# Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings

arXiv: https://arxiv.org/abs/2606.07123 · HTML: https://mlsanity.com/paper/2606.07123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation](https://mlsanity.com/paper/2205.00501/) (2022)
- [When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Dataset](https://mlsanity.com/paper/2306.06826/) (2023)
- [The Ecological Fallacy in Annotation: Modelling Human Label Variation goes beyond Sociodemographics](https://mlsanity.com/paper/2306.11559/) (2023)
- [Ten Social Dimensions of Conversations and Relationships](https://mlsanity.com/paper/2001.09954/) (2020)
- [The language of opinion change on social media under the lens of communicative action](https://mlsanity.com/paper/2210.17234/) (2022)
- [From Reddit to Wall Street: The role of committed minorities in financial collective action](https://mlsanity.com/paper/2107.07361/) (2021)
- [How Epidemic Psychology Works on Twitter: Evolution of responses to the COVID-19 pandemic in the U.S](https://mlsanity.com/paper/2007.13169/) (2020)
- [P1SCO: Social Dimensions from a Perspectivist Lens](https://mlsanity.com/paper/2605.25312/) (2026)

## Abstract (arXiv)

Social meaning in language is inherently perspectival, varying across annotator backgrounds, demographics, and ideological positions. However, most NLP systems collapse this variation into a single ground-truth label, ignoring the diversity of interpretations. In this work, we model social dimensions along a perspectivist spectrum, capturing how interpretations vary across demographic groups on a dataset consisting of 28k human annotations. We benchmark multiple modeling paradigms, including zero-shot, few-shot, and fine-tuned approaches, and propose fusion embeddings that integrate textual and demographic representations. Our fusion models yield consistent and statistically significant improvements over text-only baselines across all fusion strategies (+5.9-6.5% relative macro PR-AUC), with shuffle ablations confirming that demographic profiles carry genuine predictive signal rather than spurious correlations.