Autonomous Link Spam Detection in Purely Collaborative Environments
| Autonomous Link Spam Detection in Purely Collaborative Environments | |
| Author(s) | Andrew G. West, Avantika Agrawal, Phillip Baker, Brittney Exline, Insup Lee |
| Published in | WikiSym |
| Date | 2011-10 |
| Volume | Unknown [+] |
| Issue | Unknown [+] |
| Page(s) | 91-100 |
| Keyword(s) | Wikipedia, collaboration, collaborative security, information security, link spam, spam mitigation, reputation, spatio- temporal features, machine-learning, intelligent routing |
| Peer-reviewed? | Yes |
| Language(s) | English |
| License(s) | Unknown [+] |
| Identifiers | |
| ISBN | Unknown [+] |
| DOI | Unknown [+] |
| OCLC Number | Unknown [+] |
| CiteULike | Unknown [+] |
| arXiv | Unknown [+] |
| PubMed | Unknown [+] |
| Related material | |
| Concept(s) | Unknown [+] |
| Tool(s) | STiki |
| Dataset(s) | Unknown [+] |
| Slides | www.cis.upenn.edu |
| Presentation | Not available [+] |
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| Article | BASE, CiteSeerX, Google Scholar |
| Web | Ask, Bing, Google (PDF), Yahoo! |
| Download and mirrors | |
| Local copy | Not available [+] |
| Remote mirror(s) | repository.upenn.edu |
| Archive(s) | Not available [+] |
| Paywall(s) | Not available [+] |
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Autonomous Link Spam Detection in Purely Collaborative Environments is a 2011 conference paper written in English by Andrew G. West, Avantika Agrawal, Phillip Baker, Brittney Exline, Insup Lee and published in WikiSym.
[edit] 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 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.
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