Detecting Malicious Twitter Bots using Machine Learning

Abstract

Abstract: Twitter play a significant role in our daily lives, offering a wide range of opportunities to their users. However, Twitter and online social networks (OSNs) in general are increasingly being utilized by automated accounts, commonly known as bots, as they continue to gain immense popularity across various user demographics. Malicious twitter Bots detection is required to detect real users from fraudulent users because it leads to spreading of spam messages and engage in fraudulent activities. To overcome this, we are going to differentiate bots from legitimate users using feature extraction techniques and find malicious bots and tweets using machine learning algorithm and deep learning architecture known as VGG19 which is combined with the convolutional neural network (CNN) in order to identify whether the tweets are posted by bots or real users and also identifying malicious twitter bots along with malicious URLs. By following these techniques, we can identify the account as bots or real user and prevent spreading of malicious content in the society. The deep learning architecture combined with convolutional neural network to evaluate over a series of experiments using two large real Twitter datasets and compare the experimental results with other machine learning algorithms and provide advantages over other existing techniques like logistic regression targeting the identification of malicious users in social media.