Providing Web Credibility Assessment Support

Abstract

Presence of information from multiple sources on the internet requires evaluating the credibility of the information, before its utilization. Researchers have suggested that internet users experience difficulty in accessing necessary information and do not pay enough attention to its credibility. We present here the design and implementation of an automated Web Credibility Assessment Support Tool (WebCAST) that considers multiple factors (type of website, popularity, sentiment, date of last update, reputation and review based on users' ratings reflecting personal experience) for assessing the credibility of information and returns a summary indication of the credibility of a website. We use Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method of Multi-Criteria Decision Analysis (MCDA) to give weights to the scale values on each factor, representing the relative importance of the attributes. An empirical evaluation of the tool was conducted by computing the correlation between the tool-generated credibility scores and that of human judges. The correlation was found to be 0.89, thus verifying the validity of the tool. In the future the proposed tool can be made useful to students in their learning process of credibility assessment.

Web Pages Credibility Scores for Improving Accuracy of Answers in Web-Based Question Answering Systems

“…The automated Web Credibility Assessment Support Tool (WebCAST) developed by Aggarwal, et al [31] is one such system that covers a wide range of credibility factors and addresses all seven categories. The WebCAST system an upgrade of its predecessor [30] and provides credibility data such as author details, update date, domain type, Website ranking, and tone of content from realtime databases including Alchemy API, Alexa API, Google API, World of Trust API [31]. The credibility factors used in existing Web-based QA systems and information systems allowed this research to map the ones available under each credibility category.…”

“…A Webpage's content creation or updating date is extracted using Diffbot (2016) API. Currency score is assigned depending upon the bracket range the date belongs to, as specified by Aggarwal, et al [31] given in Appendix-I. Web pages published or updated within the last year are given maximum credibility score.…”

Developing a Web Credibility Evaluation Tool Using PROMETHEE Method

“…This type can be obtained based on prestigious awards, authors' expertise and so on (Liu, 2004). Aggarwal, Van Oostendorp, Reddy, & Indurkhya (2014) focused on specific factors when they implemented their system regards to web evaluation. These factors are webpage type, sentiments outcome, google page ranking, latest update, reputation and reviews.…”

“…Of course, this was because of the limited number of factors that were incorporated. Aggarwal & Oostendorp & Reddy & Indurkhya (2014) developed WebCAST to evaluate web credibility in order to produce a summary indication of web credibility. They likewise utilized the PAPRIKA method as it assigns specific weights to factors which represent the significance of the factors.…”

Supporting Credibility Assessment of News in Social Media using Star Ratings and Alternate Sources

“…Wang et al [9] explored the potential for search engines to verify facts by presenting users with search results about questionable claims made on websites. Other work has explored the potential for users to provide subjective credibility ratings of webpages to support the judgments of future readers [4] and for such ratings to be obtained through automated techniques [1]. Similarly, services such as TweetCred 1 offer algorithmically generated assessments in the form of a numeric 'credibility score' [3] that can provide users with an additional resource for judging the credibility of information found online.…”