Towards Automatic Bot Detection in Twitter for Health-related Tasks

• Type of Material: Conference Paper
• Authors: Anahita Davoudi, University of Pennsylvania; Ari Klein, University of Pennsylvania; Md Sarker, Emory University; Graciela Gonzalez-Hernandez, University of Pennsylvania
• Language: English
• Date: 2020-05-30
• Publisher: Emory University Libraries
• Publication Version: Final Published Version
• Copyright Statement: ©2020 AMIA - All rights reserved.
• Title of Journal or Parent Work: AMIA Jt Summits Transl Sci Proc
• Conference or Event Name: AMIA Joint Summits on Translational Science
• Start Page: 136
• End Page: 141
• Grant/Funding Information: This study was funded in part by the National Library of Medicine (NLM) (grant number: R01LM011176) and the National Institute on Drug Abuse (NIDA) (grant number: R01DA046619) of the National Institutes of Health (NIH).
• Keywords: health-related information; dataset; Twitter; credibility; automatic bot detection; social media; research
• Research Categories: Biology, Biostatistics; Computer Science; Operations Research; Health Sciences, Education

Abstract

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 not from originating personal accounts. While automatic bot detection approaches have been proposed, none 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 improve bot detection performance significantly. Our approach obtains F1-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.