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

arXiv: https://arxiv.org/abs/2210.14531 · HTML: https://mlsanity.com/paper/2210.14531/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 60 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 29 of 29)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets](https://mlsanity.com/paper/1908.07898/) (2019)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes](https://mlsanity.com/paper/2008.09094/) (2020)
- [Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement](https://mlsanity.com/paper/2109.13563/) (2021)
- [UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis](https://mlsanity.com/paper/2110.00135/) (2021)
- [Personalized Language Model for Query Auto-Completion](https://mlsanity.com/paper/1804.09661/) (2018)
- [Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks](https://mlsanity.com/paper/2104.01569/) (2021)
- [Overcoming Language Variation in Sentiment Analysis with Social Attention](https://mlsanity.com/paper/1511.06052/) (2015)
- [PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision](https://mlsanity.com/paper/2010.01494/) (2020)
- [Graph-based Modeling of Online Communities for Fake News Detection](https://mlsanity.com/paper/2008.06274/) (2020)
- [Compositional Demographic Word Embeddings](https://mlsanity.com/paper/2010.02986/) (2020)
- [Exploring the Value of Personalized Word Embeddings](https://mlsanity.com/paper/2011.06057/) (2020)
- [Misinformation Has High Perplexity](https://mlsanity.com/paper/2006.04666/) (2020)
- [FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias](https://mlsanity.com/paper/2205.06181/) (2022)
- [Analysis of Moral Judgement on Reddit](https://mlsanity.com/paper/2101.07664/) (2021)
- [Representing Social Media Users for Sarcasm Detection](https://mlsanity.com/paper/1808.08470/) (2018)
- [Exploring Author Context for Detecting Intended vs Perceived Sarcasm](https://mlsanity.com/paper/1910.11932/) (2019)
- [Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit](https://mlsanity.com/paper/2201.11155/) (2022)
- [Understanding Interpersonal Conflict Types and their Impact on Perception Classification](https://mlsanity.com/paper/2208.08758/) (2022)
- [Perceived and Intended Sarcasm Detection with Graph Attention Networks](https://mlsanity.com/paper/2110.04001/) (2021)

## Built on by (most cited first, 22 of 22)

- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers](https://mlsanity.com/paper/2311.09180/) (2023)
- [When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks](https://mlsanity.com/paper/2305.06626/) (2023)
- [LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts](https://mlsanity.com/paper/2501.00274/) (2024)
- [You Are What You Annotate: Towards Better Models through Annotator Representations](https://mlsanity.com/paper/2305.14663/) (2023)
- [The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels](https://mlsanity.com/paper/2405.05860/) (2024)
- [Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks](https://mlsanity.com/paper/2311.09743/) (2023)
- [Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment](https://mlsanity.com/paper/2305.14492/) (2023)
- [Which Demographics do LLMs Default to During Annotation?](https://mlsanity.com/paper/2410.08820/) (2024)
- [Corpus Considerations for Annotator Modeling and Scaling](https://mlsanity.com/paper/2404.02340/) (2024)
- [Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding](https://mlsanity.com/paper/2306.09520/) (2023)
- [Your spouse needs professional help: Determining the Contextual Appropriateness of Messages through Modeling Social Relationships](https://mlsanity.com/paper/2307.02763/) (2023)
- [Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It's Best to Relate Perspectives!](https://mlsanity.com/paper/2311.03153/) (2023)
- [Estimating Causal Effects of Text Interventions Leveraging LLMs](https://mlsanity.com/paper/2410.21474/) (2024)
- [VideoNorms: Benchmarking Cultural Awareness of Video Language Models](https://mlsanity.com/paper/2510.08543/) (2025)
- [The Impact of Annotator Personas on LLM Behavior Across the Perspectivism Spectrum](https://mlsanity.com/paper/2508.17164/) (2025)
- [Personality-aware Human-centric Multimodal Reasoning: A New Task, Dataset and Baselines](https://mlsanity.com/paper/2304.02313/) (2023)
- [SOLAR: Towards Characterizing Subjectivity of Individuals through Modeling Value Conflicts and Trade-offs](https://mlsanity.com/paper/2504.12633/) (2025)
- [Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms](https://mlsanity.com/paper/2512.16034/) (2025)
- [Not All Subjectivity Is the Same! Defining Desiderata for the Evaluation of Subjectivity in NLP](https://mlsanity.com/paper/2603.28351/) (2026)
- [Beyond Black-Box Labels: Interpretable Criteria for Diagnosing Subjective NLP Tasks](https://mlsanity.com/paper/2604.17022/) (2026)
- [Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales](https://mlsanity.com/paper/2604.21667/) (2026)

## Abstract (arXiv)

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.