# P1SCO: Social Dimensions from a Perspectivist Lens

arXiv: https://arxiv.org/abs/2605.25312 · HTML: https://mlsanity.com/paper/2605.25312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 25 May 2026.

## Builds on (most cited first, 11 of 11)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Dataset](https://mlsanity.com/paper/2306.06826/) (2023)
- [Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions](https://mlsanity.com/paper/2502.20897/) (2025)
- [Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement](https://mlsanity.com/paper/2301.10684/) (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)
- [Drivers of social influence in the Twitter migration to Mastodon](https://mlsanity.com/paper/2305.19056/) (2023)

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

- [Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings](https://mlsanity.com/paper/2606.07123/) (2026)

## Abstract (arXiv)

We introduce P1SCO, a dataset of social media comments collected from three distinct platforms, annotated according to ten social dimensions to capture the diversity of social interactions and perceptions. The dataset is carefully disaggregated to allow analysis at the level of individual comments, annotators, and platforms. In addition to the social dimension labels, we include rich metadata on the annotators, including demographics, Big Five personality profiles, and political affiliation. This combination of comment-level annotations and annotator-level features enables nuanced analyses of how social perception varies across platforms, individual differences, and demographic factors. By preserving the diversity of annotator perspectives, our dataset supports studies of inter- and intra-annotator agreement, the influence of personality and political orientation on social interpretation, and the cross-platform dynamics of social discourse.