Autonomous Link Spam Detection in Purely Collaborative Environments

Autonomous Link Spam Detection in Purely Collaborative Environments

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by Andrew G. West , Avantika Agrawal , Phillip Baker , Brittney Exline , Insup Lee

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Datum	Value	Source
TITLE	Autonomous Link Spam Detection in Purely Collaborative Environments	SVM HeaderParse 0.2
AUTHOR NAME	Andrew G. West	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer and Information Science- University of Pennsylvania- Philadelphia, PA	SVM HeaderParse 0.2
AUTHOR NAME	Avantika Agrawal	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer and Information Science- University of Pennsylvania- Philadelphia, PA	SVM HeaderParse 0.2
AUTHOR NAME	Phillip Baker	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer and Information Science- University of Pennsylvania- Philadelphia, PA	SVM HeaderParse 0.2
AUTHOR NAME	Brittney Exline	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer and Information Science- University of Pennsylvania- Philadelphia, PA	SVM HeaderParse 0.2
AUTHOR NAME	Insup Lee	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer and Information Science- University of Pennsylvania- Philadelphia, PA	SVM HeaderParse 0.2
ABSTRACT	Collaborative models (e.g., wikis) are an increasingly prevalent Web technology. However, the open-access that defines such systems can also be utilized for nefarious purposes. In particular, this paper examines the use of collaborative functionality to add inappropriate hyperlinks to destinations outside the host environment (i.e., link spam). The collaborative encyclopedia, Wikipedia, is the basis for our analysis. Recent research has exposed vulnerabilities in Wikipedia’s link spam mitigation, finding that human editors are latent and dwindling in quantity. To this end, we propose and develop an autonomous classifier for link additions. Such a system presents unique challenges. For example, low barriersto-entry invite a diversity of spam types, not just those with economic motivations. Moreover, issues can arise with how a link is presented (regardless of the destination). In this work, a spam corpus is extracted from over 235,000 link additions to English Wikipedia. From this, 40+ features are codified and analyzed. These indicators are computed using wiki metadata, landing site analysis, and external data sources. The resulting classifier attains 64 % recall at 0.5% false-positives (ROC-AUC = 0.97). Such performance could enable egregious link additions to be blocked automatically with low false-positive rates, while prioritizing the remainder for human inspection. Finally, a live Wikipedia implementation of the technique has been developed. Categories andSubject Descriptors H.5.3 [Group and Organization Interfaces]: collaborative	SVM HeaderParse 0.2
CITATIONS	35 found	ParsCit 1.0