# Reconsidering Tweets: Intervening During Tweet Creation Decreases Offensive Content

arXiv: https://arxiv.org/abs/2112.00773 · HTML: https://mlsanity.com/paper/2112.00773/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2021.

## Built on by (most cited first, 11 of 11)

- [Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization](https://mlsanity.com/paper/2206.04007/) (2022)
- [HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter](https://mlsanity.com/paper/2411.15462/) (2024)
- [Can Language Model Moderators Improve the Health of Online Discourse?](https://mlsanity.com/paper/2311.10781/) (2023)
- [The DSA Transparency Database: Auditing Self-reported Moderation Actions by Social Media](https://mlsanity.com/paper/2312.10269/) (2023)
- [The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discourse](https://mlsanity.com/paper/2410.15182/) (2024)
- [Personalized Interventions for Online Moderation](https://mlsanity.com/paper/2205.09462/) (2022)
- [A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter](https://mlsanity.com/paper/2210.06351/) (2022)
- [Context-Aware Detection and Victim-Centered Response Generation for Online Harassment in Private Messaging](https://mlsanity.com/paper/2512.14700/) (2025)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [AI Should Facilitate Democratic Deliberation at Scale](https://mlsanity.com/paper/2609.20059/) (2026)
- [From Web(logs) to Web(AI): Questions, Platforms, and Methods across Twenty Editions of ICWSM](https://mlsanity.com/paper/2610.00205/) (2026)

## Abstract (arXiv)

The proliferation of harmful and offensive content is a problem that many online platforms face today. One of the most common approaches for moderating offensive content online is via the identification and removal after it has been posted, increasingly assisted by machine learning algorithms. More recently, platforms have begun employing moderation approaches which seek to intervene prior to offensive content being posted. In this paper, we conduct an online randomized controlled experiment on Twitter to evaluate a new intervention that aims to encourage participants to reconsider their offensive content and, ultimately, seeks to reduce the amount of offensive content on the platform. The intervention prompts users who are about to post harmful content with an opportunity to pause and reconsider their Tweet. We find that users in our treatment prompted with this intervention posted 6% fewer offensive Tweets than non-prompted users in our control. This decrease in the creation of offensive content can be attributed not just to the deletion and revision of prompted Tweets -- we also observed a decrease in both the number of offensive Tweets that prompted users create in the future and the number of offensive replies to prompted Tweets. We conclude that interventions allowing users to reconsider their comments can be an effective mechanism for reducing offensive content online.