Journal article
Deciphering implicit hate: evaluating automated detection algorithms for multimodal hate
- Abstract:
-
Accurate detection and classification of online hate is a difficult task. Implicit hate is particularly challenging as such content tends to have unusual syntax, polysemic words, and fewer markers of prejudice (e.g., slurs). This problem is heightened with multimodal content, such as memes (combinations of text and images), as they are often harder to decipher than unimodal content (e.g., text alone). This paper evaluates the role of semantic and multimodal context for detecting implicit and ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 640.9KB, Terms of use)
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- Publisher copy:
- 10.18653/v1/2021.findings-acl.166
Authors
Bibliographic Details
- Publisher:
- Association for Computational Linguistics
- Series:
- Findings of the Association for Computational Linguistics
- Series number:
- ACL-IJCNLP 2021
- Journal:
- Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 More from this journal
- Pages:
- 1896-1907
- Publication date:
- 2021-01-01
- Event title:
- Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- Event location:
- Findings
- Event website:
- https://aclanthology.org/volumes/2021.findings-acl/
- Event start date:
- 2021-08-01
- Event end date:
- 2021-08-01
- DOI:
- ISBN:
- 9781954085541
Item Description
Terms of use
- Copyright holder:
- Association for Computational Linguistics
- Copyright date:
- 2021
- Rights statement:
- ©2021 Association for Computational Linguistics. This material is licensed on a Creative Commons Attribution 4.0 International License.
- Notes:
- Please note the paper contains examples of hateful content.
- Licence:
- CC Attribution (CC BY)
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