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arXiv · 2109.03710

BotSpot: Deep Learning Classification of Bot Accounts within Twitter

Christopher Braker·Stavros Shiaeles·Gueltoum Bendiab·Nick Savage·Konstantinos Limniotis
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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.

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Christopher Braker, Stavros Shiaeles, Gueltoum Bendiab, Nick Savage, Konstantinos Limniotis. 2021-09-08. BotSpot: Deep Learning Classification of Bot Accounts within Twitter. https://doi.org/10.1007/978-3-030-65726-0_16

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