The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Understanding and combating link farming in the twitter social network
Abstract
Recently, Twitter has emerged as a popular platform for discovering real-time information on the Web, such as news stories and people's reaction to them. Like the Web, Twitter has become a target for link farming, where users, especially spammers, try to acquire large numbers of follower links in the social network. Acquiring followers not only increases the size of a user's direct audience, but also contributes to the perceived influence of the user, which in turn impacts the ranking of the user's tweets by search engines.
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Cites
Earthquake shakes Twitter users: real-time event detection by social sensors
Sakaki, T., Okazaki, M., & Matsuo, Y. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772777 · Cited by 3,640
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 · cited 5×
“For a detailed description of this dataset we refer the user to reference [9].”
BackgroundMethodInfluentialSelf-citationTwitterRank: finding topic-sensitive influential twitterers
Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010) · Proceedings of the third ACM international conference on Web search and data mining · doi:10.1145/1718487.1718520 · Cited by 1,742
Haveliwala, T. H. (2002) · Proceedings of the 11th international conference on World Wide Web · doi:10.1145/511446.511513 · Cited by 1,567
Combating Web Spam with TrustRank
Gyöngyi, Z., Garcia-Molina, H., & Pedersen, J. (2004) · Proceedings 2004 VLDB Conference · doi:10.1016/b978-012088469-8.50052-8 · Cited by 1,085
Improved Algorithms for Topic Distillation in a Hyperlinked Environment
Bharat, K., & Henzinger, M. R. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130367 · Cited by 676
Magno, G., Rodrigues, T., & Almeida, V. A. F. (2010) · Cited by 648
Uncovering social spammers: social honeypots + machine learning
Lee, K., Caverlee, J., & Webb, S. (2010) · Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1835449.1835522 · Cited by 619
@spam: the underground on 140 characters or less
Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010) · Proceedings of the 17th ACM conference on Computer and communications security · doi:10.1145/1866307.1866311 · Cited by 558
Understanding the network-level behavior of spammers
Ramachandran, A., & Feamster, N. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1151659.1159947 · Cited by 510
Cited by
Gupta, A., Lamba, H., Kumaraguru, P., & Joshi, A. (2013) · Proceedings of the 22nd International Conference on World Wide Web Companion · doi:10.1145/2487788.2488033 · Cited by 573
Spotting opinion spammers using behavioral footprints
Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487580 · Cited by 426
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion · doi:10.1145/3041021.3055135 · Cited by 381
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
Hooi, B., Song, H. A., Beutel, A., Shah, N., Shin, K., & Faloutsos, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939747 · Cited by 305
A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks
Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114006 · Cited by 265
Metadata
- Authors
- Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., & Gummadi, K. P.
- Venue
- Proceedings of the 21st international conference on World Wide Web
- Publisher
- ACM
- Published
- 2012-04-16
- Pages
- 61-70
- OpenAlex
- W2005556331
- Cited by
- 342
- References
- 26
- FWCI
- 28.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., & Gummadi, K. P. (2012). Understanding and combating link farming in the twitter social network. Proceedings of the 21st international conference on World Wide Web. https://doi.org/10.1145/2187836.2187846
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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.
