We found that these selected profiles keep to a narrow theme with lower diversity in hashtags, URLs, and domains, they are thematically similar to each other (in a coordinated manner, if not through intent), and have a high likelihood of bot-like behavior (likely to have progenitors with intentions to influence). From this data, we selected the most extreme profiles in terms of consistency of toxic content and examined their tweet texts, and the domains, hashtags, and URLs they shared. Our data spans 14 years of tweets from 122K Twitter profiles and more than 293M tweets. Going beyond past works, we perform a longitudinal study of a large selection of Twitter profiles, which enables us to characterize profiles in terms of how consistently they post highly toxic content. The main goal of identification is to curb natural and mechanical misconduct and make OSNs a safer place for social discourse. The research to date tends to focus on the deployment of machine learning to identify and classify types of misbehavior such as bullying, aggression, and racism to name a few.
Misbehavior in online social networks (OSN) is an ever-growing phenomenon.
