Bot and gender identification from twitter notebook for PAN at CLEF 2019

Abstract

The popularity of social media raises a concern about the quality of content over its platforms. The quality of data is important, especially for fair and considerable predictive analysis. If the quality of data is less, it may result in the prediction of wrong circumstances of an event. This causes misleading trending problems and more importantly, the sensitive stock price may fluctuate. The contents available on social media can be corrupted and overflowed by bots. There are a variety of bots available such as Spam Bots, Influence Bots, etc. Our target is to identify such bots on Twitter. Twitter data is mostly used by data analysts for applications related to scientific predictions or opinion analysis. This working note is capitalized on earlier approaches and Machine Learning (ML) approaches used to classify between a bot and human and find the gender further for interesting studies in crime detection etc. By sharing many attributes for user profiles, we have identified the pattern to find out that the given user is a bot or human based on the tweets posted. � 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CLEF 2019, 9-12 September 2019, Lugano, Switzerland.