Listen to What They Say: Better Understand and Detect Online Misinformation with User Feedback

Abstract

Social media users who report content are key allies in the management of online misinformation; however, no research has been conducted yet to understand their role and the different trends underlying their reporting activity. We suggest an original approach to studying misinformation: examining it from the reporting users’ perspective at the content-level and comparatively across regions and platforms. We propose the first classification of reported content pieces, resulting from a human review of c. 9,000 items reported on Facebook and Instagram in France, the UK, and the US in June 2020. This allows us to observe meaningful distinctions regarding misinformation propagation between countries and platforms as it significantly varies in volume, type, topic, and manipulation technique. Examining six of these techniques, we identify a novel one that is specific to Instagram US and significantly more sophisticated than others, presenting a concrete challenge for algorithmic detection and human moderation. We also identify four reporting behaviours, from which we derive four types of noise capable of explaining 55% of the inaccuracy in misinformation reporting. We finally show that breaking down the user reporting signal into a plurality of behaviours allows us to build a simple classifier trained on a small dataset with a combination of basic users-reports capable of identifying these different types of noise, thus improving the quality of the reporting signal for misinformation detection.