Ranked by Truth Metrics: A New Communication Method Approach, on Crowd-Sourced Fact-Checking Platforms for Journalistic and Social Media Content

Abstract

Fake news, misinformation, and non-true stories create a definite threat to the world's public sphere. Fake news contaminates democracy by blurring the sight and the vision, or by altering the beliefs of citizens on simple everyday matters but also on significant matters such as vaccination, politics, social issues, or public health. Lots of efforts have been conducted in order to tackle the phenomenon. Fact-checking platforms consist of a major step in this issue. Certain cases of fact-checking platforms worldwide seem to work properly and fulfill their strategic goals, although functional and other issues might emerge. This study comes to take the fact-checking platform evolution one step beyond by proposing a new communication model for fake news detection and busting. The proposed model's blueprint is based on the Greek "Ellinika Hoaxes" fact-checking platform with some critical reinforcements: More extensive use of crowdsourcing strategies for detecting and busting non-true stories with the aid of AI chatbots in order not only to bust non-true stories but also to rank news outlets, writers, social media personas and journalists for their credibility. This way, serious news outlets, journalists, and media professionals can build their trust and be ranked for the credibility of their services for a more trustful and democratic public sphere.