# LAMBRETTA: Learning to Rank for Twitter Soft Moderation

arXiv: https://arxiv.org/abs/2212.05926 · HTML: https://mlsanity.com/paper/2212.05926/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2022.

## Built on by (most cited first, 8 of 8)

- [ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails](https://mlsanity.com/paper/2502.13458/) (2025)
- [Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models](https://mlsanity.com/paper/2312.15099/) (2023)
- [Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale](https://mlsanity.com/paper/2308.02068/) (2023)
- [Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text](https://mlsanity.com/paper/2401.09407/) (2024)
- [Enabling Contextual Soft Moderation on Social Media through Contrastive Textual Deviation](https://mlsanity.com/paper/2407.20910/) (2024)
- [Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites](https://mlsanity.com/paper/2501.09102/) (2025)
- [PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter](https://mlsanity.com/paper/2407.20987/) (2024)
- [Quantifying Feature Importance for Online Content Moderation](https://mlsanity.com/paper/2510.19882/) (2025)

## Abstract (arXiv)

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.