BotSpot: Deep Learning Classification of Bot Accounts within Twitter

Autor:	Braker, Christopher, Shiaeles, Stavros, Bendiab, Gueltoum, Savage, Nick, Limniotis, Konstantinos
Rok vydání:	2021
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence Computer Science - Social and Information Networks
Zdroj:	NEW2AN 2020, ruSMART 2020: Internet of Things, Smart Spaces, and Next Generation Networks and Systems pp 165-175
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-030-65726-0_16
Popis:	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. Comment: 7 pages, 3 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.03710 Zobrazit plný text záznamu View this record from Arxiv