# OnCoCo 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Conversations

arXiv: https://arxiv.org/abs/2512.09804 · HTML: https://mlsanity.com/paper/2512.09804/

## Key facts

- Cited by 1 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 10 Dec 2025.

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

- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations](https://mlsanity.com/paper/2111.06647/) (2021)
- [Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes](https://mlsanity.com/paper/1907.00326/) (2019)

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

- [GEMCo: A Validated, Ethically Releasable Proxy for Inaccessible Counselling Data](https://mlsanity.com/paper/2607.23621/) (2026)

## Abstract (arXiv)

This paper presents OnCoCo 1.0, a new public dataset for fine-grained message classification in online counseling. It is based on a new, integrative system of categories, designed to improve the automated analysis of psychosocial online counseling conversations. Existing category systems, predominantly based on Motivational Interviewing (MI), are limited by their narrow focus and dependence on datasets derived mainly from face-to-face counseling. This limits the detailed examination of textual counseling conversations. In response, we developed a comprehensive new coding scheme that differentiates between 38 types of counselor and 28 types of client utterances, and created a labeled dataset consisting of about 2.800 messages from counseling conversations. We fine-tuned several models on our dataset to demonstrate its applicability. The data and models are publicly available to researchers and practitioners. Thus, our work contributes a new type of fine-grained conversational resource to the language resources community, extending existing datasets for social and mental-health dialogue analysis.