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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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Emotionally Informed Hate Speech Detection: A Multi-target Perspective
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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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Abstract:
With the recent explosion of social media content, the amount of online hate speech is increasing, making it impossible to filter it manually. For automatic hate speech detection, a lot of annotated data is needed, which is mostly available for high-resource languages. In spite of data scarcity in low-resource languages, we want to detect hate speech in those languages. We use cross-lingual embeddings to achieve an acceptable performance in hate speech detection in a target language, using data from another language. We use hate speech comments from English, German, and Croatian. We use fastText word embeddings, align them with the RCSLS method, and achieve reasonable performance in 2 out of 6 language pairs. With Multilingual BERT, we improve upon this method, and achieve acceptable performance in 3 out of 6 language pairs. ...
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Keyword:
BERT language model; cross-lingual embedding; deep learning; hate speech detection; natural language processing; RCSLS method; word embedding
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URL: https://dx.doi.org/10.5281/zenodo.3894644 https://zenodo.org/record/3894644
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