TY - GEN
T1 - Detecting aggressors and bullies on twitter
AU - Chatzakou, Despoina
AU - Kourtellis, Nicolas
AU - Blackburn, Jeremy
AU - De Cristofaro, Emiliano
AU - Stringhini, Gianluca
AU - Vakali, Athena
N1 - Publisher Copyright: © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
PY - 2017
Y1 - 2017
N2 - Online social networks constitute an integral part of people's every day social activity and the existence of aggressive and bullying phenomena in such spaces is inevitable. In this work, we analyze user behavior on Twitter in an effort to detect cyberbullies and cuber-aggressors by considering specific attributes of their online activity using machine learning classifiers.
AB - Online social networks constitute an integral part of people's every day social activity and the existence of aggressive and bullying phenomena in such spaces is inevitable. In this work, we analyze user behavior on Twitter in an effort to detect cyberbullies and cuber-aggressors by considering specific attributes of their online activity using machine learning classifiers.
UR - https://www.scopus.com/pages/publications/85058676770
U2 - 10.1145/3041021.3054211
DO - 10.1145/3041021.3054211
M3 - Conference contribution
T3 - 26th International World Wide Web Conference 2017, WWW 2017 Companion
SP - 767
EP - 768
BT - 26th International World Wide Web Conference 2017, WWW 2017 Companion
PB - International World Wide Web Conferences Steering Committee
T2 - 26th International World Wide Web Conference, WWW 2017 Companion
Y2 - 3 April 2017 through 7 April 2017
ER -