# Do Differences in Values Influence Disagreements in Online Discussions?

arXiv: https://arxiv.org/abs/2310.15757 · HTML: https://mlsanity.com/paper/2310.15757/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 86 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes](https://mlsanity.com/paper/2008.09094/) (2020)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](https://mlsanity.com/paper/1906.03538/) (2019)
- [We Need to Talk About Random Splits](https://mlsanity.com/paper/2005.00636/) (2020)
- [ValueNet: A New Dataset for Human Value Driven Dialogue System](https://mlsanity.com/paper/2112.06346/) (2021)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments](https://mlsanity.com/paper/2203.14563/) (2022)
- [I Beg to Differ: A study of constructive disagreement in online conversations](https://mlsanity.com/paper/2101.10917/) (2021)
- [Conversational Markers of Constructive Discussions](https://mlsanity.com/paper/1604.07407/) (2016)
- [Reason Against the Machine: Future Directions for Mass Online Deliberation](https://mlsanity.com/paper/2107.12711/) (2021)
- [Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses](https://mlsanity.com/paper/1911.03850/) (2019)
- [Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction](https://mlsanity.com/paper/2209.08966/) (2022)
- ["Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space](https://mlsanity.com/paper/2101.10952/) (2021)
- [Perspective-taking to Reduce Affective Polarization on Social Media](https://mlsanity.com/paper/2110.05596/) (2021)
- [Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions](https://mlsanity.com/paper/2302.03950/) (2023)
- [On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation](https://mlsanity.com/paper/2007.08107/) (2020)
- [From Hesitancy Framings to Vaccine Hesitancy Profiles: A Journey of Stance, Ontological Commitments and Moral Foundations](https://mlsanity.com/paper/2202.09456/) (2022)
- [A First Look at Human Values-Violation in App Reviews](https://mlsanity.com/paper/2012.10095/) (2020)

## Built on by (most cited first, 15 of 15)

- [Annotator-Centric Active Learning for Subjective NLP Tasks](https://mlsanity.com/paper/2404.15720/) (2024)
- [ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions](https://mlsanity.com/paper/2407.02472/) (2024)
- [Evaluating Moral Beliefs across LLMs through a Pluralistic Framework](https://mlsanity.com/paper/2411.03665/) (2024)
- [An Empirical Analysis of Diversity in Argument Summarization](https://mlsanity.com/paper/2402.01535/) (2024)
- [Dialogue Systems for Emotional Support via Value Reinforcement](https://mlsanity.com/paper/2501.17182/) (2025)
- [Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive Learning](https://mlsanity.com/paper/2401.17228/) (2024)
- [Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems](https://mlsanity.com/paper/2402.16751/) (2024)
- [A Hybrid Intelligence Method for Argument Mining](https://mlsanity.com/paper/2403.09713/) (2024)
- [The Value of Nothing: Multimodal Extraction of Human Values Expressed by TikTok Influencers](https://mlsanity.com/paper/2501.11770/) (2025)
- [Facilitating Opinion Diversity through Hybrid NLP Approaches](https://mlsanity.com/paper/2405.09439/) (2024)
- [Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case study](https://mlsanity.com/paper/2505.08143/) (2025)
- [That is Unacceptable: the Moral Foundations of Canceling](https://mlsanity.com/paper/2503.05720/) (2025)
- [Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit](https://mlsanity.com/paper/2405.10213/) (2024)
- [SOLAR: Towards Characterizing Subjectivity of Individuals through Modeling Value Conflicts and Trade-offs](https://mlsanity.com/paper/2504.12633/) (2025)
- [Taking a SEAT: Predicting Value Interpretations from Sentiment, Emotion, Argument, and Topic Annotations](https://mlsanity.com/paper/2510.01976/) (2025)

## Abstract (arXiv)

Disagreements are common in online discussions. Disagreement may foster collaboration and improve the quality of a discussion under some conditions. Although there exist methods for recognizing disagreement, a deeper understanding of factors that influence disagreement is lacking in the literature. We investigate a hypothesis that differences in personal values are indicative of disagreement in online discussions. We show how state-of-the-art models can be used for estimating values in online discussions and how the estimated values can be aggregated into value profiles. We evaluate the estimated value profiles based on human-annotated agreement labels. We find that the dissimilarity of value profiles correlates with disagreement in specific cases. We also find that including value information in agreement prediction improves performance.