1f:T400,The physical world is rife with cues that allow us to distinguish between safe and unsafe situations. By contrast, the Internet offers a much more ambiguous environment; hence many users are unable to distinguish a scam from a legitimate Web page. To help address this problem, we explore how to train classifiers that can automatically identify malicious Web pages based on clues from their textual content, structural tags, page links, visual appearance, and URLs. Using a contemporary labeled data feed from a large Web mail provider, we extract such features and demonstrate how they can be used to improve classification accuracy over previous, more constrained approaches. In particular, by analyzing the full content of individual Web pages, we more than halve the error rate obtained by a comparably trained classifier that only extracts features from URLs. By training classifiers on different sets of features, we are further able to assess the strength of clues provided by these different sources of information.20:T400,Wikipedia is presently the largest free-and-open online encyclopedia collaboratively edited and maintained by volunteers. While Wikipedia offers full-text search to its users, the accuracy of its relevance-based search can be compromised by poor quality articles edited by non-experts and inexperienced contributors. In this paper, we propose a framework that re-ranks Wikipedia search results considering article quality. We develop two quality measurement models, namely Basic and PeerReview, to derive article quality based on co-authoring data gathered from articles' edit history. Compared withWikipedia's full-text search engine, Google and Wikiseek, our experimental results showed that (i) quality-only ranking produced by PeerReview gives comparable performance to that of Wikipedia and Wikiseek; (ii) PeerReview combined with relevance ranking outperforms Wikipedia's full-text search significantly, delivering search accuracy comparable to Google.21:Teee,

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