@inproceedings{albrecht-etal-2026-oncoco,
title = "{O}n{C}o{C}o 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Conversations",
author = "Albrecht, Jens and
Lehmann, Robert and
Poltermann, Aleksandra and
Rudolph, Eric and
Steigerwald, Philipp and
Stieler, Mara",
editor = "Stranisci, Marco Antonio and
Falk, Neele and
Labat, Sofie and
Lo, Soda Marem and
Velutharambath, Aswathy and
Weber, Sabine and
Damiano, Rossana and
Frenda, Simona and
Hoste, Veronique and
Kleinberg, Bennett and
Klinger, Roman and
Patti, Viviana and
Plaza-del-Arco, Flor Miriam and
Sap, Maarten and
Yimam, Seid Muhie",
booktitle = "Proceedings of the 1st Workshop on Social Context ({S}o{C}on) and the 2nd Workshop on Integrating {NLP} and Psychology to Study Social Interactions ({NLPSI}) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.socon-1.9/",
doi = "10.63317/3fv7ej9tcxf9",
pages = "85--94",
abstract = "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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="albrecht-etal-2026-oncoco">
<titleInfo>
<title>OnCoCo 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Conversations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jens</namePart>
<namePart type="family">Albrecht</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Robert</namePart>
<namePart type="family">Lehmann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aleksandra</namePart>
<namePart type="family">Poltermann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eric</namePart>
<namePart type="family">Rudolph</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Philipp</namePart>
<namePart type="family">Steigerwald</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mara</namePart>
<namePart type="family">Stieler</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 1st Workshop on Social Context (SoCon) and the 2nd Workshop on Integrating NLP and Psychology to Study Social Interactions (NLPSI) @ LREC 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Marco</namePart>
<namePart type="given">Antonio</namePart>
<namePart type="family">Stranisci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Neele</namePart>
<namePart type="family">Falk</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sofie</namePart>
<namePart type="family">Labat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Soda</namePart>
<namePart type="given">Marem</namePart>
<namePart type="family">Lo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aswathy</namePart>
<namePart type="family">Velutharambath</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sabine</namePart>
<namePart type="family">Weber</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rossana</namePart>
<namePart type="family">Damiano</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simona</namePart>
<namePart type="family">Frenda</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bennett</namePart>
<namePart type="family">Kleinberg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Roman</namePart>
<namePart type="family">Klinger</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviana</namePart>
<namePart type="family">Patti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Flor</namePart>
<namePart type="given">Miriam</namePart>
<namePart type="family">Plaza-del-Arco</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maarten</namePart>
<namePart type="family">Sap</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Seid</namePart>
<namePart type="given">Muhie</namePart>
<namePart type="family">Yimam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>European Language Resources Association (ELRA)</publisher>
<place>
<placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>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.</abstract>
<identifier type="citekey">albrecht-etal-2026-oncoco</identifier>
<identifier type="doi">10.63317/3fv7ej9tcxf9</identifier>
<location>
<url>https://aclanthology.org/2026.socon-1.9/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>85</start>
<end>94</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T OnCoCo 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Conversations
%A Albrecht, Jens
%A Lehmann, Robert
%A Poltermann, Aleksandra
%A Rudolph, Eric
%A Steigerwald, Philipp
%A Stieler, Mara
%Y Stranisci, Marco Antonio
%Y Falk, Neele
%Y Labat, Sofie
%Y Lo, Soda Marem
%Y Velutharambath, Aswathy
%Y Weber, Sabine
%Y Damiano, Rossana
%Y Frenda, Simona
%Y Hoste, Veronique
%Y Kleinberg, Bennett
%Y Klinger, Roman
%Y Patti, Viviana
%Y Plaza-del-Arco, Flor Miriam
%Y Sap, Maarten
%Y Yimam, Seid Muhie
%S Proceedings of the 1st Workshop on Social Context (SoCon) and the 2nd Workshop on Integrating NLP and Psychology to Study Social Interactions (NLPSI) @ LREC 2026
%D 2026
%8 May
%I European Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F albrecht-etal-2026-oncoco
%X 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.
%R 10.63317/3fv7ej9tcxf9
%U https://aclanthology.org/2026.socon-1.9/
%U https://doi.org/10.63317/3fv7ej9tcxf9
%P 85-94
Markdown (Informal)
[OnCoCo 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Conversations](https://aclanthology.org/2026.socon-1.9/) (Albrecht et al., SoCon-NLPSI 2026)
ACL