TY - JOUR
T1 - Hate speech detection on Twitter
AU - Martín-Del-Campo-Rodríguez, Carolina
AU - Sidorov, Grigori
AU - Batyrshin, Ildar
N1 - Publisher Copyright:
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2021
Y1 - 2021
N2 - With the use of social networks, the automatic detection of hate speech has become of great importance to prevent people, being protected by anonymity, from feeling free to discriminate against different groups. This document describes two approaches taken to detect hate speech by author: the first based on the individual processing of tweets by the author, which establishes a threshold of hate tweets to identify hate speech; the second based in the concatenation of tweets by author for processing.
AB - With the use of social networks, the automatic detection of hate speech has become of great importance to prevent people, being protected by anonymity, from feeling free to discriminate against different groups. This document describes two approaches taken to detect hate speech by author: the first based on the individual processing of tweets by the author, which establishes a threshold of hate tweets to identify hate speech; the second based in the concatenation of tweets by author for processing.
KW - Deep neural network
KW - Hate speech
KW - SVM
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85113517612&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85113517612
SN - 1613-0073
VL - 2936
SP - 2060
EP - 2063
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021
Y2 - 21 September 2021 through 24 September 2021
ER -