Abstract
This paper addresses the challenge of detecting spam URLs in social media, which is an important task for shielding users from links associated with phishing, malware, and other low-quality, suspicious content. Rather than rely on traditional blacklist-based filters or content analysis of the landing page for Web URLs, we examine the behavioral factors of both who is posting the URL and who is clicking on the URL. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. Concretely, we propose and evaluate fifteen click and posting-based features. Through extensive experimental evaluation, we find that this purely behavioral approach can achieve high precision (0.86), recall (0.86), and area-under-the-curve (0.92), suggesting the potential for robust behavior-based spam detection.
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Cao, C., Caverlee, J. (2015). Detecting Spam URLs in Social Media via Behavioral Analysis. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_77
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DOI: https://doi.org/10.1007/978-3-319-16354-3_77
Publisher Name: Springer, Cham
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