# Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detection

arXiv: https://arxiv.org/abs/2106.15896 · HTML: https://mlsanity.com/paper/2106.15896/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 57 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [A Just and Comprehensive Strategy for Using NLP to Address Online Abuse](https://mlsanity.com/paper/1906.01738/) (2019)
- [Fine-grained Sentiment Classification using BERT](https://mlsanity.com/paper/1910.03474/) (2019)
- [Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter](https://mlsanity.com/paper/1803.03662/) (2018)

## Built on by (most cited first, 30 of 38)

- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022)
- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation](https://mlsanity.com/paper/2205.00501/) (2022)
- [SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)](https://mlsanity.com/paper/2304.14803/) (2023)
- [The Ecological Fallacy in Annotation: Modelling Human Label Variation goes beyond Sociodemographics](https://mlsanity.com/paper/2306.11559/) (2023)
- [You Are What You Annotate: Towards Better Models through Annotator Representations](https://mlsanity.com/paper/2305.14663/) (2023)
- [The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels](https://mlsanity.com/paper/2405.05860/) (2024)
- [The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation](https://mlsanity.com/paper/2405.01299/) (2024)
- [ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation](https://mlsanity.com/paper/2310.14979/) (2023)
- [Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks](https://mlsanity.com/paper/2112.07475/) (2021)
- [AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection](https://mlsanity.com/paper/2212.10405/) (2022)
- [D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation](https://mlsanity.com/paper/2404.10857/) (2024)
- [iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?](https://mlsanity.com/paper/2305.06074/) (2023)
- [Subjective textitIsms? On the Danger of Conflating Hate and Offence in Abusive Language Detection](https://mlsanity.com/paper/2403.02268/) (2024)
- [LeWiDi-2025 at NLPerspectives: Third Edition of the Learning with Disagreements Shared Task](https://mlsanity.com/paper/2510.08460/) (2025)
- [Corpus Considerations for Annotator Modeling and Scaling](https://mlsanity.com/paper/2404.02340/) (2024)
- [Re-examining Sexism and Misogyny Classification with Annotator Attitudes](https://mlsanity.com/paper/2410.03543/) (2024)
- [Training and Evaluating with Human Label Variation: An Empirical Study](https://mlsanity.com/paper/2502.01891/) (2025)
- [Your spouse needs professional help: Determining the Contextual Appropriateness of Messages through Modeling Social Relationships](https://mlsanity.com/paper/2307.02763/) (2023)
- [Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation](https://mlsanity.com/paper/2402.14101/) (2024)
- [Understanding The Effect Of Temperature On Alignment With Human Opinions](https://mlsanity.com/paper/2411.10080/) (2024)
- [Embracing Diversity: A Multi-Perspective Approach with Soft Labels](https://mlsanity.com/paper/2503.00489/) (2025)
- [The Impact of Annotator Personas on LLM Behavior Across the Perspectivism Spectrum](https://mlsanity.com/paper/2508.17164/) (2025)
- [Can Humans Identify Domains?](https://mlsanity.com/paper/2404.01785/) (2024)
- [Designing NLP Systems That Adapt to Diverse Worldviews](https://mlsanity.com/paper/2405.11197/) (2024)
- [How Many Ratings per Item are Necessary for Reliable Significance Testing?](https://mlsanity.com/paper/2412.02968/) (2024)
- [Lon-ea at SemEval-2023 Task 11: A Comparison of Activation Functions for Soft and Hard Label Prediction](https://mlsanity.com/paper/2303.02468/) (2023)
- [SafeWebUH at SemEval-2023 Task 11: Learning Annotator Disagreement in Derogatory Text: Comparison of Direct Training vs Aggregation](https://mlsanity.com/paper/2305.01050/) (2023)
- [Multi-Perspective LLM Annotations for Valid Analyses in Subjective Tasks](https://mlsanity.com/paper/2603.21404/) (2026)

## Abstract (arXiv)

Social media platforms provide users the freedom of expression and a medium to exchange information and express diverse opinions. Unfortunately, this has also resulted in the growth of abusive content with the purpose of discriminating people and targeting the most vulnerable communities such as immigrants, LGBT, Muslims, Jews and women. Because abusive language is subjective in nature, there might be highly polarizing topics or events involved in the annotation of abusive contents such as hate speech (HS). Therefore, we need novel approaches to model conflicting perspectives and opinions coming from people with different personal and demographic backgrounds. In this paper, we present an in-depth study to model polarized opinions coming from different communities under the hypothesis that similar characteristics (ethnicity, social background, culture etc.) can influence the perspectives of annotators on a certain phenomenon. We believe that by relying on this information, we can divide the annotators into groups sharing similar perspectives. We can create separate gold standards, one for each group, to train state-of-the-art deep learning models. We can employ an ensemble approach to combine the perspective-aware classifiers from different groups to an inclusive model. We also propose a novel resource, a multi-perspective English language dataset annotated according to different sub-categories relevant for characterising online abuse: hate speech, aggressiveness, offensiveness and stereotype. By training state-of-the-art deep learning models on this novel resource, we show how our approach improves the prediction performance of a state-of-the-art supervised classifier.