# Towards Automatic Bot Detection in Twitter for Health-related Tasks

arXiv: https://arxiv.org/abs/1909.13184 · HTML: https://mlsanity.com/paper/1909.13184/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

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

- [Social media mining for toxicovigilance of prescription medications: End-to-end pipeline, challenges and future work](https://mlsanity.com/paper/2211.10443/) (2022)
- [Public interest in science or bots? Selective amplification of scientific articles on Twitter](https://mlsanity.com/paper/2410.01842/) (2024)

## Abstract (arXiv)

With the increasing use of social media data for health-related research, the credibility of the information from this source has been questioned as the posts may originate from automated accounts or "bots". While automatic bot detection approaches have been proposed, there are none that have been evaluated on users posting health-related information. In this paper, we extend an existing bot detection system and customize it for health-related research. Using a dataset of Twitter users, we first show that the system, which was designed for political bot detection, underperforms when applied to health-related Twitter users. We then incorporate additional features and a statistical machine learning classifier to significantly improve bot detection performance. Our approach obtains F_1 scores of 0.7 for the "bot" class, representing improvements of 0.339. Our approach is customizable and generalizable for bot detection in other health-related social media cohorts.