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  1. Proceedings of the 4th international workshop on Adversarial information retrieval on the web (AIRWeb '08)
  2. A large-scale study of automated web search traffic
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A large-scale study of automated web search traffic
Cleaning search results using term distance features
Analysing features of Japanese splogs and characteristics of keywords
Identifying video spammers in online social networks
Robust PageRank and locally computable spam detection features
Identifying web spam with user behavior analysis
Exploring linguistic features for web spam detection: a preliminary study
Web spam identification through content and hyperlinks
A few bad votes too many?: towards robust ranking in social media
Query-log mining for detecting spam
Latent dirichlet allocation in web spam filtering
The anti-social tagger: detecting spam in social bookmarking systems

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A large-scale study of automated web search traffic

Content Provider ACM Digital Library
Author Buehrer, Greg Stokes, Jack W. Chellapilla, Kumar
Abstract As web search providers seek to improve both relevance and response times, they are challenged by the ever-increasing tax of automated search query traffic. Third party systems interact with search engines for a variety of reasons, such as monitoring a website's rank, augmenting online games, or possibly to maliciously alter click-through rates. In this paper, we investigate automated traffic in the query stream of a large search engine provider. We define automated traffic as any search query not generated by a human in real time. We first provide examples of different categories of query logs generated by bots. We then develop many different features that distinguish between queries generated by people searching for information, and those generated by automated processes. We categorize these features into two classes, either an interpretation of the physical model of human interactions, or as behavioral patterns of automated interactions. We believe these features formulate a basis for a production-level query stream classifier.
Starting Page 1
Ending Page 8
Page Count 8
File Format PDF
ISBN 9781605581590
DOI 10.1145/1451983.1451985
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2008-04-22
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Query Bot Search
Content Type Text
Resource Type Article
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