Canada

We summarize the findings from a cross-cultural study of offensiveness annotations by 4295 participants from 21 different countries across 8 geo-cultural regions. Our results show that (1) perceptions of offensiveness vary significantly across geo-cultural regions, despite controlling for gender, age, and socio-economic status, and (2) these differences are significantly mediated by annotators&#39; individual moral concerns that vary across cultures. These findings have implications in social scientific studies of offensive language detection as a moral judgement, data curation efforts that seek to represent a variety of perspectives of the task, and efforts for removing harmful language from language model training data and outputs.

ACL 2023

Disentangling Disagreements on Offensiveness: A Cross-Cultural Study

morality

culture

subjectivity

disagreement

annotation

We summarize the findings from a cross-cultural study of offensiveness annotations by 4295 participants from 21 different countries across 8 geo-cultural regions. Our results show that (1) perceptions of offensiveness vary significantly across geo-cultural regions, despite controlling for gender, age, and socio-economic status, and (2) these differences are significantly mediated by annotators' individual moral concerns that vary across cultures. These findings have implications in social scientific studies of offensive language detection as a moral judgement, data curation efforts that seek to represent a variety of perspectives of the task, and efforts for removing harmful language from language model training data and outputs.

workshop paper

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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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Factoring Hate Speech: A New Annotation Framework to Study Hate Speech in Social Media

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Scientific Fact-Checking: A Survey of Resources and Approaches

The ability to conduct retrospective analyses of attacks on human rights defenders over time and by location is important for humanitarian organizations to better understand historical or ongoing human rights violations and thus better manage the global impact of such events. We hypothesize that NLP can support such efforts by quickly processing large collections of news articles to detect and summarize the characteristics of attacks on human rights defenders. To that end, we propose a new dataset for detecting Attacks on Human Rights Defenders (HRDsAttack) consisting of crowdsourced annotations on 500 online news articles. The annotations include fine-grained information about the type and location of the attacks, as well as information about the victim(s). We demonstrate the usefulness of the dataset by using it to train and evaluate baseline models on several sub-tasks to predict the annotated characteristics.

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Disentangling Disagreements on Offensiveness: A Cross-Cultural Study

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Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data

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