  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.
