Papers by Matthew Chapman
Improving the Detection of Multilingual Online Attacks with Rich Social Media Data from Singapore (2023.acl-long)
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Janosch Haber, Bertie Vidgen, Matthew Chapman, Vibhor Agarwal, Roy Ka-Wei Lee, Yong Keong Yap, Paul Röttger
| Challenge: | Toxic content is a global problem, but most resources for detecting toxic content are in English . new datasets and models for non-English languages focus exclusively on one language or dialect . |
| Approach: | They propose to use a multilingual dataset of online attacks to identify code-mixed toxic content in Singapore . they collect reddit comments in Indonesian, Malay, Singlish, and other languages and provide fine-grained hierarchical labels for attacks . |
| Outcome: | The proposed dataset provides fine-grained hierarchical labels for online attacks in Singapore . it shows that the metadata can be used for granular error analysis . |