Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter Mouton December 12, 2023

Offensive language in media discussion forums: A pragmatic analysis

  • Olga Dontcheva Navratilova

    Olga Dontcheva-Navratilova is Associate Professor of English Linguistics at the Faculty of Education, Masaryk University, Czech Republic. Her research interests include English for academic and specific purposes and political discourse. She has published the books Analysing Genre: The Colony Text of UNESCO Resolutions (2009), Coherence in Political Speeches (2011) and co-authored Persuasion in Specialised Discourses (2020). She is co-editor of the journal Discourse and Interaction.

    ORCID logo
    and Renata Povolná

    Renata Povolná is Associate Professor of English Linguistics at the Faculty of Education, Masaryk University, Czech Republic. Her research lies in the area of discourse analysis, pragmatics and conversation analysis. She has published the books Spatial and Temporal Adverbials in English Authentic Face-to-Face Conversation (2003), Interactive Discourse Markers in Spoken English (2010) and co-authored Persuasion in Specialised Discourses (2020). She is co-editor of the journal Discourse and Interaction.

    ORCID logo
From the journal Lodz Papers in Pragmatics

Abstract

This study intends to contribute to the delimitation of selected offensive language categories based on an analysis of a corpus of contributions to discussion forums in Czech online national newspapers and news platforms called Czech Corpus of Offensive Language (CCOL). It endeavours to study three problematic areas (1) delimitation between the speech acts performed, (ii) lexical realisation of specific properties of the target and (iii) identification and categorisation of implicit offence (e.g. figurative semantic shifts) by exploring contextual cues for the speech act identification, the keywords indicating the properties of the target and the types of semantic shifts in implicit expressions of offence. The findings indicate that annotation systems that do not use context information for the detection of offensive language may face problems with adequate interpretation of the language means under investigation.

About the authors

Olga Dontcheva Navratilova

Olga Dontcheva-Navratilova is Associate Professor of English Linguistics at the Faculty of Education, Masaryk University, Czech Republic. Her research interests include English for academic and specific purposes and political discourse. She has published the books Analysing Genre: The Colony Text of UNESCO Resolutions (2009), Coherence in Political Speeches (2011) and co-authored Persuasion in Specialised Discourses (2020). She is co-editor of the journal Discourse and Interaction.

Renata Povolná

Renata Povolná is Associate Professor of English Linguistics at the Faculty of Education, Masaryk University, Czech Republic. Her research lies in the area of discourse analysis, pragmatics and conversation analysis. She has published the books Spatial and Temporal Adverbials in English Authentic Face-to-Face Conversation (2003), Interactive Discourse Markers in Spoken English (2010) and co-authored Persuasion in Specialised Discourses (2020). She is co-editor of the journal Discourse and Interaction.

References

Archard, David. 2014. Insults, free speech and offensiveness. Journal of Applied Philosophy 31(2). 127–141.10.1111/japp.12048Search in Google Scholar

Austin, John L. 1962. How to Do Things with Words. Oxford: Oxford University Press.Search in Google Scholar

Ayass, Ruth & Cornelia Gerhardt. 2012. The Appropriation of Media in Everyday Life. Amsterdam: John Benjamins.10.1075/pbns.224Search in Google Scholar

Chovanec, Jan. 2018. Participating with media: exploring online media activity. In Colleen Cotter & Daniel Perrin (eds.), The Routledge Handbook of Language and Media, 505–522. London: Routledge.10.4324/9781315673134-37Search in Google Scholar

Clark, Herbert H. 1996. Using Language. Cambridge: Cambridge University Press.Search in Google Scholar

Culpeper, Jonathan & Jane Demmen. 2015. Keywords. In Douglas Biber & Randi Reppen (eds.), The Cambridge Handbook of English Corpus Linguistics, 90–105. Cambridge: Cambridge University Press.10.1017/CBO9781139764377.006Search in Google Scholar

Davidson, Thomas, Dana Warmsley, Michael Macy & Ingmar Weber. 2017. Automated hate speech detection and the problem of offensive language. In Proceedings of the International AAAI Conference on Web and Social Media 11(1). 512–515. DOI: https://doi.org/10.1609/icwsm.v11i1.14955.10.1609/icwsm.v11i1.14955Search in Google Scholar

Dontcheva-Navratilova, Olga & Renata Povolná. 2023. Czech Offensive Language: Testing a Simplified Offensive Language Taxonomy. In Sara Carvalho, Anas Fahad Khan, Ana Ostroški Anić, Blerina Spahiu, Jorge Gracia, John P. McCrae, Dagmar Gromann, Barbara Heinisch & Ana Salgado (eds.), LDK 2023 proceedings, 627–632. Lisbon: NOVA CLUNL Available at: https://aclanthology.org/2023.ldk-1.68.pdf (accessed 30 October 2023).Search in Google Scholar

Fortuna, Paula, Juan Soler-Company & Leo Wanner. 2021. How well do hate speech, toxicity, abusive and offensive language classification models generalize across datasets? Information Processing & Management 58(3). 102524.10.1016/j.ipm.2021.102524Search in Google Scholar

Grice, Paul H. 1967. Logic and conversation. In Paul Grice (ed.), Studies in the Way of Words, 41–58. Cambridge MA: Harvard University Press.10.1163/9789004368811_003Search in Google Scholar

Groom, Nicholas. 2005. Pattern and meaning across genres and disciplines: An exploratory study. Journal of English for Academic Purposes 4(3). 257–277.10.1016/j.jeap.2005.03.002Search in Google Scholar

Groom, Nicholas. 2010. Closed-class keywords and corpus-driven discourse analysis. In Marina Bondi & Mike Scott (eds.), Keyness in Texts, 59–78. Amsterdam: John Benjamins.10.1075/scl.41.05groSearch in Google Scholar

Kogilavani, Shanmuga V., Subramanian Malliga, K. R. Jaiabinaya, M. Malini, M. Manisha Kokila. 2021. Characterization and mechanical properties of offensive language taxonomy and detection techniques. Materials Today: Proceedings, Vol. 81, Part 2. 630–633. Amsterdam: Elsevier. Available at: https://doi.org/10.1016/j.matpr.2021.04.102 (accessed 30 August 2023).10.1016/j.matpr.2021.04.102Search in Google Scholar

Landis, Richard & Gari Koch. 1997. The measurement of observer agreement for categorical data. Biometrics 33. 159–174.10.2307/2529310Search in Google Scholar

Lewandowska-Tomaszczyk, Barbara, Slavko Žitnik, Chaya Liebeskind, Geidre Valunaite Oleskevicienė, Anna Bączkowska, Paul A. Wilson, Marcin Trojszczak, Ivana Brač, Lobel Filipić, Ana Ostroški Anić, Olga Dontcheva-Navratilova, Agnieszka Borowiak, Kristina Despot & Jelena Mitrović. 2023. Annotation scheme and evaluation: The case of OFFENSIVE language. Rasprave 49(1). 155–175.10.31724/rihjj.49.1.8Search in Google Scholar

Madhu, Hiren, Shrey Satapara, Sandip Modha, Thomas Mandl & Prasenjit Majumder. 2023. Detecting offensive speech in conversational code-mixed dialogue on social media: A contextual dataset and benchmark experiments. Expert Systems with Applications 2015, 119342.10.1016/j.eswa.2022.119342Search in Google Scholar

Menini, Stefano, Alessio Palmero Aprosio & Sara Tonelli. 2021. Abuse is contextual, what about nlp? The role of context in abusive language annotation and detection. arXiv preprint arXiv:2103.14916.Search in Google Scholar

Miller, Don. 2020. Analysing frequency lists. In Magali Paquot & Stefan Gries (eds.), A Practical Handbook of Corpus Linguistics, 77–98. Cham: Springer.10.1007/978-3-030-46216-1_4Search in Google Scholar

Moulson, Geir. 2016. Zuckerberg in Germany: No place for hate speech on facebook. Available at: http://abcnews.go.com/Technology/wireStory/zukerberg-place-hate-speech-facebook-37217309 (accessed 10 September 2023).Search in Google Scholar

Nobata, Chikashi, Joel Tetreault, Achint Thomas, Yashar Mehdad & Yi Chang. 2016. Abusive language detection in online user content. In Proceedings of the 25th International Conference on World Wide Web, 145–153. Geneva: International World Wide Web Conferences Steering Committee.10.1145/2872427.2883062Search in Google Scholar

Norrick, Neal R. 1978. Expressive illocutionary acts. Journal of Pragmatics 2(3). 277–291.10.1016/0378-2166(78)90005-XSearch in Google Scholar

Piskorska, Agnieszka. 2017. On the strength of explicit and implicit verbal offences: A relevance-theoretic view. In Silvia Bonacchi (ed.), Verbale Aggression: Multidisziplinäre Zugänge zur verletzenden Macht der Sprache, 51–72. Berlin: De Gruyter.10.1515/9783110522976-003Search in Google Scholar

Plaza-del-Arco, Flor Miriam, M. Dolores Molina-González, L. Alfonso Ureña-López & María-Teresa Martín-Valdivia. 2022. Integrating implicit and explicit linguistic phenomena via multi-task learning for offensive language detection. Knowledge-Based Systems 258. 109965.10.1016/j.knosys.2022.109965Search in Google Scholar

Rayson, Paul & Amanda Potts. 2020. Analysing keyword lists. In Magali Paquot & Stefan Th. Gries (eds.), A Practical Handbook of Corpus Linguistics, 199–140. Cham: Springer.10.1007/978-3-030-46216-1_6Search in Google Scholar

Ronan, Patricia. 2015. Categorizing expressive speech acts in the pragmatically annotated SPICE Ireland corpus. ICAME Journal 39. 25–45.10.1515/icame-2015-0002Search in Google Scholar

Searle, John. 1969. Speech Acts. Cambridge: Cambridge University Press.Search in Google Scholar

Searle, John. 1976. A classification of illocutionary acts. Language in Society 5. 1–23.10.1017/S0047404500006837Search in Google Scholar

Sim, Juůius & Chris Wright. 2005. The Kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Physical Therapy 85(3). 257–268.10.1093/ptj/85.3.257Search in Google Scholar

Taulé, Mariona, Alejandro Ariza, Montserrat Nofre, Enrique Amigó & Paolo Rosso. 2021. Overview of the DETOXIS task at IberLEF-2021: Detection of toxicity in comments in Spanish. Inicio 67. 209–221.Search in Google Scholar

Wales, Katie. 2011. A Dictionary of Stylistics. 3rd ed. Oxon: Routledge.Search in Google Scholar

Walton, Douglas. 2000. The speech act of making a threat. In Douglas Walton, Scare Tactics. Argumentation Library,101–128. Cham: Springer.10.1007/978-94-017-2940-6_4Search in Google Scholar

Talat, Zeerak, James Thorne & Joachim Bingel. 2018. Bridging the gaps: Multi-task learning for domain transfer of hate speech detection. In Jennifer Golbeck (ed.), Online Harassment. Human–Computer Interaction Series. 1st ed, 29–55. Cham: Springer.10.1007/978-3-319-78583-7_3Search in Google Scholar

Wiegand, Michael, Josef Ruppenhofer & Elisabeth Eder. 2021. Implicitly abusive language – what does it actually look like and why are we not getting there? In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Online, 576–587. Available at: https://aclanthology.org/2021.naaclmain.48 (accessed 10 August 2023).10.18653/v1/2021.naacl-main.48Search in Google Scholar

Wiegand, Michael, Josef Ruppenhofer & Thomas Kleinbauer. 2019. Detection of abusive language: the problem of biased datasets. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1 (Long and Short Papers), 602–608. Minneapolis: Association for Computational Linguistics.Search in Google Scholar

Zampieri, Marcos, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra & Ritesh Kumar. 2019. SemEval-2019 Task 6: Identifying and categorizing offensive language in social media (OffensEval). In Proceedings of the 13th International Workshop on Semantic Evaluation, 75–86. Minneapolis, Minnesota, USA: Association for Computational Linguistics.10.18653/v1/S19-2010Search in Google Scholar

Published Online: 2023-12-12
Published in Print: 2023-12-15

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 1.6.2024 from https://www.degruyter.com/document/doi/10.1515/lpp-2023-0012/html
Scroll to top button