The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach
Open accessAbstract
As online social networking sites become more and more popular, they have also attracted the attentions of the spammers. In this paper, Twitter, a popular micro-blogging service, is studied as an example of spam bots detection in online social networking sites. A machine learning approach is proposed to distinguish the spam bots from normal ones. To facilitate the spam bots detection, three graph-based features, such as the number of friends and the number of followers, are extracted to explore the unique follower and friend relationships among users on Twitter. Three content-based features are also extracted from user's most recent 20 tweets. A real data set is collected from Twitter's public available information using two different methods. Evaluation experiments show that the detection system is efficient and accurate to identify spam bots in Twitter.
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Cites
A Bayesian Approach to Filtering Junk E-Mail
Sahami, M., Dumais, S., Heckerman, D. E., & Horvitz, E. (1998) · Cited by 1,206
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
Krishnamurthy, B., Gill, P., & Arlitt, M. (2008) · Proceedings of the first workshop on Online social networks · doi:10.1145/1397735.1397741 · Cited by 697
Arasu, A., Cho, J., Garcia-Molina, H., Paepcke, A., & Raghavan, S. (2001) · ACM Transactions on Internet Technology · doi:10.1145/383034.383035 · Cited by 505
Know your neighbors: web spam detection using the web topology
Castillo, C., Donato, D., Gionis, A., Murdock, V., & Silvestri, F. (2007) · Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1277741.1277814 · Cited by 310
Detecting spammers and content promoters in online video social networks
Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., & Gonçalves, M. (2009) · Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1571941.1572047 · Cited by 220
Link spam detection based on mass estimation
Gyöngyi, Z., Berkhin, P., & Yahoo Inc (2006) · Cited by 140
Spam detection in voice-over-IP calls through semi-supervised clustering
Wu, Y., Bagchi, S., Singh, N., et al. (2009) · IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) · doi:10.1109/dsn.2009.5270323 · Cited by 69
Transductive link spam detection
Zhou, D., Burges, C. J. C., & Tao, T. (2007) · Adversarial Information Retrieval on the Web · doi:10.1145/1244408.1244413 · Cited by 42
Cited by
Social Media, Echo Chambers, and Political Polarization
Barberá, P. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.004 · Cited by 373
Twitter spammer detection using data stream clustering
Miller, Z., Dickinson, B., Deitrick, W., Hu, W., & Wang, A. H. (2014) · Information Sciences · doi:10.1016/j.ins.2013.11.016 · Cited by 332
Self-citationMisinformation, Disinformation, and Online Propaganda
Guess, A. M., & Lyons, B. A. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.003 · Cited by 284
DeBot: Twitter Bot Detection via Warped Correlation
Chavoshi, N., Hamooni, H., & Mueen, A. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0096 · Cited by 260
Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265
Metadata
- Authors
- Wang, A. H.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 335-342
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1517046895
- Cited by
- 257
- References
- 9
- FWCI
- 5.17
- Type
- conference paper
- Language
- en
- Open access
- green
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