# A Similarity Measure for Comparing Conversational Dynamics

arXiv: https://arxiv.org/abs/2507.18956 · HTML: https://mlsanity.com/paper/2507.18956/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 63 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Large Language Models for Data Annotation and Synthesis: A Survey](https://mlsanity.com/paper/2402.13446/) (2024)
- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013)
- [Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles](https://mlsanity.com/paper/2407.00870/) (2024)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop](https://mlsanity.com/paper/1909.01362/) (2019)
- [The Role of Conversation Context for Sarcasm Detection in Online Interactions](https://mlsanity.com/paper/1707.06226/) (2017)
- [WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community](https://mlsanity.com/paper/1810.13181/) (2018)
- [Dynamic Forecasting of Conversation Derailment](https://mlsanity.com/paper/2110.05111/) (2021)
- [Quantifying the Causal Effects of Conversational Tendencies](https://mlsanity.com/paper/2009.03897/) (2020)
- [The language of opinion change on social media under the lens of communicative action](https://mlsanity.com/paper/2210.17234/) (2022)
- [A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection](https://mlsanity.com/paper/1606.05704/) (2016)
- [Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog](https://mlsanity.com/paper/1709.05305/) (2017)
- [We've had this conversation before: A Novel Approach to Measuring Dialog Similarity](https://mlsanity.com/paper/2110.05780/) (2021)
- [Combining Textual Content and Structure to Improve Dialog Similarity](https://mlsanity.com/paper/1802.07117/) (2018)
- [Personality Differences Drive Conversational Dynamics: A High-Dimensional NLP Approach](https://mlsanity.com/paper/2410.11043/) (2024)
- [Look Who's Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog](https://mlsanity.com/paper/1904.11610/) (2019)
- [TaskDiff: A Similarity Metric for Task-Oriented Conversations](https://mlsanity.com/paper/2310.15298/) (2023)
- [What you say or how you say it? Predicting Conflict Outcomes in Real and LLM-Generated Conversations](https://mlsanity.com/paper/2409.09338/) (2024)

## Built on by (most cited first, 1 of 1)

- [Conversational DNA: A Visual Language and Interactive Atlas of Human and AI Dialogue](https://mlsanity.com/paper/2508.07520/) (2025)

## Abstract (arXiv)

The quality of a conversation goes beyond the individual quality of each reply, and instead emerges from how these combine into interactional dynamics that give the conversation its distinctive overall "shape". However, there is no robust automated method for comparing conversations in terms of their overall dynamics. Such methods could enhance the analysis of conversational data and help evaluate conversational agents more holistically. In this work, we introduce a similarity measure for comparing conversations with respect to their dynamics. We design a validation procedure for testing the robustness of the metric in capturing differences in conversation dynamics and for assessing its sensitivity to the topic of the conversations. To illustrate the measure's utility, we use it to analyze conversational dynamics in a large online community, bringing new insights into the role of situational power in conversations.