TY - GEN
T1 - Characterizing and detecting hateful users on twitter
AU - Ribeiro, Manoel Horta
AU - Calais, Pedro H.
AU - Santos, Yuri A.
AU - Almeida, Virgílio A.F.
AU - Meira, Wagner
N1 - Publisher Copyright:
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2018
Y1 - 2018
N2 - Current approaches to characterize and detect hate speech focus on content posted in Online Social Networks (OSNs). They face shortcomings to get the full picture of hate speech due to its subjectivity and the noisiness of OSN text. This work partially addresses these issues by shifting the focus towards users. We obtain a sample of Twitter's retweet graph with 100, 386 users and annotate 4, 972 as hateful or normal, and also find 668 users suspended after 4 months. Our analysis shows that hateful/suspended users differ from normal/active ones in terms of their activity patterns, word usage and network structure. Exploiting Twitter's network of connections, we find that a node embedding algorithm outperforms content-based approaches for detecting both hateful and suspended users. Overall, we present a user-centric view of hate speech, paving the way for better detection and understanding of this relevant and challenging issue.
AB - Current approaches to characterize and detect hate speech focus on content posted in Online Social Networks (OSNs). They face shortcomings to get the full picture of hate speech due to its subjectivity and the noisiness of OSN text. This work partially addresses these issues by shifting the focus towards users. We obtain a sample of Twitter's retweet graph with 100, 386 users and annotate 4, 972 as hateful or normal, and also find 668 users suspended after 4 months. Our analysis shows that hateful/suspended users differ from normal/active ones in terms of their activity patterns, word usage and network structure. Exploiting Twitter's network of connections, we find that a node embedding algorithm outperforms content-based approaches for detecting both hateful and suspended users. Overall, we present a user-centric view of hate speech, paving the way for better detection and understanding of this relevant and challenging issue.
UR - http://www.scopus.com/inward/record.url?scp=85050632406&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050632406&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85050632406
T3 - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
SP - 676
EP - 679
BT - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
PB - AAAI press
T2 - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
Y2 - 25 June 2018 through 28 June 2018
ER -