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Real or not? Identifying untrustworthy news websites using third-party partnerships
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Gopal, Ram, Hidaji, Hooman, Kutlu, Sule, Patterson, Raymond A., Rolland, Erik and Zhdanov, Dmitry (2020) Real or not? Identifying untrustworthy news websites using third-party partnerships. ACM Transactions on Management Information Systems, 11 (3). 10. doi:10.1145/3382188
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WRAP-real-not-indentifying-websites-third-party-partnerships-Gopal-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1779Kb) | Preview |
Official URL: https://doi.org/10.1145/3382188
Abstract
Untrustworthy content such as fake news and clickbait have become a pervasive problem on the Internet, causing significant socio-political problems around the world. Identifying untrustworthy content is a crucial step in countering them. The current best-practices for identification involve content analysis and arduous fact-checking of the content. To complement content analysis, we propose examining websites? third-parties to identify their trustworthiness. Websites utilize third-parties, also known as their digital supply chains, to create and present content and help the website function. Third-parties are an important indication of a website?s business model. Similar websites exhibit similarities in the third-parties they use. Using this perspective, we use machine learning and heuristic methods to discern similarities and dissimilarities in third-party usage, which we use to predict trustworthiness of websites. We demonstrate the effectiveness and robustness of our approach in predicting trustworthiness of websites from a database of News, Fake News, and Clickbait websites. Our approach can be easily and cost-effectively implemented to reinforce current identification methods.
| Item Type: | Journal Article | ||||||||
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| Subjects: | H Social Sciences > HE Transportation and Communications J Political Science > JC Political theory P Language and Literature > PN Literature (General) > PN0080 Criticism Q Science > QA Mathematics |
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| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||
| Library of Congress Subject Headings (LCSH): | Fake news , Disinformation, Digital media , Heuristic algorithms, Online journalism, Web sites -- Evaluation, Social conflict in mass media | ||||||||
| Journal or Publication Title: | ACM Transactions on Management Information Systems | ||||||||
| Publisher: | ACM | ||||||||
| ISSN: | 2158-656X | ||||||||
| Official Date: | August 2020 | ||||||||
| Dates: |
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| Volume: | 11 | ||||||||
| Number: | 3 | ||||||||
| Article Number: | 10 | ||||||||
| DOI: | 10.1145/3382188 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © Author | ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Management Information Systems http://dx.doi.org/10.1145/3382188 | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
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