Today, we have a slew of media houses and streaming services that inundate consumers with audio, video, online, and print news. This has raised the specter of rampant news misinformation and disinformation. The threat is amplified by broadcasters who use the same source to disseminate news to consumers. Any bias in the original news source is perpetrated by all secondary services, thus delivering a limited view of news to consumers. However, researchers at EPFL’s Distributed Information Systems Laboratory (LSIR) in the School of Computer and Communication Sciences have developed an algorithm that can detect such biases and external influences in the news industry.
The research team—Jérémie Rappaz, Dylan Bourgeois, and Karl Aberer—is working with Swiss daily newspaper Le Temps to develop a web-based platform that will model news production around the world, bring transparency in news services, and thus increase public awareness about the threat of disinformation.
After feeding the algorithm about 500 million articles published by 8,000 different sources over the past three years, the LSIR team mapped the articles based on their similarities. Apart from classifying news services in terms of region and topic, the algorithm also detected the influence of media groups on the news they release. By associating a particular media group with its news content, the project aims to build awareness among people about such external influences that lead to a phenomenon called “media concentration.”
Media Observatory is supported by the EPFL-based Initiative for Media Innovation (IMI). The online platform, to be launched next year, will be based on open-source technology and personalized algorithms. Users worldwide, including journalists and news consumers, can access an interactive map to identify patterns that influence the media industry and gain critical insights on how news is covered.