# Relationship Between Online Harmful Behaviors and Social Network Message Writing Style

arXiv: https://arxiv.org/abs/2212.07526 · HTML: https://mlsanity.com/paper/2212.07526/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings](https://mlsanity.com/paper/1911.09304/) (2019)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles](https://mlsanity.com/paper/2010.01309/) (2020)
- [25 Tweets to Know You: A New Model to Predict Personality with Social Media](https://mlsanity.com/paper/1704.05513/) (2017)
- [Generalisation of Cyberbullying Detection](https://mlsanity.com/paper/2009.01046/) (2020)

## Abstract (arXiv)

In this paper, we explore the relationship between an individual's writing style and the risk that they will engage in online harmful behaviors (such as cyberbullying). In particular, we consider whether measurable differences in writing style relate to different personality types, as modeled by the Big-Five personality traits and the Dark Triad traits, and can differentiate between users who do or do not engage in harmful behaviors. We study messages from nearly 2,500 users from two online communities (Twitter and Reddit) and find that we can measure significant personality differences between regular and harmful users from the writing style of as few as 100 tweets or 40 Reddit posts, aggregate these values to distinguish between healthy and harmful communities, and also use style attributes to predict which users will engage in harmful behaviors.