# Multi-Perspective Stance Detection

arXiv: https://arxiv.org/abs/2411.08752 · HTML: https://mlsanity.com/paper/2411.08752/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks](https://mlsanity.com/paper/2311.09743/) (2023)
- [Evaluation Metrics for Measuring Bias in Search Engine Results](https://mlsanity.com/paper/2210.10517/) (2022)

## Built on by (most cited first, 1 of 1)

- [Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems](https://mlsanity.com/paper/2506.20209/) (2025)

## Abstract (arXiv)

Subjective NLP tasks usually rely on human annotations provided by multiple annotators, whose judgments may vary due to their diverse backgrounds and life experiences. Traditional methods often aggregate multiple annotations into a single ground truth, disregarding the diversity in perspectives that arises from annotator disagreement. In this preliminary study, we examine the effect of including multiple annotations on model accuracy in classification. Our methodology investigates the performance of perspective-aware classification models in stance detection task and further inspects if annotator disagreement affects the model confidence. The results show that multi-perspective approach yields better classification performance outperforming the baseline which uses the single label. This entails that designing more inclusive perspective-aware AI models is not only an essential first step in implementing responsible and ethical AI, but it can also achieve superior results than using the traditional approaches.