The elements of computer credibility
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
What makes Web sites credible?: a report on a large quantitative study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Stable algorithms for link analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Attack detection in time series for recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Profile Injection Attacks in Collaborative Recommender Systems
CEC-EEE '06 Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Sentiment Bias Detection in Support of News Credibility Judgment
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
Enhancing credibility judgment of web search results
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Augmenting web pages and search results to support credibility assessment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A decentralized recommender system for effective web credibility assessment
Proceedings of the 21st ACM international conference on Information and knowledge management
Web credibility: features exploration and credibility prediction
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Unlike traditional media such as television and newspapers, web contents are relatively easy to be published without being rigorously fact-checked. This seriously influences people's daily life if non-credible web contents are utilized for decision making. Recently, web credibility evaluation systems have emerged where web credibility is derived by aggregating ratings from the community (e.g., MyWOT). In this paper, We focus on the robustness of such systems by identifying a new type of attack scenario where an attacker imitates the behavior of trustworthy experts by copying system's credibility ratings to quickly build high reputation and then attack certain web contents. In order to defend this attack, we propose a two-stage defence algorithm. At stage 1, our algorithm applies supervised learning algorithm to predict the credibility of a web content and compare it with a user's rating to estimate whether this user is malicious or not. In case the user's maliciousness can not be determined with high confidence, the algorithm goes to stage 2 where we investigate users' past rating patterns and detect the malicious one by applying hierarchical clustering algorithm. Evaluation using real datasets demonstrates the efficacy of our approach.