Magdy, Walid

Webber, Bonnie

Oprea, Silviu Vlad

2023-04-25T13:16:53Z

2023-04-25T13:16:53Z

2023-04-25

The presence of sarcasm in online communication has motivated an increasing number of computational investigations of sarcasm across the scientific community. In this thesis, we build upon these investigations. Pointing out their limitations, we bring four contributions that span two research directions: sarcasm detection and sarcasm understanding. Sarcasm detection is the task of building computational models optimised for recognising sarcasm in a given text. These models are often built in a supervised learning paradigm, relying on datasets of texts labelled for sarcasm. We bring two contributions in this direction. First, we question the effectiveness of previous methods used to label texts for sarcasm. We argue that the labels they produce might not coincide with the sarcastic intention of the authors of the texts that they are labelling. In response, we suggest a new method, and we use it to build iSarcasm, a novel dataset of sarcastic and non-sarcastic tweets. We show that previous models achieve considerably lower performance on iSarcasm than on previous datasets, while human annotators achieve a considerably higher performance, compared to models, pointing out the need for more effective models. Therefore, as a second contribution, we organise a competition that invites the community to create such models. Sarcasm understanding is the task of explicating the phenomena that are subsumed under the umbrella of sarcasm through computational investigation. We bring two contributions in this direction. First, we conduct an alaysis into the socio-demographic ecology of sarcastic exchanges between human interlocutors. We find that the effectiveness of such exchanges is influenced by the socio-demographic similarity between the interlocutors, with factors such as English language nativeness, age, and gender, being particualry influential. We suggest that future social analysis tools should account for these factors. Second, we challenge the motivation of a recent endeavour of the community; mainly, that of augmenting dialogue systems with the ability to generate sarcastic responses. Through a series of social experiments, we provide guidelines for dialogue systems concerning the appropriateness of generating sarcastic responses, and the formulation of such responses. Through our work, we aim to encourage the community to consider computational investigations of sarcasm interdisciplinarily, at the intersection of natural language processing and computational social science.

https://hdl.handle.net/1842/40531

http://dx.doi.org/10.7488/era/3297

en

The University of Edinburgh

Abu Farha, I., Oprea, S. V., Wilson, S., and Magdy, W. (2022a). iSarcasmEval, in tended sarcasm detection in english and arabic. In Proceedings of The 16th Inter national Workshop on Semantic Evaluation, Seattle, Washington. Association for Computational Linguistics

Abu Farha, I., Wilson, S., Oprea, S., and Magdy, W. (2022b). Sarcasm detection is way too easy! an empirical comparison of human and machine sarcasm detection. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5284–5295, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics

Oprea, S. V. and Magdy, W. (2019). Exploring author context for detecting intended vs perceived sarcasm. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2854–2859, Florence, Italy. Association for Computational Linguistics.

Oprea, S. V. and Magdy, W. (2020). The effect of sociocultural variables on sarcasm communication online. Proc. ACM Hum.-Comput. Interact., 4(CSCW1)

Oprea, S. V. and Magdy, W. (2020). iSarcasm: A dataset of intended sarcasm. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1279–1289, Online. Association for Computational Linguistics.

Oprea, S. V., Wilson, S., and Magdy, W. (2021). Chandler: An explainable sarcastic response generator. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 339–349, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.

Oprea, S. V., Wilson, S., and Magdy, W. (2022). Should a chatbot be sarcastic? under standing user preferences towards sarcasm generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland. Association for Computational Linguistics.

Twitter users

sarcasm in tweets

datasets

socio-demographic ecology

chatbots

Computational sarcasm detection and understanding in online communication

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy