Stance detection: concepts, approaches, resources, and outstanding issues
buir.contributor.author | Can, Fazlı | |
dc.citation.epage | 2676 | en_US |
dc.citation.spage | 2673 | en_US |
dc.contributor.author | Küçük, Dilek | |
dc.contributor.author | Can, Fazlı | |
dc.coverage.spatial | New York, NY, United States | en_US |
dc.date.accessioned | 2022-02-09T09:31:08Z | |
dc.date.available | 2022-02-09T09:31:08Z | |
dc.date.issued | 2021-07-11 | |
dc.department | Department of Computer Engineering | en_US |
dc.description | Conference Name: SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval | en_US |
dc.description | Date of Conference: 11-15 July 2021 | en_US |
dc.description.abstract | Stance detection (also known as stance classification and stance prediction) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to de termine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly speci fied in the text, or implied only. The output of the stance detection procedure is usually from this set: {Favor, Against, None}. In this tutorial, we will define the core concepts and research problems re lated to stance detection, present historical and contemporary ap proaches to stance detection, provide pointers to related resources (datasets and tools), and we will cover outstanding issues and ap plication areas of stance detection. As solutions to stance detection can contribute to significant tasks including trend analysis, opin ion surveys, user reviews, personalization, and predictions for ref erendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and partic ularly on social media. Finally, we believe that image and video content will commonly be the subject of stance detection research soon. | en_US |
dc.identifier.doi | 10.1145/3404835.3462815 | en_US |
dc.identifier.isbn | 978-1-4503-8037-9 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/77159 | en_US |
dc.language.iso | English | en_US |
dc.publisher | Association for Computing Machinery | en_US |
dc.relation.isversionof | https://dx.doi.org/10.1145/3404835.3462815 | en_US |
dc.source.title | SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval | en_US |
dc.subject | Stance detection | en_US |
dc.subject | Social media analysis | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Data streams | en_US |
dc.title | Stance detection: concepts, approaches, resources, and outstanding issues | en_US |
dc.type | Conference Paper | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Stance_detection_concepts,_approaches,_resources,_and_outstanding_issues.pdf
- Size:
- 1.03 MB
- Format:
- Adobe Portable Document Format
- Description:
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.69 KB
- Format:
- Item-specific license agreed upon to submission
- Description: