Understanding Counterspeech for Online Harm Mitigation
Counterspeech offers direct rebuttals to hateful speech by challenging perpetrators of hate and showing support to targets of abuse. It provides a promising alternative to more contentious measures, such as content moderation and deplatforming, by contributing a greater amount of positive online sp...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Linköping University Electronic Press
2024-12-01
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| Series: | Northern European Journal of Language Technology |
| Online Access: | https://nejlt.ep.liu.se/article/view/5203 |
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| _version_ | 1832591274076536832 |
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| author | Yi-Ling Chung Gavin Abercrombie Florence Enock Jonathan Bright Verena Rieser |
| author_facet | Yi-Ling Chung Gavin Abercrombie Florence Enock Jonathan Bright Verena Rieser |
| author_sort | Yi-Ling Chung |
| collection | DOAJ |
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Counterspeech offers direct rebuttals to hateful speech by challenging perpetrators of hate and showing support to targets of abuse. It provides a promising alternative to more contentious measures, such as content moderation and deplatforming, by contributing a greater amount of positive online speech rather than attempting to mitigate harmful content through removal. Advances in the development of large language models mean that the process of producing counterspeech could be made more efficient by automating its generation, which would enable large-scale online campaigns. However, we currently lack a systematic understanding of several important factors relating to the efficacy of counterspeech for hate mitigation, such as which types of counterspeech are most effective, what are the optimal conditions for implementation, and which specific effects of hate it can best ameliorate. This paper aims to fill this gap by systematically reviewing counterspeech research in the social sciences and comparing methodologies and findings with natural language processing (NLP) and computer science efforts in automatic counterspeech generation. By taking this multi-disciplinary view, we identify promising future directions in both fields.
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| format | Article |
| id | doaj-art-ee0462375f414bc9ab9f808f0b587484 |
| institution | Kabale University |
| issn | 2000-1533 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Linköping University Electronic Press |
| record_format | Article |
| series | Northern European Journal of Language Technology |
| spelling | doaj-art-ee0462375f414bc9ab9f808f0b5874842025-01-22T15:24:15ZengLinköping University Electronic PressNorthern European Journal of Language Technology2000-15332024-12-0110110.3384/nejlt.2000-1533.2024.5203Understanding Counterspeech for Online Harm MitigationYi-Ling Chung0Gavin AbercrombieFlorence EnockJonathan BrightVerena RieserThe Alan Turing Institute Counterspeech offers direct rebuttals to hateful speech by challenging perpetrators of hate and showing support to targets of abuse. It provides a promising alternative to more contentious measures, such as content moderation and deplatforming, by contributing a greater amount of positive online speech rather than attempting to mitigate harmful content through removal. Advances in the development of large language models mean that the process of producing counterspeech could be made more efficient by automating its generation, which would enable large-scale online campaigns. However, we currently lack a systematic understanding of several important factors relating to the efficacy of counterspeech for hate mitigation, such as which types of counterspeech are most effective, what are the optimal conditions for implementation, and which specific effects of hate it can best ameliorate. This paper aims to fill this gap by systematically reviewing counterspeech research in the social sciences and comparing methodologies and findings with natural language processing (NLP) and computer science efforts in automatic counterspeech generation. By taking this multi-disciplinary view, we identify promising future directions in both fields. https://nejlt.ep.liu.se/article/view/5203 |
| spellingShingle | Yi-Ling Chung Gavin Abercrombie Florence Enock Jonathan Bright Verena Rieser Understanding Counterspeech for Online Harm Mitigation Northern European Journal of Language Technology |
| title | Understanding Counterspeech for Online Harm Mitigation |
| title_full | Understanding Counterspeech for Online Harm Mitigation |
| title_fullStr | Understanding Counterspeech for Online Harm Mitigation |
| title_full_unstemmed | Understanding Counterspeech for Online Harm Mitigation |
| title_short | Understanding Counterspeech for Online Harm Mitigation |
| title_sort | understanding counterspeech for online harm mitigation |
| url | https://nejlt.ep.liu.se/article/view/5203 |
| work_keys_str_mv | AT yilingchung understandingcounterspeechforonlineharmmitigation AT gavinabercrombie understandingcounterspeechforonlineharmmitigation AT florenceenock understandingcounterspeechforonlineharmmitigation AT jonathanbright understandingcounterspeechforonlineharmmitigation AT verenarieser understandingcounterspeechforonlineharmmitigation |