{"type":"ScholarlyArticle","title":"A Survey of Multimodal Sarcasm Detection","authors":[{"name":"Shafkat Farabi","slug":"shafkat-farabi","given":"Shafkat","family":"Farabi","affiliation":"George Mason University , USA,"},{"name":"Tharindu Ranasinghe","slug":"tharindu-ranasinghe","given":"Tharindu","family":"Ranasinghe","affiliation":"Lancaster University , UK"},{"name":"Diptesh Kanojia","slug":"diptesh-kanojia","given":"Diptesh","family":"Kanojia","affiliation":"University of Surrey , UK,"},{"name":"Yu Kong","slug":"yu-kong","given":"Yu","family":"Kong","affiliation":"Michigan State University , USA"},{"name":"Marcos Zampieri","slug":"marcos-zampieri","given":"Marcos","family":"Zampieri","affiliation":"George Mason University , USA,"}],"abstract":"Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.","doi":"10.24963/ijcai.2024/887","url":"https://ijcai.org/proceedings/2024/887","pdf":"https://ijcai.org/proceedings/2024/0887.pdf","markdown":"https://ijcai.org/proceedings/2024/887.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/887","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":887,"pages":{"first":8020,"last":8028},"keywords":["Machine Learning: General","Machine Learning: ML: Multi-modal learning","Natural Language Processing: NLP: Sentiment analysis, stylistic analysis, and argument mining","Natural Language Processing: NLP: Applications","Natural Language Processing: NLP: Text classification","Natural Language Processing: NLP: Speech","Natural Language Processing: General"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal et al., 2023] Sajal Aggarwal, Ananya Pandey, and Dinesh Kumar Vishwakarma. 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