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arXiv · 2212.05926

LAMBRETTA: Learning to Rank for Twitter Soft Moderation

Pujan Paudel·Jeremy Blackburn·Emiliano De Cristofaro·Savvas Zannettou·Gianluca Stringhini
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Abstract

To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run LAMBRETTA on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.

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Pujan Paudel, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, Gianluca Stringhini. 2022-12-12. LAMBRETTA: Learning to Rank for Twitter Soft Moderation. https://arxiv.org/abs/2212.05926

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