A Survey on Twitter Bot Detection: Comparative Study of Machine Learning and Deep Learning Techniques
Year: 2025
Type: article
Abstract: X (formerly known as Twitter) has emerged as one of the most prominent social networking platforms, attracting diverse users, including individuals, influencers, businesses, and organizations. It allows users to share their content, opinions, news, and multimedia. Recently, there has been growing concern about the significant rise of malicious bots on social media platforms, especially on X. These bots can manipulate online discussions and spread misinformation, potentially exerting considerable influence on communities. This survey examines and conducts a comparative analysis of various machine learning algorithms, including Support Vector Machines (SVM) and Random Forest, as well as deep learning models such as LSTM networks and BiLSTM. The goal is to identify intricate patterns that can improve detection performance and achieve greater accuracy in identifying malicious activity.
Authors: Jyothis Joseph, Nandhitha Binu, Vyshnavi K. B, Nandana Santhosh
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893328355
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