Abstract
Researchers have designed features of Twitter accounts that help machine learning algorithms to detect spammers. Spammers try to evade detection by manipulating such features. This has led to the design of robust features, i.e., features that are hard to manipulate. In this paper, we propose and evaluate five new robust features. © 2014 Springer International Publishing.
Cite
CITATION STYLE
Karim, M. R., & Zilles, S. (2014). Robust features for detecting evasive spammers in Twitter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8436 LNAI, pp. 295–300). Springer Verlag. https://doi.org/10.1007/978-3-319-06483-3_28
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