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 Multi-layer Perceptron Neural Networks achieved an overall accuracy rate of 92%, which proves the performance of the proposed approach.
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Acknowledgment
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 786698. The work reflects only the authors’ view and the Agency is not responsible for any use that may be made of the information it contains.
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Braker, C., Shiaeles, S., Bendiab, G., Savage, N., Limniotis, K. (2020). BotSpot: Deep Learning Classification of Bot Accounts Within Twitter. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2020 2020. Lecture Notes in Computer Science(), vol 12525. Springer, Cham. https://doi.org/10.1007/978-3-030-65726-0_16
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