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Emotionally Informed Hate Speech Detection: A Multi-target Perspective
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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)
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Multiword Expression Features for Automatic Hate Speech Detection
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In: NLDB 2021 - 26th International Conference on Natural Language & Information Systems ; https://hal.archives-ouvertes.fr/hal-03231047 ; NLDB 2021 - 26th International Conference on Natural Language & Information Systems, Jun 2021, Saarbrücken/Virtual, Germany ; http://nldb2021.sb.dfki.de/ (2021)
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Hate speech and offensive language detection using transfer learning approaches ; Détection du discours de haine et du langage offensant utilisant des approches de Transfer Learning
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In: https://tel.archives-ouvertes.fr/tel-03276023 ; Document and Text Processing. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAS007⟩ (2021)
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The Telegram Chronicles of Online Harm
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In: Journal of Open Humanities Data; Vol 7 (2021); 8 ; 2059-481X (2021)
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A Language Model for Misogyny Detection in Latin American Spanish Driven by Multisource Feature Extraction and Transformers
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection ...
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SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection ...
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SWSR: A Chinese Dataset and Lexicon for Sexist Hate Speech Detection ...
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SWSR: A Chinese Dataset and Lexicon for Sexist Hate Speech Detection ...
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| 10 |
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
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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.
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Keyword:
Affective resources; Hate speech detection; Hate speech targets; Multi-task learning; Social media
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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
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| 11 |
Towards multidomain and multilingual abusive language detection: a survey
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Hate speech and topic shift in the covid-19 public discourse on social media in Italy
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Achieving Hate Speech Detection in a Low Resource Setting
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In: All Graduate Theses and Dissertations (2021)
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Application-Oriented Approach for Detecting Cyberaggression in Social Media
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In: International Conference on Applied Human Factors and Ergonomics ; https://hal.archives-ouvertes.fr/hal-02903422 ; International Conference on Applied Human Factors and Ergonomics, Jul 2020, San Diego, United States. pp.129-136, ⟨10.1007/978-3-030-51328-3_19⟩ ; https://link.springer.com/chapter/10.1007%2F978-3-030-51328-3_19 (2020)
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“Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media
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Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions
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Multilingual and Multitarget Hate Speech Detection in Tweets
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In: Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts ; Conférence sur le Traitement Automatique des Langues Naturelles (TALN - PFIA 2019) ; https://hal.archives-ouvertes.fr/hal-02567777 ; Conférence sur le Traitement Automatique des Langues Naturelles (TALN - PFIA 2019), Jul 2019, Toulouse, France. pp.351-360 ; https://www.aclweb.org/anthology/2019.jeptalnrecital-court.21/ (2019)
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IMT Mines Ales at HASOC 2019: Automatic Hate Speech Detection
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In: FIRE 2019 - 11th Forum for Information Retrieval Evaluation ; https://hal.mines-ales.fr/hal-02427843 ; FIRE 2019 - 11th Forum for Information Retrieval Evaluation, Dec 2019, Kolkata, India. p.279-284 ; http://ceur-ws.org/Vol-2517/ (2019)
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Cross-lingual embeddings for hate speech detection in comments ...
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Cross-lingual embeddings for hate speech detection in comments ...
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