Building a motivational interviewing dataset

Verónica Pérez-Rosas; Rada Mihalcea; Kenneth Resnicow; Satinder Singh; Lawrence An

Building a motivational interviewing dataset

Verónica Pérez-Rosas
, Rada Mihalcea
, Kenneth Resnicow
, Satinder Singh
, Lawrence An

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology
Subtitle of host publication	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
Editors	Kristy Hollingshead, Lyle Ungar
Publisher	Association for Computational Linguistics (ACL)
Pages	42-51
Number of pages	10
ISBN (Electronic)	9781941643549
State	Published - 2016
Externally published	Yes
Event	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 - San Diego, United States Duration: Jun 16 2016 → …

Publication series

Name	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

Conference

Conference	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
Country/Territory	United States
City	San Diego
Period	6/16/16 → …

Bibliographical note

Publisher Copyright:
© 2016 Association for Computational Linguistics

Find It

Find It at U of M Libraries

Cite this

Pérez-Rosas, V., Mihalcea, R., Resnicow, K., Singh, S., & An, L. (2016). Building a motivational interviewing dataset. In K. Hollingshead, & L. Ungar (Eds.), 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). (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). Association for Computational Linguistics (ACL).

Building a motivational interviewing dataset. / Pérez-Rosas, Verónica; Mihalcea, Rada; Resnicow, Kenneth et al.
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. ed. / Kristy Hollingshead; Lyle Ungar. Association for Computational Linguistics (ACL), 2016. p. 42-51 (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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Pérez-Rosas, V, Mihalcea, R, Resnicow, K, Singh, S & An, L 2016, Building a motivational interviewing dataset. in K Hollingshead & L Ungar (eds), 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. 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, Association for Computational Linguistics (ACL), pp. 42-51, 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, San Diego, United States, 6/16/16.

Pérez-Rosas V, Mihalcea R, Resnicow K, Singh S, An L. Building a motivational interviewing dataset. In Hollingshead K, Ungar L, editors, 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. Association for Computational Linguistics (ACL). 2016. p. 42-51. (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).

Pérez-Rosas, Verónica ; Mihalcea, Rada ; Resnicow, Kenneth et al. / Building a motivational interviewing dataset. 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. editor / Kristy Hollingshead ; Lyle Ungar. Association for Computational Linguistics (ACL), 2016. pp. 42-51 (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).

@inproceedings{17943703a4094b9aa86398bdf2fb4407,

title = "Building a motivational interviewing dataset",

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.",

author = "Ver{\'o}nica P{\'e}rez-Rosas and Rada Mihalcea and Kenneth Resnicow and Satinder Singh and Lawrence An",

note = "Publisher Copyright: {\textcopyright} 2016 Association for Computational Linguistics; 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 ; Conference date: 16-06-2016",

year = "2016",

language = "English (US)",

series = "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",

publisher = "Association for Computational Linguistics (ACL)",

pages = "42--51",

editor = "Kristy Hollingshead and Lyle Ungar",

booktitle = "Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology",

}

TY - GEN

T1 - Building a motivational interviewing dataset

AU - Pérez-Rosas, Verónica

AU - Mihalcea, Rada

AU - Resnicow, Kenneth

AU - Singh, Satinder

AU - An, Lawrence

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85053179241

UR - https://www.scopus.com/pages/publications/85053179241#tab=citedBy

M3 - Conference contribution

AN - SCOPUS:85053179241

T3 - 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

SP - 42

EP - 51

BT - Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology

A2 - Hollingshead, Kristy

A2 - Ungar, Lyle

PB - Association for Computational Linguistics (ACL)

T2 - 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

Y2 - 16 June 2016

ER -

Building a motivational interviewing dataset

Abstract

Publication series

Conference

Bibliographical note

Find It

Other files and links

Fingerprint

Cite this