Papers with automated classification
From legal to technical concept: Towards an automated classification of German political Twitter postings as criminal offenses (N19-1)
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| Challenge: | 'Network Enforcement Act' provides for a regulatory framework for 'illegal content' on social network platforms like Twitter or Facebook. |
| Approach: | They propose a data annotation schema to determine whether a particular tweet could constitute a criminal offense and a binary classification schema to help with this. |
| Outcome: | The proposed schema shows that the majority of offensive posts do not constitute a criminal offense and still contribute to public discourse. |
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks (2023.acl-long)
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Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, Artem Shelmanov
| Challenge: | Existing methods for text classification tasks are inherently ambiguous and can cause errors. |
| Approach: | They propose a method that combines epistemic and aleatoric uncertainty to estimate toxicity detection errors. |
| Outcome: | The proposed method outperforms existing methods for toxicity detection and other ambiguous text classification tasks. |
Re-examining Sexism and Misogyny Classification with Annotator Attitudes (2024.findings-emnlp)
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| Challenge: | Existing datasets for content moderation fail to capture plurality of possible annotator perspectives or ensure representation of affected groups. |
| Approach: | They examine the relationship between annotator identities and attitudes and the responses they give to two GBV labelling tasks. |
| Outcome: | The results show that higher Right Wing Authoritarianism scores are associated with a higher propensity to label text as sexist . higher scores are also associated with negative attitudes towards sexism and neosexist attitudes . |