Exploring social bots: A feature-based approach to improve bot detection in social networks

TL;DR

This paper investigates user profile and content features to enhance social bot detection, achieving improved accuracy over existing methods through feature selection and classical machine learning.

Contribution

It introduces a feature-based approach that identifies key features for detecting social bots, surpassing current state-of-the-art performance.

Findings

Certain profile features are highly indicative of bots

Content features significantly improve detection accuracy

Classical machine learning models outperform some existing methods

Abstract

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.