# Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms

Source: [Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms](<https://www.sota2.com/research/paper/6944dbe0423ea422657298ac>)

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

Data updated: 2026-01-28T05:00:08.385Z

Authors: Kieran Henderson, Kian Omoomi, Vasudha Varadarajan, Allison Lahnala, Charles Welch

Published: 2025-12-17T23:32:48.000Z

## Abstract

Recent work has explored the use of personal information in the form of persona sentences or self-disclosures to improve modeling of individual characteristics and prediction of annotator labels for subjective tasks. The volume of personal information has historically been restricted and thus little exploration has gone into understanding what kind of information is most informative for predicting annotator labels. In this work, we categorize self-disclosures and use them to build annotator models for predicting judgments of social norms. We perform several ablations and analyses to examine the impact of the type of information on our ability to predict annotation patterns. Contrary to previous work, only a small number of comments related to the original post are needed. Lastly, a more diverse sample of annotator self-disclosures did not lead to the best performance. Sampling from a larger pool of comments without filtering still yields the best performance, suggesting that there is still much to uncover in terms of what information about an annotator is most useful for verdict prediction.

## 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: 68.1.

- [Judgment Prediction on AITA verdict](<https://www.sota2.com/research/sota/judgment-prediction-on-aita-verdict.md>): Judgment Prediction; AITA verdict; Accuracy: 85.9.

- [Verdict Prediction on AITA 1.0 (author split)](<https://www.sota2.com/research/sota/verdict-prediction-on-aita-1-0-author-split.md>): Verdict Prediction; AITA 1.0 (author split); Accuracy: 85.6.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2512.16034>)

[PDF](<https://arxiv.org/pdf/2512.16034.pdf>)