Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment
ICMI ’24, November 04–08, 2024, San Jose, Costa Rica
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM|INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION
2024
|
| Online Access: | https://hdl.handle.net/1721.1/157794 |
| _version_ | 1824458191396667392 |
|---|---|
| author | Spitale, Micol Catania, Fabio Panzeri, Francesca |
| author2 | McGovern Institute for Brain Research at MIT |
| author_facet | McGovern Institute for Brain Research at MIT Spitale, Micol Catania, Fabio Panzeri, Francesca |
| author_sort | Spitale, Micol |
| collection | MIT |
| description | ICMI ’24, November 04–08, 2024, San Jose, Costa Rica |
| first_indexed | 2025-02-19T04:21:58Z |
| format | Article |
| id | mit-1721.1/157794 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:21:58Z |
| publishDate | 2024 |
| publisher | ACM|INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION |
| record_format | dspace |
| spelling | mit-1721.1/1577942025-01-07T04:42:04Z Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment Spitale, Micol Catania, Fabio Panzeri, Francesca McGovern Institute for Brain Research at MIT ICMI ’24, November 04–08, 2024, San Jose, Costa Rica rony detection is a complex task that often stumps both humans, who frequently misinterpret ironic statements, and artificial intelligence (AI) systems. While the majority of AI research on irony detection has concentrated on linguistic cues, the role of non-verbal cues like facial expressions and auditory signals has been largely overlooked. This paper investigates the effectiveness of machine learning models in recognizing irony using solely non-verbal cues. To this end, we conducted the following experiments and analysis: (i) we trained and evaluated some machine-learning models to detect irony; (ii) we compared the results with human interpretations; and (iii) we analysed and identified multi-modal non-verbal irony markers. Our research demonstrates that machine learning models trained on nonverbal data have shown significant promise in detecting irony, outperforming human judgments in this task. Specifically, we found that certain facial action units and acoustic characteristics of speech are key indicators of irony expression. These non-verbal cues, often overlooked in traditional irony detection methods, were effectively identified by machine learning models, leading to improved accuracy in detecting irony. 2024-12-06T21:50:58Z 2024-12-06T21:50:58Z 2024-11-04 2024-12-01T08:48:57Z Article http://purl.org/eprint/type/ConferencePaper 979-8-4007-0462-8 https://hdl.handle.net/1721.1/157794 Spitale, Micol, Catania, Fabio and Panzeri, Francesca. 2024. "Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment." PUBLISHER_POLICY en https://doi.org/10.1145/3678957.3685723 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The author(s) application/pdf ACM|INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION Association for Computing Machinery |
| spellingShingle | Spitale, Micol Catania, Fabio Panzeri, Francesca Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment |
| title | Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment |
| title_full | Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment |
| title_fullStr | Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment |
| title_full_unstemmed | Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment |
| title_short | Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment |
| title_sort | understanding non verbal irony markers machine learning insights versus human judgment |
| url | https://hdl.handle.net/1721.1/157794 |
| work_keys_str_mv | AT spitalemicol understandingnonverbalironymarkersmachinelearninginsightsversushumanjudgment AT cataniafabio understandingnonverbalironymarkersmachinelearninginsightsversushumanjudgment AT panzerifrancesca understandingnonverbalironymarkersmachinelearninginsightsversushumanjudgment |