Showing papers by "Raymond A. Patterson published in 2020"
30 Jul 2020
TL;DR: This work uses machine learning and heuristic methods to discern similarities and dissimilarities in third-party usage, which are used to predict trustworthiness of websites and demonstrates the effectiveness and robustness of the approach.
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.
5 citations