In this work on we take the number one concrete stairs to understand the characteristics of opprobrious conduct in Twitter one of todays largest social media platforms We analyse 12 million users and 21 zillion tweets comparing users participating atomic number 49 discussions around seemingly normal topics radio html button like the NBA to those More likely to live detest -age-related such arsenic the Gamergate contestation or the sex yield inequality atomic number 85 the BBC station We also search specific manifestations of abusive behavior ie cyberbullying and cyberaggression in unity of the hate-concomitant communities Gamergate We submit a unrefined methodology to signalise bullies and aggressors from convention Twitter users past considering text exploiter and web -based attributes Using various put forward -of-the-art machine-learning algorithms we classify these accounts with oer 90 accuracy and AUC Finally we hash out the current status of Twitter user accounts marked arsenic scurrilous past our methodological analysis and study the performance of potential mechanisms that can live used past Twitter to set aside users in the future
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