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Augmenting web pages and search results to support credibility assessment

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Published:07 May 2011Publication History

ABSTRACT

The presence (and, sometimes, prominence) of incorrect and misleading content on the Web can have serious consequences 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 promising of these features. Our lab evaluation finds that our augmented search results are particularly effective at increasing 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.

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        • Published in

          cover image ACM Conferences
          CHI '11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
          May 2011
          3530 pages
          ISBN:9781450302289
          DOI:10.1145/1978942

          Copyright © 2011 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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          Publication History

          • Published: 7 May 2011

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          CHI '11 Paper Acceptance Rate410of1,532submissions,27%Overall Acceptance Rate6,199of26,314submissions,24%

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