# Testing Hypotheses from the Social Approval Theory of Online Hate: An Analysis of 110 Million Messages from Parler

arXiv: https://arxiv.org/abs/2507.10810 · HTML: https://mlsanity.com/paper/2507.10810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying](https://mlsanity.com/paper/1702.07784/) (2017)
- [Social Approval and Network Homophily as Motivators of Online Toxicity](https://mlsanity.com/paper/2310.07779/) (2023)

## Abstract (arXiv)

We examined how social approval motivates online hate via the social approval theory, which argues social approval signals on hate messages predict more hate and toxicity. Using 110 million messages from Parler (2018-2021), we observed that upvotes on hate speech posts (e.g., messages broadcast on users' profiles to followers' feeds) predicted a lower probability of hate in the next immediate post but more in the following week's post. In other analyses, upvotes on comments (e.g., messages embedded within conversational threads) negatively predicted hate speech in the next comment but positively predicted toxicity during the next week and month. Between-person effects revealed a similar pattern of negative immediate short-term effects but positive longer-term effects for hate speech. For comments, social disapproval (downvotes) moderated these relationships, making them less positive at weekly through quarterly time-intervals and more strongly negative at six-months. Social approval predicts online hate in time- and message-dependent ways.