# A Bootstrapped Model to Detect Abuse and Intent in White Supremacist Corpora

arXiv: https://arxiv.org/abs/2008.04276 · HTML: https://mlsanity.com/paper/2008.04276/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Identity Construction in a Misogynist Incels Forum](https://mlsanity.com/paper/2306.15745/) (2023)
- [A Weakly Supervised Classifier and Dataset of White Supremacist Language](https://mlsanity.com/paper/2306.15732/) (2023)

## Abstract (arXiv)

Intelligence analysts face a difficult problem: distinguishing extremist rhetoric from potential extremist violence. Many are content to express abuse against some target group, but only a few indicate a willingness to engage in violence. We address this problem by building a predictive model for intent, bootstrapping from a seed set of intent words, and language templates expressing intent. We design both an n-gram and attention-based deep learner for intent and use them as colearners to improve both the basis for prediction and the predictions themselves. They converge to stable predictions in a few rounds. We merge predictions of intent with predictions of abusive language to detect posts that indicate a desire for violent action. We validate the predictions by comparing them to crowd-sourced labelling. The methodology can be applied to other linguistic properties for which a plausible starting point can be defined.