BotSpot: Deep Learning Classification of Bot Accounts within Twitter

TL;DR

This paper presents BotSpot, a deep learning-based method using multilayer perceptrons and nine features to accurately classify Twitter bot accounts, achieving 92% accuracy with a dataset of 860 accounts.

Contribution

The paper introduces a novel bot detection approach utilizing deep learning and automatically collected data, improving accuracy in identifying Twitter bots.

Findings

Achieved 92% accuracy in bot detection.

Developed a web crawler for dataset collection.

Demonstrated effectiveness of deep learning in social media security.

Abstract

The openness feature of Twitter allows programs to generate and control Twitter accounts automatically via the Twitter API. These accounts, which are known as bots, can automatically perform actions such as tweeting, re-tweeting, following, unfollowing, or direct messaging other accounts, just like real people. They can also conduct malicious tasks such as spreading of fake news, spams, malicious software and other cyber-crimes. In this paper, we introduce a novel bot detection approach using deep learning, with the Multi-layer Perceptron Neural Networks and nine features of a bot account. A web crawler is developed to automatically collect data from public Twitter accounts and build the testing and training datasets, with 860 samples of human and bot accounts. After the initial training is done, the Multilayer Perceptron Neural Networks achieved an overall accuracy rate of 92%, which proves the performance of the proposed approach.