# Personality Differences Drive Conversational Dynamics: A High-Dimensional NLP Approach

arXiv: https://arxiv.org/abs/2410.11043 · HTML: https://mlsanity.com/paper/2410.11043/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Measuring Semantic Coherence of a Conversation](https://mlsanity.com/paper/1806.06411/) (2018)

## Built on by (most cited first, 3 of 3)

- [Persona Alchemy: Designing, Evaluating, and Implementing Psychologically-Grounded LLM Agents for Diverse Stakeholder Representation](https://mlsanity.com/paper/2505.18351/) (2025)
- [Dynamic Personality Adaptation in Large Language Models via State Machines](https://mlsanity.com/paper/2602.22157/) (2026)
- [A Similarity Measure for Comparing Conversational Dynamics](https://mlsanity.com/paper/2507.18956/) (2025)

## Abstract (arXiv)

This paper investigates how the topical flow of dyadic conversations emerges over time and how differences in interlocutors' personality traits contribute to this topical flow. Leveraging text embeddings, we map the trajectories of N = 1655 conversations between strangers into a high-dimensional space. Using nonlinear projections and clustering, we then identify when each interlocutor enters and exits various topics. Differences in conversational flow are quantified via textittopic entropy, a summary measure of the "spread" of topics covered during a conversation, and textitlinguistic alignment, a time-varying measure of the cosine similarity between interlocutors' embeddings. Our findings suggest that interlocutors with a larger difference in the personality dimension of openness influence each other to spend more time discussing a wider range of topics and that interlocutors with a larger difference in extraversion experience a larger decrease in linguistic alignment throughout their conversation. We also examine how participants' affect (emotion) changes from before to after a conversation, finding that a larger difference in extraversion predicts a larger difference in affect change and that a greater topic entropy predicts a larger affect increase. This work demonstrates how communication research can be advanced through the use of high-dimensional NLP methods and identifies personality difference as an important driver of social influence.