJ Data Science · doi:10.1140/epjds18 · Cited by 79
Information-centric networking: a natural design for social network applications
Mathieu, B., Truong, P., You, W., & Peltier, J.-F. (2012) · IEEE Communications Magazine · doi:10.1109/mcom.2012.6231278 · Cited by 81
Wang, B., Wang, C., Bu, J., Chen, C., Zhang, W. V., Cai, D., & He, X. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488505 · Cited by 81
“make an in-depth comparison of three measures of influence: indegree, retweets, and mentions and investigate the dynamics of user influence across topics and time [6].”
BackgroundArif, A., Shanahan, K., Chou, F.-J., Dosouto, Y., Starbird, K., & Spiro, E. S. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2819964 · Cited by 82
“[5].”
Predicting Retweeting Behavior on Breast Cancer Social Networks: Network and Content Characteristics
Kim, E., Hou, J., Han, J. Y., & Himelboim, I. (2016) · Journal of Health Communication · doi:10.1080/10810730.2015.1103326 · Cited by 84
“…of information, (b) developing and promoting a conversation, (c) validating other authors ’ thoughts, (d) showing loyalty and friendship to authors, (e) gaining fol-lowers or reciprocity from more visible Twitter users, and (f) engaging in social activism (Boyd et al., 2010; Cha et al., 2010).”
BackgroundRethinking the generational gap in online news use: An infrastructural perspective
Taneja, H., Wu, A. X., & Edgerly, S. (2017) · New Media & Society · doi:10.1177/1461444817707348 · Cited by 81
“structure where a few star users are ubiquitously followed and control a large share of attention (Cha et al., 2010).”
BackgroundMental illness and bipolar disorder on Twitter: implications for stigma and social support
Budenz, A., Klassen, A. C., Purtle, J., Yom‐Tov, E., Yudell, M., & Massey, P. M. (2019) · Journal of Mental Health · doi:10.1080/09638237.2019.1677878 · Cited by 89
Social network user influence sense-making and dynamics prediction
Li, J., Peng, W., Li, T., Sun, T., Li, Q., & Xu, J. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.02.038 · Cited by 81
Risk Perceptions of Airbnb Hosts: Evidence from a Mediterranean Island
Malazizi, N., Alipour, H., & Olya, H. (2018) · Sustainability · doi:10.3390/su10051349 · Cited by 86
“[136] “discrimination occurs among landlords of all sizes, including small landlords sharing the property and larger landlords with multiple properties”.”
BackgroundMethodLiu, Y., Chen, Y., & Fan, Z. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.08.052 · Cited by 87
Xie, L., Natsev, A., Kender, J. R., Hill, M., & Smith, J. R. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072307 · Cited by 80
“The first aspect is similar to the reweet and mention measures recently reported for Twitter [5].”
BackgroundResultInfluentialA hybrid approach for spam detection for Twitter
Malik, M., Iqbal, M. A., Aleem, M., & Islam, M. A. (2017) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast.2017.7868095 · Cited by 79
Meng, J., Peng, W., Tan, P., Liu, W., Cheng, Y., & Bae, A. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.039 · Cited by 79
The complex triad of congruence issues in influencer marketing
von Mettenheim, W., & Wiedmann, K. (2021) · Journal of Consumer Behaviour · doi:10.1002/cb.1935 · Cited by 99
“Influencers are individuals who create valuable content, have high reputations in specific fields (Cha et al., 2010; S. Kim et al., 2017) and are”
BackgroundEfficient temporal mining of micro-blog texts and its application to event discovery
Stilo, G., & Velardi, P. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-015-0412-3 · Cited by 78
“Another large collection is described in Cha et al. (2010), however this collection was 4 https://dev.twitter.com/docs/streaming-apis. obtained (with the permission of Twitter) by crawling only about 80,000 Twitter users and, moreover, is not available.”
BackgroundOLFinder: Finding opinion leaders in online social networks
Abolfazl Aleahmad, A., Karisani, P., Rahgozar, M., & Oroumchian, F. (2015) · Journal of Information Science · doi:10.1177/0165551515605217 · Cited by 78
“It is often reported that many structural factors of social networks have not been helpful for finding influential people [33, 34, 52]; therefore, the primary motivation behind our work is to use another important feature for this purpose.”
BackgroundInfluentialThe Anti-Social System Properties: Bitcoin Network Data Analysis
Alqassem, I., Rahwan, I., & Svetinović, D. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2883678 · Cited by 78
“8% of twitter profiles [22].”
FA-GNN: Filter and Augment Graph Neural Networks for Account Classification in Ethereum
Liu, J., Zheng, J., Wu, J., & Zheng, Z. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3166655 · Cited by 79
An Epidemic Model for News Spreading on Twitter
Abdullah, S., & Wu, X. (2011) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2011.33 · Cited by 80
“[4] provided an empirical study on user influence on Twitter.”
BackgroundEmotional Divergence Influences Information Spreading in Twitter
Pfitzner, R., Garas, A., & Schweitzer, F. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14294 · Cited by 76
“Later studies addressed more questions concerning the structure of the underlying social network and the influence of users (Kwak et al. 2010; Cha et al. 2010), the dynamics of tweets and topics (Zaman et al.”
BackgroundThomas, K., Li, F., Grier, C., & Paxson, V. (2014) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2660267.2660282 · Cited by 80
“This influence manifests in information cascades including the adoption of online memes [1, 2, 5, 23], health and lifestyles choices [6], and the spread of biological infections [21].”
BackgroundMobile App Recommendation: An Involvement-Enhanced Approach
He, J., Fang, X., Liu, H., & Li, X. (2018) · MIS Quarterly · doi:10.2139/ssrn.3279195 · Cited by 77
Sociolinguistic analysis of Twitter in multilingual societies
Kim, S., Weber, I. G., Wei, L., & Oh, A. H. (2014) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2631775.2631824 · Cited by 77
Tweeting Links to Academic Articles
Thelwall, M. A., Tsou, A., Weingart, S., Holmberg, K., & Haustein, S. (2013) · DIGITAL.CSIC (Spanish National Research Council (CSIC)) · Cited by 76
Vo, T. C., Sharma, R., Kumar, R., Sơn, L. H., Pham, B. T., Bui, D. T., Priyadarshini, I., Sarkar, M., & Le, T. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-190870 · Cited by 76
“Other side, Cha et al. [36] discussed about the influences in Twitter such as retweets influence, indegree influence and user’s mentions.”
A Model for Scale-Free Networks: Application to Twitter
Aparicio, S., Villazón-Terrazas, J., & Álvarez, G. (2015) · Entropy · doi:10.3390/e17085848 · Cited by 76
“Fortunately, in [25], the authors were able to obtain such a snapshot (Twitter administrators kindly removed the access limit for them), which was then made publicly available.”
BackgroundQuestioner or question: Predicting the response rate in social question and answering on Sina Weibo
Liu, Z., & Jansen, B. J. J. (2017) · Information Processing & Management · doi:10.1016/j.ipm.2017.10.004 · Cited by 76
“− Number of followers The number of followers has been investigated as a possible indicator of a user's activeness in spreading information to e ff ective readers (Cha, Haddadi, Benevenuto, & Gummadi, 2010).”
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 ›
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