The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
TwitterRank: finding topic-sensitive influential twitterers
Open accessAbstract
This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.
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Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Haveliwala, T. H. (2002) · Proceedings of the 11th international conference on World Wide Web · doi:10.1145/511446.511513 · Cited by 1,567
Research works printed but not linked: 12
- 10.5555/944919.944937 · doi:10.5555/944919.944937
- 10.1109/TIT.2003.813506 · DOI · Paper page
- 10.1145/1348549.1348556 · DOI · Paper page
- 10.1145/956750.956769 · DOI · Paper page
- 10.1007/11523468_91 · DOI · Paper page
- 10.1093/biomet/30.1-2.81 · DOI · Paper page
- 10.1145/324133.324140 · DOI · Paper page
- 10.1146/annurev.soc.27.1.415 · DOI · Paper page
- R.G. Miller . Beyond ANOVA , basics of applied statistics . Wiley Series in Probability and Mathematical Statistics . Wiley , 1986 . R.G. Miller. Beyond ANOVA, basics of applied statistics. Wiley Series in Probability and Mathematical Statistics. Wiley, 1986.
- S. Milstein , A. Chowdhury , G. Hochmuth , B. Lorica , and R. Magoulas . Twitter and the micro-messaging revolution: Communication, connections, and immediacy-140 characters at a time. O'Reilly Report , November 2008 . S. Milstein, A. Chowdhury, G. Hochmuth, B. Lorica, and R. Magoulas. Twitter and the micro-messaging revolution: Communication, connections, and immediacy-140 characters at a time. O'Reilly Report, November 2008.
- 10.1145/1401890.1401960 · DOI · Paper page
- M. Steyvers and T. Griffiths . Probabilistic topic models . In T. Landauer, D. McNamara, S. Dennis, and W. Kintsch, editors, Latent Semantic Analysis: A Road to Meaning . Laurence Erlbaum , In Press . M. Steyvers and T. Griffiths. Probabilistic topic models. In T. Landauer, D. McNamara, S. Dennis, and W. Kintsch, editors, Latent Semantic Analysis: A Road to Meaning. Laurence Erlbaum, In Press.
Cited by
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022
“More recently, a PageRank-like measure has been proposed to quantify influence on Twitter (Weng et al. 2010).”
BackgroundDe Veirman, M., Cauberghe, V., & Hudders, L. (2017) · International Journal of Advertising · doi:10.1080/02650487.2017.1348035 · Cited by 2,548
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
Comparing Twitter and Traditional Media Using Topic Models
Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., & Li, X. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-20161-5_34 · Cited by 1,214
Self-citation
Metadata
- Authors
- Weng, J., Lim, E.-P., Jiang, J., & He, Q.
- Venue
- Proceedings of the third ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2010-02-04
- Pages
- 261-270
- OpenAlex
- W2076219102
- Cited by
- 1,742
- References
- 3
- FWCI
- 96.65
- Type
- conference paper
- Language
- en
- Open access
- green
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- Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010). TwitterRank: finding topic-sensitive influential twitterers. Proceedings of the third ACM international conference on Web search and data mining. https://doi.org/10.1145/1718487.1718520
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
