mlsanityData to 7 Oct 2026

Ten Social Dimensions of Conversations and Relationships

arXiv 2001.09954 ↗First posted 27 Jan 2020cs.CYcited by 17

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Abstract (arXiv)

Decades of social science research identified ten fundamental dimensions that provide the conceptual building blocks to describe the nature of human relationships. Yet, it is not clear to what extent these concepts are expressed in everyday language and what role they have in shaping observable dynamics of social interactions. After annotating conversational text through crowdsourcing, we trained NLP tools to detect the presence of these types of interaction from conversations, and applied them to 160M messages written by geo-referenced Reddit users, 290k emails from the Enron corpus and 300k lines of dialogue from movie scripts. We show that social dimensions can be predicted purely from conversations with an AUC up to 0.98, and that the combination of the predicted dimensions suggests both the types of relationships people entertain (conflict vs. support) and the types of real-world communities (wealthy vs. deprived) they shape.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2020.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 3; 2025: 5; 2026: 4.

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  1. 2023Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmarkcited by 62
  2. 2023The Persuasive Power of Large Language Modelscited by 46
  3. 2023The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Taskscited by 35
  4. 2021Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversationscited by 20
  5. 2024Whose wife is it anyway? Assessing bias against same-gender relationships in machine translationcited by 6
  6. 2025Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Redditcited by 4
  7. 2026"I understand your perspective": LLM Persuasion through the Lens of Communicative Action Theorycited by 4
  8. 2022The Need for Interpretable Features: Motivation and Taxonomycited by 3
  9. 2025How Social is It? A Benchmark for LLMs' Capabilities in Multi-user Multi-turn Social Agent Taskscited by 3
  10. 2024ExpressivityBench: Can LLMs Communicate Implicitly?cited by 2
  11. 2025You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with a Multi-Agent Conversationscited by 2
  12. 2025Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluationcited by 1
5 more
  1. 2026Frictionless Love: Associations Between AI Companion Roles and Behavioral Addictioncited by 1
  2. 2026P1SCO: Social Dimensions from a Perspectivist Lenscited by 1
  3. 2024Semantic-Guided RL for Interpretable Feature Engineeringcited by 0
  4. 2025Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communitiescited by 0
  5. 2026Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddingscited by 0