The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
DON’T FOLLOW ME - Spam Detection in Twitter
Open accessAbstract
Abstract: The rapidly growing social network Twitter has been infiltrated by large amount of spam. In this paper, a spam detection prototype system is proposed to identify suspicious users on Twitter. A directed social graph model is proposed to explore the “follower ” and “friend ” relationships among Twitter. Based on Twitter’s spam policy, novel content-based features and graph-based features are also proposed to facilitate spam detection. A Web crawler is developed relying on API methods provided by Twitter. Around 25K users, 500K tweets, and 49M follower/friend relationships in total are collected from public available data on Twitter. Bayesian classification algorithm is applied to distinguish the suspicious behaviors from normal ones. I analyze the data set and evaluate the performance of the detection system. Classic evaluation metrics are used to compare the performance of various traditional classification methods. Experiment results show that the Bayesian classifier has the best overall performance in term of F-measure. The trained classifier is also applied to the entire data set. The result shows that the spam detection system can achieve 89 % precision. 1
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Cites
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Cited by
Predicting the Political Alignment of Twitter Users
Conover, M. D., Goncalves, B., Ratkiewicz, J., Flammini, A., & Menczer, F. (2011) · 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing · doi:10.1109/passat/socialcom.2011.34 · Cited by 588
Ratkiewicz (2011) · doi:10.1145/1963192.1963301 · Cited by 541
Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling
Zhou, P., Qi, Z., Zheng, S., Xu, J., Bao, H., & Xu, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06639 · Cited by 434
Mean Birds: Detecting Aggression and Bullying on Twitter
Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., & Vakali, A. (2017) · Proceedings of the 2017 ACM on Web Science Conference · doi:10.1145/3091478.3091487 · Cited by 369
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
Metadata
- Venue
- Proceedings of the International Conference on Security and Cryptography
- Published
- 2010
- Pages
- 142-151
- OpenAlex
- W1993081839
- Cited by
- 462
- References
- 11
- FWCI
- 16.19
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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