# Unifying Data Perspectivism and Personalization: An Application to Social Norms

Source: [Unifying Data Perspectivism and Personalization: An Application to Social Norms](<https://www.sota2.com/research/paper/68fa70f71e209131cea4e715>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:54:43.381Z

Authors: Joan Plepi, B\\'ela Neuendorf, Lucie Flek, Charles Welch

Published: 2022-10-26T07:43:26.000Z

## Abstract

Instead of using a single ground truth for language processing tasks, several recent studies have examined how to represent and predict the labels of the set of annotators. However, often little or no information about annotators is known, or the set of annotators is small. In this work, we examine a corpus of social media posts about conflict from a set of 13k annotators and 210k judgements of social norms. We provide a novel experimental setup that applies personalization methods to the modeling of annotators and compare their effectiveness for predicting the perception of social norms. We further provide an analysis of performance across subsets of social situations that vary by the closeness of the relationship between parties in conflict, and assess where personalization helps the most.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Annotator Modeling on AITA situation](<https://www.sota2.com/research/sota/annotator-modeling-on-aita-situation.md>): Annotator Modeling; AITA situation; Accuracy: 67.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.14531>)

[PDF](<https://arxiv.org/pdf/2210.14531.pdf>)