ABSTRACT The presence (and, sometimes, prominence) of incorrect and misleading content on the Web can have serious conse-quences for people who increasingly rely on the internet as their information source for topics such as health, politics, and financial advice. In this paper, we identify and collect several page features (such as popularity among specialized user groups) that are currently difficult or impossible for end-users to assess, yet provide valuable signals regarding credibility. We then present visualizations designed to augment search results and Web pages with the most prom-ising of these features. Our lab evaluation finds that our augmented search results are particularly effective at in-creasing the accuracy of users’ credibility assessments, highlighting the potential of data aggregation and simple interventions to help people make more informed decisions as they search for information online.
Msg#: 4285063 posted 2:14 am on Apr 4, 2011 (gmt 0)
It's a pdf, copying it produces strange results. Read it, they aren't in there, just on the copy pasted here.
I read the article and was stuck with the feeling that the entire process is biased towards the assumption that people are looking for wikipedia type content. The internet isn't only about facts, it's about opinions, and if someone is a regular visitor to your site they trust your opinion. The paper forgets that point, it may rank your opinion poorly if it determines you're not wikipedia-like enough.
the article does give some insight into the future of search:
Content Analysis One approach to helping people find factually correct content is to automatically identify false facts. Extracting factual information from the Web is an active research area. An example of such work is Open Information Extraction . However, natural language approaches are not yet reliable or comprehensive enough for use on the open Web. Some researchers have applied content analysis approaches to specific aspects of credibility. For instance, Dispute Finder  identifies contentious topics by looking for text such as “X is disputed,” and BLEWS  provides insight into online news by analyzing the content and sentiment of blogs referencing particular articles.
So if I write "microsoft sucks" and the algo thinks that's incorrect the article, or entire site, suffers a negative rankings bump. Ironic.