The elements of computer credibility
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Prominence-interpretation theory: explaining how people assess credibility online
CHI '03 Extended Abstracts on Human Factors in Computing Systems
How do users evaluate the credibility of Web sites?: a study with over 2,500 participants
Proceedings of the 2003 conference on Designing for user experiences
IEEE Transactions on Knowledge and Data Engineering
Countering web spam with credibility-based link analysis
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
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
Tweeting is believing?: understanding microblog credibility perceptions
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Reliability prediction of webpages in the medical domain
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
CredibleWeb: a platform for web credibility evaluation
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Defending imitating attacks in web credibility evaluation systems
Proceedings of the 22nd international conference on World Wide Web companion
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The open nature of the World Wide Web makes evaluating webpage credibility challenging for users. In this paper, we aim to automatically assess web credibility by investigating various characteristics of webpages. Specifically, we first identify features from textual content, link structure, webpages design, as well as their social popularity learned from popular social media sites (e.g., Facebook, Twitter). A set of statistical analyses methods are applied to select the most informative features, which are then used to infer webpages credibility by employing supervised learning algorithms. Real dataset-based experiments under two application settings show that we attain an accuracy of 75% for classification, and an improvement of 53% for the mean absolute error (MAE), with respect to the random baseline approach, for regression.