Exploring social bots: A feature-based approach to improve bot detection in social networks
This addresses the issue of misinformation and malicious activities spread by bots on social media, which impacts users and platform integrity, but it is incremental as it builds on existing detection methods.
The paper tackled the problem of detecting automated accounts (bots) in social networks by investigating user profile and content features, achieving state-of-the-art performance on several metrics with classical machine learning algorithms and identifying key feature types for detection.
The importance of social media in our daily lives has unfortunately led to an increase in the spread of misinformation, political messages and malicious links. One of the most popular ways of carrying out those activities is using automated accounts, also known as bots, which makes the detection of such accounts a necessity. This paper addresses that problem by investigating features based on the user account profile and its content, aiming to understand the relevance of each feature as a basis for improving future bot detectors. Through an exhaustive process of research, inference and feature selection, we are able to surpass the state of the art on several metrics using classical machine learning algorithms and identify the types of features that are most important in detecting automated accounts.