Link spamming Wikipedia for profit
| Link spamming Wikipedia for profit | |
| Author(s) | Andrew G. West, Jian Chang, Krishna Venkatasubramanian, Oleg Sokolsky, Insup Lee |
| Published in | CEAS |
| Date | 2011 |
| Volume | Unknown [+] |
| Issue | Unknown [+] |
| Page(s) | 152-161 |
| Keyword(s) | Wikipedia, attack model, collaborative security, link spam, measurement study, spam economics, web 2.0 spam, wikis |
| Peer-reviewed? | Unknown [+] |
| Language(s) | English |
| License(s) | Unknown [+] |
| Identifiers | |
| ISBN | 978-1-4503-0788-8 |
| DOI | 10.1145/2030376.2030394 |
| OCLC Number | Unknown [+] |
| CiteULike | Unknown [+] |
| arXiv | Unknown [+] |
| PubMed | Unknown [+] |
| Related material | |
| Concept(s) | Unknown [+] |
| Tool(s) | Unknown [+] |
| Dataset(s) | Unknown [+] |
| Slides | Not available [+] |
| Presentation | Not available [+] |
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| Remote mirror(s) | Not available [+] |
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Link spamming Wikipedia for profit is a 2011 conference paper written in English by Andrew G. West, Jian Chang, Krishna Venkatasubramanian, Oleg Sokolsky, Insup Lee and published in CEAS.
[edit] Abstract
Collaborative functionality is an increasingly prevalent web technology. To encourage participation, these systems usually have low barriers-to-entry and permissive privileges. Unsurprisingly, ill-intentioned users try to leverage these characteristics for nefarious purposes. In this work, a particular abuse is examined -- link spamming -- the addition of promotional or otherwise inappropriate hyperlinks.
Our analysis focuses on the wiki model and the collaborative encyclopedia, Wikipedia, in particular. A principal goal of spammers is to maximize *exposure*, the quantity of people who view a link. Creating and analyzing the first Wikipedia link spam corpus, we find that existing spam strategies perform quite poorly in this regard. The status quo spamming model relies on link persistence to accumulate exposures, a strategy that fails given the diligence of the Wikipedia community. Instead, we propose a model that exploits the latency inherent in human anti-spam enforcement.
Statistical estimation suggests our novel model would produce significantly more link exposures than status quo techniques. More critically, the strategy could prove economically viable for perpetrators, incentivizing its exploitation. To this end, we address mitigation strategies.
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