Brittney Exline

Brittney Exline (Alternative names for this author)
Affiliation	Unknown [+]
Country	Unknown [+]
Co-authors	Andrew G. West, Avantika Agrawal, Insup Lee, Phillip Baker
Website	Unknown [+]
Statistics
Authorship	Publications (1), datasets (0), tools (0)
Citations	Total (0), average (0), median (0), max (0), min (0)
Keywords
Collaboration Collaborative security Information security Intelligent routing Machine learning Reputation Spam Spam mitigation Spatio- temporal features Wikipedia
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Brittney Exline is an author.

Publications

Only those publications related to wikis are shown here.

Title	Keyword(s)	Published in	Language	DateThis property is a special property in this wiki.	Abstract	R	C
Autonomous Link Spam Detection in Purely Collaborative Environments	Wikipedia Collaboration Collaborative security Information security Spam Spam mitigation Reputation Spatio- temporal features Machine learning Intelligent routing	WikiSym	English	October 2011	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 barriers-to-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.	0	0