Towards Automatic Bot Detection in Twitter for Health-related Tasks
This addresses the credibility issue in health-related social media research by providing a customizable bot detection method, though it is incremental as it builds on existing approaches.
The paper tackled the problem of bot detection in health-related Twitter data by extending an existing system, showing it underperformed and then improving it with additional features and a classifier, achieving an F1 score of 0.7 for bots, a 0.339 improvement.
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.