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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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Abstract:
Harmful language is frequent in social media, in particular in spaces which are considered anonymous and/or allow free participation. In this paper, we analyze the language in a Telegram channel populated by followers of former US President Donald Trump. We seek to identify the ways in which harmful language is used to create a specific narrative in a group of mostly like-minded discussants. Our research has several aims. First, we create an extended taxonomy of potentially harmful language that includes not only hate speech and direct insults (which have been the focus of existing computational methods), but also other forms of harmful speech discussed in the literature. We manually apply this taxonomy to a large portion of the corpus, including the time period leading up to and the aftermath of the January 2021 US Capitol riot. Our data gives empirical evidence for harmful speech, such as in/out-group divisive language and the use of codes within certain communities, that have not often been investigated before. Second, we compare our manual annotations of harmful speech to several automatic methods for classifying hate speech and offensive language, namely list-based and machine-learning-based approaches. We find that the Telegram data sets still pose particular challenges for these automatic methods. Finally, we argue for the value of studying such naturally-occurring, coherent data sets for research on online harm and how to address it in linguistics and philosophy.
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Keyword:
computational linguistics; corpus linguistics; hate speech; linguistics; offensive language detection; online harm; philosophy; social media; Telegram
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URL: https://doi.org/10.5334/johd.31 https://openhumanitiesdata.metajnl.com/jms/article/view/31
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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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