# A survey on extremism analysis using Natural Language Processing

arXiv: https://arxiv.org/abs/2104.04069 · HTML: https://mlsanity.com/paper/2104.04069/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets](https://mlsanity.com/paper/2405.08562/) (2024)
- [Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks](https://mlsanity.com/paper/2406.17875/) (2024)
- [Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection](https://mlsanity.com/paper/2412.11745/) (2024)

## Abstract (arXiv)

Extremism research has grown as an open problem for several countries during recent years, especially due to the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. Natural Language Processing (NLP) represents a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by this groups, with the final objective of detecting and preventing its spread. This survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a description and comparison of the frequently used NLP techniques, how they were applied, the insights they provided, the most frequently used NLP software tools and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested.