# Towards Explaining Subjective Ground of Individuals on Social Media

arXiv: https://arxiv.org/abs/2211.09953 · HTML: https://mlsanity.com/paper/2211.09953/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [Thinking Fast and Slow: Optimization Decomposition Across Timescales](https://mlsanity.com/paper/1704.07785/) (2017)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications](https://mlsanity.com/paper/1905.00563/) (2019)
- [Interpreting Neural Networks With Nearest Neighbors](https://mlsanity.com/paper/1809.02847/) (2018)
- [An Interpretable Knowledge Transfer Model for Knowledge Base Completion](https://mlsanity.com/paper/1704.05908/) (2017)
- [Interpreting Neural Networks to Improve Politeness Comprehension](https://mlsanity.com/paper/1610.02683/) (2016)
- [Interpretable Question Answering on Knowledge Bases and Text](https://mlsanity.com/paper/1906.10924/) (2019)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual's theory of mind and behavior from text is far from being resolved. This research proposes a neural model -- Subjective Ground Attention -- that learns subjective grounds of individuals and accounts for their judgments on situations of others posted on social media. Using simple attention modules as well as taking one's previous activities into consideration, we empirically show that our model provides human-readable explanations of an individual's subjective preference in judging social situations. We further qualitatively evaluate the explanations generated by the model and claim that our model learns an individual's subjective orientation towards abstract moral concepts