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When Sarcasm Hurts: Irony-Aware Models for Abusive Language Detection

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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14163))

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Abstract

Linguistic literature on irony discusses sarcasm as a form of irony characterized by its biting nature and the intention to mock a victim. This particular trait makes sarcasm apt to convey hate speech and not only humour. Previous works on abusive language stressed the need to address ironic language to lead the system to recognize correctly hate speech, especially in spontaneous texts, like tweets [13]. In this context, our main hypothesis is that information about the presence of sarcasm could help to improve the detection of hateful messages, especially when they are camouflaged as sarcastic. To corroborate this hypothesis: i) we perform analysis on HaSpeeDe20_ext, an Italian corpus of tweets about the integration of cultural minorities in Italy, ii) we carry out computational experiments injecting the knowledge of sarcasm in a system of hate speech detection, and iii) we adopt strategies of validation in terms of performance and significance of the obtained results. Results confirm our hypothesis and overcome the state of the art.

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Notes

  1. 1.

    One of the most complete definitions is provided by [25]: a content is considered hateful on the basis of its action and its target. The action is the illocutionary act of the utterance aimed to spread or justify hate, incite violence, or threat people’s freedom, dignity, and safety. The target must be a protected group or an individual belonging to such a group, attacked for his/her individual characteristics.

  2. 2.

    Sir, everybody has the right to a dignified life, but if you put a migrant in my way, I will be Salvini. (Matthew 15, 83).

  3. 3.

    A plate of pasta and let’s apologize for not being Muslims too. Maybe then they become our friends and won’t kill us anymore.

  4. 4.

    http://www.di.unito.it/~tutreeb/haspeede-evalita20/index.html.

  5. 5.

    http://www.di.unito.it/~tutreeb/haspeede-evalita23/index.html.

  6. 6.

    https://www.evalita.it/campaigns/evalita-2023/.

  7. 7.

    These three language models are trained on different genres of texts in Italian and available on the Hugging Face platform: https://huggingface.co/models.

  8. 8.

    The schema of annotation of ironic language is inherited by [5] who annotated the IronITA corpus of tweets for the first time, to our knowledge, as ironic and sarcastic.

  9. 9.

    In particular, they used a linear layer with a softmax on top of the CLS token, applying a novel technique of layer-wise learning rate. That is the main difference with our approach.

  10. 10.

    So if an Italian dies in the hospital in the midst of ants it is an ‘episode’ while if a Nigerian dies of a circumcision, free healthcare is required for immigrants. Stuff from civil war and riots up to the ramparts of Orion.

  11. 11.

    https://github.com/fornaciari/boostsa#readme.

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Acknowledgments

The work of S. Frenda and V. Patti was partially funded by the Multilingual Perspective-Aware NLU Project in partnership with Amazon Alexa. The work of the P. Rosso was done in the framework of the FairTransNLP-Stereotypes research project on Fairness and Transparency for equitable NLP applications in social media: Identifying stereotypes and prejudices and developing equitable systems (Grant PID2021-124361OB-C31 funded by MCIN/AEI/10.130 39/501100011033 and by ERDF, EU A way of making Europe).

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Authors and Affiliations

  1. University of Turin, Turin, Italy

    Simona Frenda & Viviana Patti

  2. aequa-tech srl, Turin, Italy

    Simona Frenda

  3. Universitat Politècnica de València, Valencia, Spain

    Paolo Rosso

Authors
  1. Simona Frenda
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  2. Viviana Patti
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  3. Paolo Rosso
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Corresponding author

Correspondence to Simona Frenda .

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Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Avi Arampatzis

  2. University of Amsterdam, Amsterdam, The Netherlands

    Evangelos Kanoulas

  3. CERTH-ITI, Thessaloniki, Greece

    Theodora Tsikrika

  4. CERTH-ITI, Thessaloniki, Greece

    Stefanos Vrochidis

  5. Utrecht University, Utrecht, The Netherlands

    Anastasia Giachanou

  6. Elsevier, Amsterdam, The Netherlands

    Dan Li

  7. University of Amsterdam, Amsterdam, The Netherlands

    Mohammad Aliannejadi

  8. University of Lausanne, Lausanne, Switzerland

    Michalis Vlachos

  9. University of Padua, Padova, Italy

    Guglielmo Faggioli

  10. University of Padua, Padova, Italy

    Nicola Ferro

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Frenda, S., Patti, V., Rosso, P. (2023). When Sarcasm Hurts: Irony-Aware Models for Abusive Language Detection. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2023. Lecture Notes in Computer Science, vol 14163. Springer, Cham. https://doi.org/10.1007/978-3-031-42448-9_4

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