"Study Finds Bot Detection Software Isn't as Accurate as It Seems"
The challenges posed by bots on social media continue to be diverse, ranging from the minor annoyance of spamming to the potentially grave issues of spreading misinformation, influencing elections, and inflaming polarization. Recent research suggests that existing third-party bot detection tools may not be as accurate as they appear. MIT researchers Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, and Philipp Zimmer report in a recently published paper that bot detection models' supposedly high accuracy rates result from a critical limitation in the data used to train them. Much research is dedicated to developing tools that distinguish between humans and bots. Social media platforms have their systems for identifying and removing bot accounts, but these systems are often kept secret. Third-party bot-detection tools use curated data sets and sophisticated Machine Learning (ML) models trained on those data sets to identify patterns believed to be human or not human. These models are then deployed on social media to analyze the operation of bots. This article continues to discuss the study on the accuracy of bot detection software.