DeFacto: An algorithmic approach to media bias and fake news.

The public’s trust in media is incredibly important in a democracy, but that trust and attention has rightfully degraded because of for example news dilution (CNN’s 24-hour news reporting without real news to report), media bias (with news organisations implicitly allowing personal bias to slip through) and because of the modern fake news trends (lazy journalism or purposeful misleading of readers to degrade trust in media). Stats further show that the U.S. public trust in media has been in steady decline since 1999, but fell even more sharply in 2016 in wake of contentious presidential campaign.

Case DeFacto - Article 2
Case DeFacto - Article 2

As part of the Prototyping Service Design course at Aalto University, we created a concept called DeFacto which would work to restore reasons for the public to trust media. It is a concept idea for a service platform which uses an algorithm to analyse all news on a certain subject and algorithmically verifies and then displays an accurate account of events to reader. Crucially, the platform explains to the reader why a piece of news is considered accurate or not. This approach allows the public to stay informed and, with the media, hold those in power responsible. Additionally, it would educate the public to recognise and better understand fake news & media bias.

Case DeFacto - Article Compare 1
Case DeFacto - Article Compare 1

Thus we wanted to create a service which would cut through the noise of news dilation, media bias and fake news to accurately inform readers and actively educate them about these pitfalls. The project and the prototype we created are based on eight interviews with people based in the US, three of which are current or former journalists.

Team members

Laura Meskanen-Kundu

Liam Turner

Andreas Sode

Involved organisations