ABSTRACT
Social bots are considered to be the most popular type of spamming like spamming malware links, produce fake news, spread rumours to manipulate public opinion. Recently large-scale social bots have been created and are wide spread on social which have a bad impact on public and internet users’ safety in all social media platforms. Bot detection aims to distinguish bots from humans to aid understanding the news or opinions. In recent times, classification of bots in social media have become more as they are populated everywhere. In this paper, we propose a decision tree classifier and a deep learning method to classify bots and humans in Twitter using Twitter API. This proposed model uses what an account has tweeted and cross reference against a bag of words model. These methods are unique that applies deep learning concepts to classification. Using real world data from twitter shows the validity of the model we proposed.
