2024/11/10 by Salvador López-Joya, Lopez-Joya, Salvador, Jose A. Diaz-Garcia +5 · 1 citation
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Misinformation and Its Impacts #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Spam and Phishing Detection
paper · pdf · doi:10.48550/arxiv.2411.06626
openalex publication_date 2024/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.