Home
Catalogue search
Refine your search:
Keyword
Creator / Publisher:
Benamara, Farah (3)
Chiril, Patricia (3)
Moriceau, Véronique (3)
Pamungkas, Endang Wahyu (3)
Patti, Viviana (3)
Institut de recherche en informatique de Toulouse (IRIT) (1)
MEthodes et ingénierie des Langues, des Ontologies et du DIscours (IRIT-MELODI) (1)
Open access funding provided by Università degli Studi di Torino within the CRUI-CARE Agreement (1)
Progetto di Ateneo / CSP 2016 (Immigrants, Hate and Prejudice in Social Media, S1618.L2.BOSC.01) (1)
Project “Be Positive!” (under the 2019 “Google.org Impact Challenge on Safety” call) (1)
more
Year
Medium:
Online (3)
Type:
Article (3)
BLLDB-Access
Search in the Catalogues and Directories
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
Sort by
creator [A → Z]
'
creator [Z → A]
'
publishing year ↑ (asc)
'
publishing year ↓ (desc)
'
title [A → Z]
'
title [Z → A]
'
Simple Search
Hits 1 – 3 of 3
1
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
Chiril, Patricia
;
Pamungkas, Endang Wahyu
;
Benamara, Farah
...
In: ISSN: 1866-9956 ; EISSN: 1866-9964 ; Cognitive Computation ; https://hal.archives-ouvertes.fr/hal-03275549 ; Cognitive Computation, Springer, 2021, 13 (4), ⟨10.1007/s12559-021-09862-5⟩ ; https://link.springer.com/article/10.1007%2Fs12559-021-09862-5 (2021)
BASE
Show details
2
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
Chiril, Patricia
;
Pamungkas, Endang Wahyu
;
Benamara, Farah
...
In: Cognit Comput (2021)
BASE
Show details
3
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
Chiril, Patricia
;
Pamungkas, Endang Wahyu
;
Benamara, Farah
;
Moriceau, Véronique
;
Patti, Viviana
. - 2021
Abstract:
Hate Speech and harassment are widespread in online communication, due to users’ freedom and anonymity and the lack of regulation provided by social media platforms. Hate speech is topically focused (misogyny, sexism, racism, xenophobia, homophobia, etc.), and each specific manifestation of hate speech targets different vulnerable groups based on characteristics such as gender (misogyny, sexism), ethnicity, race, religion (xenophobia, racism, Islamophobia), sexual orientation (homophobia), and so on. Most automatic hate speech detection approaches cast the problem into a binary classification task without addressing either the topical focus or the target-oriented nature of hate speech. In this paper, we propose to tackle, for the first time, hate speech detection from a multi-target perspective. We leverage manually annotated datasets, to investigate the problem of transferring knowledge from different datasets with different topical focuses and targets. Our contribution is threefold: (1) we explore the ability of hate speech detection models to capture common properties from topic-generic datasets and transfer this knowledge to recognize specific manifestations of hate speech; (2) we experiment with the development of models to detect both topics (racism, xenophobia, sexism, misogyny) and hate speech targets, going beyond standard binary classification, to investigate how to detect hate speech at a finer level of granularity and how to transfer knowledge across different topics and targets; and (3) we study the impact of affective knowledge encoded in sentic computing resources (SenticNet, EmoSenticNet) and in semantically structured hate lexicons (HurtLex) in determining specific manifestations of hate speech. We experimented with different neural models including multitask approaches. Our study shows that: (1) training a model on a combination of several (training sets from several) topic-specific datasets is more effective than training a model on a topic-generic dataset; (2) the multi-task approach outperforms a single-task model when detecting both the hatefulness of a tweet and its topical focus in the context of a multi-label classification approach; and (3) the models incorporating EmoSenticNet emotions, the first level emotions of SenticNet, a blend of SenticNet and EmoSenticNet emotions or affective features based on Hurtlex, obtained the best results. Our results demonstrate that multitarget hate speech detection from existing datasets is feasible, which is a first step towards hate speech detection for a specific topic/target when dedicated annotated data are missing. Moreover, we prove that domain-independent affective knowledge, injected into our models, helps finer-grained hate speech detection.
Keyword:
Affective resources
;
Hate speech detection
;
Hate speech targets
;
Multi-task learning
;
Social media
URL:
https://doi.org/10.1007/s12559-021-09862-5
http://hdl.handle.net/2318/1792620
http://link.springer.com/article/10.1007/s12559-021-09862-5
BASE
Hide details
Mobile view
All
Catalogues
UB Frankfurt Linguistik
0
IDS Mannheim
0
OLC Linguistik
0
UB Frankfurt Retrokatalog
0
DNB Subject Category Language
0
Institut für Empirische Sprachwissenschaft
0
Leibniz-Centre General Linguistics (ZAS)
0
Bibliographies
BLLDB
0
BDSL
0
IDS Bibliografie zur deutschen Grammatik
0
IDS Bibliografie zur Gesprächsforschung
0
IDS Konnektoren im Deutschen
0
IDS Präpositionen im Deutschen
0
IDS OBELEX meta
0
MPI-SHH Linguistics Collection
0
MPI for Psycholinguistics
0
Linked Open Data catalogues
Annohub
0
Online resources
Link directory
0
Journal directory
0
Database directory
0
Dictionary directory
0
Open access documents
BASE
3
Linguistik-Repository
0
IDS Publikationsserver
0
Online dissertations
0
Language Description Heritage
0
© 2013 - 2024 Lin|gu|is|tik
|
Imprint
|
Privacy Policy
|
Datenschutzeinstellungen ändern