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

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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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In this work we propose a novel annotation scheme which factors hate speech into five separate discursive categories. To evaluate our scheme, we construct a corpus of over 2.9M Twitter posts containing hateful expressions directed at Jews, and annotate a sample dataset of 1,050 tweets. We present a statistical analysis of the annotated dataset as well as discuss annotation examples, and conclude by discussing promising directions for future work.

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

The definitions of abusive, offensive, toxic and uncivil comments used for annotating corpora for automated content moderation are highly intersected and researchers call for their dis- ambiguation. We summarize the definitions of these terms as they appear in 23 papers across different fields. We compare examples given for uncivil, offensive, and toxic comments, attempting to foster more unified scientific re- sources. Additionally, we stress that the term incivility that frequently appears in social sci- ence literature has hardly been mentioned in the literature we analyzed that focuses on computational linguistics and natural language processing.

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A Cross-Lingual Study of Homotransphobia on Twitter

The automated detection of harmful language has been of great importance for the online world, especially with the growing importance of social media and, consequently, polarisation. There are many open challenges to high quality detection of harmful text, from dataset creation to generalisable application, thus calling for more systematic studies. In this paper, we explore re-annotation as a means of examining the robustness of already existing labelled datasets, showing that, despite using alternative definitions, the inter-annotator agreement remains very inconsistent, highlighting the intrinsically subjective and variable nature of the task. In addition, we build automatic toxicity detectors using the existing datasets, with their original labels, and we evaluate them on our multi-definition and multi-source datasets. Surprisingly, while other studies show that hate speech detection models perform better on data that are derived from the same distribution as the training set, our analysis demonstrates this is not necessarily true.

Harmful Language Datasets: An Assessment of Robustness

The automatic detection of hate speech online is an active research area in NLP. Most of the studies to date are based on social media datasets that contribute to the creation of hate speech detection models trained on them. However, data creation processes contain their own biases, and models inherently learn from these dataset-specific biases. In this paper, we perform a large-scale cross-dataset comparison where we fine-tune language models on different hate speech detection datasets. This analysis shows how some datasets are more generalizable than others when used as training data. Crucially, our experiments show how combining hate speech detection datasets can contribute to the development of robust hate speech detection models. This robustness holds even when controlling by data size and compared with the best individual datasets. 


Robust Hate Speech Detection: A Cross-Dataset Empirical Comparison

Different ways of linguistically expressing the same real-world event can lead to different perceptions of what happened. Previous work has shown that different descriptions of gender-based violence (GBV) influence the reader's perception of who is to blame for the violence, possibly reinforcing stereotypes which see the victim as partly responsible, too. As a contribution to raise awareness on perspective-based writing, and to facilitate access to alternative perspectives, we introduce the novel task of automatically rewriting GBV descriptions as a means to alter the perceived level of responsibility on the perpetrator. We present a quasi-parallel dataset of sentences with low and high perceived responsibility levels for the perpetrator, and experiment with unsupervised (mBART-based), zero-shot and few-shot (GPT3-based) methods for rewriting sentences. We evaluate our models using a questionnaire study and a suite of automatic metrics.

Responsibility Perspective Transfer for Italian Femicide News

The task of fact-checking deals with assessing the veracity of factual claims based on credible evidence and background knowledge. In particular, scientific fact-checking is the variation of the task concerned with verifying claims rooted in scientific knowledge. This task has received significant attention due to the growing importance of scientific and health discussions on online platforms. Automated scientific fact-checking methods based on NLP can help combat the spread of misinformation, assist researchers in knowledge discovery, and help individuals understand new scientific breakthroughs. In this paper, we present a comprehensive survey of existing research in this emerging field and its related tasks. We provide a task description, discuss the construction process of existing datasets, and analyze proposed models and approaches. Based on our findings, we identify intriguing challenges and outline potential future directions to advance the field.

Scientific Fact-Checking: A Survey of Resources and Approaches

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Next from ACL 2023

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

Similar lecture

Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features