Abstract
This paper contributes a novel psychological dataset consisting of counselors' behaviors during Motivational Interviewing encounters. Annotations were conducted using the Motivational Interviewing Integrity Treatment (MITI). We describe relevant aspects associated with the construction of a dataset that relies on behavioral coding such as data acquisition, transcription, expert data annotations, and reliability assessments. The dataset contains a total of 22,719 counselor utterances extracted from 277 motivational interviewing sessions that are annotated with 10 counselor behavioral codes. The reliability analysis showed that annotators achieved excellent agreement at session level, with Intraclass Correlation Coefficient (ICC) scores in the range of 0.75 to 1, and fair to good agreement at utterance level, with Cohen's Kappa scores ranging from 0.31 to 0.64.
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CITATION STYLE
Pérez-Rosas, V., Mihalcea, R., Resnicow, K., Singh, S., & An, L. (2016). Building a motivational interviewing dataset. In Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, CLPsych 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 (pp. 42–51). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0305
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