Large scale crowdsourcing and characterization of Twitter abusive behavior
Files
Accepted manuscript
Date
2018
DOI
Authors
Founta, Antigoni-Maria
Djouvas, Constantinos
Chatzakou, Despoina
Leontiadis, Ilias
Blackburn, Jeremy
Stringhini, Gianluca
Vakali, Athena
Sirivianos, Michael
Kourtellis, Nicolas
Version
Accepted manuscript
OA Version
Citation
Antigoni-Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, Nicolas Kourtellis. 2018. "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior.." ICWSM, pp. 491 - 500.
Abstract
In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels.By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset
of 80 thousand tweets, which we make publicly available for further scientific exploration.