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Social media provides a platform for sharing content and news and provides online marketing landscapes for start-ups and companies in their early phase social media marketing. But, in today’s scenario, we can see that a growing amount of social media content is generated by bots. These bots have been used for harmful tasks such as spreading false information and news, spams, malicious links, and contents, etc. The popularity and open structure of Twitter has attracted a large number of automated programs, known as bots [1]. In our work, we present a framework or model to detect such entities on Twitter and be able to assist human users in identifying who they are interacting with. The proposed classification model used a publicly available dataset of Twitter users, having both humans and bots account for training and testing purposes. We observe and analyze the different characteristics of human and bot in terms of their tweeting behavior, tweet content, and account properties. We also used the Twitter API to extract some additional information that was necessary for classification.

CLASSIFYING TWITTER USER AS A BOT OR NOT AND COMPARING DIFFERENT CLASSIFICATION ALGORITHMS.