# A Longitudinal Dataset of Twitter ISIS Users

arXiv: https://arxiv.org/abs/2202.00878 · HTML: https://mlsanity.com/paper/2202.00878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)

## Abstract (arXiv)

We present a large longitudinal dataset of tweets from two sets of users that are suspected to be affiliated with ISIS. These sets of users are identified based on a prior study and a campaign aimed at shutting down ISIS Twitter accounts. These users have engaged with known ISIS accounts at least once during 2014-2015 and are still active as of 2021. Some of them have directly supported the ISIS users and their tweets by retweeting them, and some of the users that have quoted tweets of ISIS, have uncertain connections to ISIS seed accounts. This study and the dataset represent a unique approach to analyzing ISIS data. Although much research exists on ISIS online activities, few studies have focused on individual accounts. Our approach to validating accounts as well as developing a framework for differentiating accounts' functionality (e.g., propaganda versus operational planning) offers a foundation for future research. We perform some descriptive statistics and preliminary analyses on our collected data to provide deeper insight and highlight the significance and practicality of such analyses. We further discuss several cross-disciplinary potential use cases and research directions.