SciLens News Platform: A System for Real-Time Evaluation of News Articles
This system addresses the need for real-time, trustworthy news evaluation for general readers and domain-experts, though it is incremental as it combines existing methods like social media analysis and expert reviews.
The authors tackled the problem of evaluating news article quality by developing the SciLens News Platform, which automatically collects contextual information and provides quality indicators from social media and journalistic sources, helping users achieve better consensus on article quality while handling thousands of articles daily.
We demonstrate the SciLens News Platform, a novel system for evaluating the quality of news articles. The SciLens News Platform automatically collects contextual information about news articles in real-time and provides quality indicators about their validity and trustworthiness. These quality indicators derive from i) social media discussions regarding news articles, showcasing the reach and stance towards these articles, and ii) their content and their referenced sources, showcasing the journalistic foundations of these articles. Furthermore, the platform enables domain-experts to review articles and rate the quality of news sources. This augmented view of news articles, which combines automatically extracted indicators and domain-expert reviews, has provably helped the platform users to have a better consensus about the quality of the underlying articles. The platform is built in a distributed and robust fashion and runs operationally handling daily thousands of news articles. We evaluate the SciLens News Platform on the emerging topic of COVID-19 where we highlight the discrepancies between low and high-quality news outlets based on three axes, namely their newsroom activity, evidence seeking and social engagement. A live demonstration of the platform can be found here: http://scilens.epfl.ch.