Connie Moon Sehat

TL;DR: An initial set of indicators for article credibility defined by a diverse coalition of experts, which originate from both within an article's text as well as from external sources or article metadata are presented.

TL;DR: The authors investigate news credibility assessments by crowds versus experts to understand when and how ratings between them differ, and find differences between expert assessments due to differing expert criteria that journalism versus science experts use, but that also suggest a way to reduce the gap by designing crowd tasks tailored to specific expert criteria.

TL;DR: This work investigates news credibility assessments by crowds versus experts to understand when and how ratings between them differ, and outlines future research directions to better design crowd processes that are tailored to specific crowds and types of content.

TL;DR: This paper collects a set of values that seem most relevant to recommender systems operating across different domains, then examines them from the perspectives of current industry practice, measurement, product design, and policy approaches.

TL;DR: In this article, credibility ratings from two populations: students within journalism or media programs, and crowd workers on UpWork were compared with the ratings of two sets of experts: journalists and climate scientists, on a set of 50 climate-science articles.