Classification of AI Powered Social Bots on Twitter by Sentiment Analysis and Data Mining through SVM

Abstract

In this paper, the behavior of twitter bots and their influence on the social media is investigated. As the user population increased on Twitter, it became an ideal platform for social manipulation and influencing perspectives. There has been a rise in autonomous entities, which are known to exploit Twitter's API feature by performing actions such as tweeting, retweeting, liking, following, or messaging other users, that engage in social engineering. In this research, a framework based on existing research to detect these autonomous entities on Twitter is presented. For detection, tweet syntax analysis, user behavior along with sentiment analysis is performed. Sentiment analysis is an opinion mining technique which analyzes people's opinions or sentiments. Crawling on Twitter is performed for random tweets, user specific tweets and features are extracted by aggregating the tweets by their senders. Based on the resultant information the human or bot training and classification is made. After successfully training with SVM, this model was able to detect Twitter bots with a precision of 0.75.

Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence

“…It is possible to generate output that mimics human behaviour, or the so-called AI-powered (enabled) social bots with companion generative networks (Foysal, Islam and Rahaman, 2019). LSTM is an artificial recurrent neural networks (RNNs) architecture classified as a deep feedforward neural network for classification of sound and signal categories (Le et al, 2019;Sak, Senior and Beaufays, 2014), handwriting recognition (Graves et al, 2009), speech tagging (Huang, Xu and Yu, 2015) and key phrase extraction as part of NLP (Alzaidy, Caragea and Giles, 2019).…”