Review mining to discover user experience issues in mental health and wellbeing chatbots

Frederick Booth; Courtney Potts; RR Bond; Maurice Mulvenna; Edel Ennis; Michael McTear

doi:10.1145/3552327.3552357

Review mining to discover user experience issues in mental health and wellbeing chatbots

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

Abstract

Mental health and wellbeing chatbots are growing in popularity. Involving the end-user in creating these products is an important design consideration, to ensure technologies meet user needs and are easy to use. Extensive databases of app reviews provide rich data sources which can inform design, based on user feedback of apps already in existence. In this study, review mining was conducted on app reviews (n=20,461) across 7 mental health and wellbeing chatbots, focusing on the reviews that included the topics of design and user experience. The aim is to establish what user experience issues of mental wellbeing chatbots can be discovered. Natural language processing techniques were used to analyse reviews, and k-means clustering was applied to identify similar reviews based on content. These processes can be used to provide recommendations to designers of digital mental health technologies.

Original language	English
Title of host publication	ECCE 2022 - Proceedings of the 33rd European Conference on Cognitive Ergonomics
Publisher	Association for Computing Machinery
Pages	1-5
Number of pages	5
ISBN (Electronic)	978-1-4503-9808-4
DOIs	https://doi.org/10.1145/3552327.3552357
Publication status	Published (in print/issue) - 4 Oct 2022
Event	33rd European Conference on Cognitive Ergonomics - Kaiserslautern, Germany Duration: 4 Oct 2022 → 7 Oct 2022 https://hci.uni-kl.de/ecce2022/

Conference

Conference	33rd European Conference on Cognitive Ergonomics
Abbreviated title	ECCE 2022
Country/Territory	Germany
City	Kaiserslautern
Period	4/10/22 → 7/10/22
Internet address	https://hci.uni-kl.de/ecce2022/

Bibliographical note

Funding Information:
This study acknowledges the funding received from the Interreg Northern Periphery and Arctic Programme under grant number 345 (ChatPal).

Publisher Copyright:
© 2022 ACM.

Funding

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

User reviews
Text mining
Usability
Mental health
Natural language processing
Chatbots
Well being
Apps
Conversational user interfaces

Access to Document

10.1145/3552327.3552357

Cite this

@inproceedings{8db204baeadc4419adefbd7fd48ed1ba,

title = "Review mining to discover user experience issues in mental health and wellbeing chatbots",

abstract = "Mental health and wellbeing chatbots are growing in popularity. Involving the end-user in creating these products is an important design consideration, to ensure technologies meet user needs and are easy to use. Extensive databases of app reviews provide rich data sources which can inform design, based on user feedback of apps already in existence. In this study, review mining was conducted on app reviews (n=20,461) across 7 mental health and wellbeing chatbots, focusing on the reviews that included the topics of design and user experience. The aim is to establish what user experience issues of mental wellbeing chatbots can be discovered. Natural language processing techniques were used to analyse reviews, and k-means clustering was applied to identify similar reviews based on content. These processes can be used to provide recommendations to designers of digital mental health technologies.",

keywords = "User reviews, Text mining, Usability, Mental health, Natural language processing, Chatbots, Well being, Apps, Conversational user interfaces",

author = "Frederick Booth and Courtney Potts and RR Bond and Maurice Mulvenna and Edel Ennis and Michael McTear",

note = "Funding Information: This study acknowledges the funding received from the Interreg Northern Periphery and Arctic Programme under grant number 345 (ChatPal). Publisher Copyright: {\textcopyright} 2022 ACM.; 33rd European Conference on Cognitive Ergonomics, ECCE 2022 ; Conference date: 04-10-2022 Through 07-10-2022",

year = "2022",

month = oct,

day = "4",

doi = "10.1145/3552327.3552357",

language = "English",

pages = "1--5",

booktitle = "ECCE 2022 - Proceedings of the 33rd European Conference on Cognitive Ergonomics",

publisher = "Association for Computing Machinery",

address = "United States",

url = "https://hci.uni-kl.de/ecce2022/",

}

Booth, F, Potts, C , Bond, RR , Mulvenna, M , Ennis, E & McTear, M 2022, Review mining to discover user experience issues in mental health and wellbeing chatbots. in ECCE 2022 - Proceedings of the 33rd European Conference on Cognitive Ergonomics., 26, Association for Computing Machinery, pp. 1-5, 33rd European Conference on Cognitive Ergonomics, Kaiserslautern, Germany, 4/10/22. https://doi.org/10.1145/3552327.3552357

Review mining to discover user experience issues in mental health and wellbeing chatbots. / Booth, Frederick; Potts, Courtney ; Bond, RR et al.
ECCE 2022 - Proceedings of the 33rd European Conference on Cognitive Ergonomics. Association for Computing Machinery, 2022. p. 1-5 26.

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

TY - GEN

T1 - Review mining to discover user experience issues in mental health and wellbeing chatbots

AU - Booth, Frederick

AU - Potts, Courtney

AU - Bond, RR

AU - Mulvenna, Maurice

AU - Ennis, Edel

AU - McTear, Michael

PY - 2022/10/4

Y1 - 2022/10/4

N2 - Mental health and wellbeing chatbots are growing in popularity. Involving the end-user in creating these products is an important design consideration, to ensure technologies meet user needs and are easy to use. Extensive databases of app reviews provide rich data sources which can inform design, based on user feedback of apps already in existence. In this study, review mining was conducted on app reviews (n=20,461) across 7 mental health and wellbeing chatbots, focusing on the reviews that included the topics of design and user experience. The aim is to establish what user experience issues of mental wellbeing chatbots can be discovered. Natural language processing techniques were used to analyse reviews, and k-means clustering was applied to identify similar reviews based on content. These processes can be used to provide recommendations to designers of digital mental health technologies.

AB - Mental health and wellbeing chatbots are growing in popularity. Involving the end-user in creating these products is an important design consideration, to ensure technologies meet user needs and are easy to use. Extensive databases of app reviews provide rich data sources which can inform design, based on user feedback of apps already in existence. In this study, review mining was conducted on app reviews (n=20,461) across 7 mental health and wellbeing chatbots, focusing on the reviews that included the topics of design and user experience. The aim is to establish what user experience issues of mental wellbeing chatbots can be discovered. Natural language processing techniques were used to analyse reviews, and k-means clustering was applied to identify similar reviews based on content. These processes can be used to provide recommendations to designers of digital mental health technologies.

KW - User reviews

KW - Text mining

KW - Usability

KW - Mental health

KW - Natural language processing

KW - Chatbots

KW - Well being

KW - Apps

KW - Conversational user interfaces

UR - https://www.scopus.com/pages/publications/85139971882

U2 - 10.1145/3552327.3552357

DO - 10.1145/3552327.3552357

M3 - Conference contribution

SP - 1

EP - 5

BT - ECCE 2022 - Proceedings of the 33rd European Conference on Cognitive Ergonomics

PB - Association for Computing Machinery

T2 - 33rd European Conference on Cognitive Ergonomics

Y2 - 4 October 2022 through 7 October 2022

ER -

Review mining to discover user experience issues in mental health and wellbeing chatbots

Abstract

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Analytics, visualisation and machine learning of general practitioner prescribing using open health data

Real world data driven insights in mental health sciences

Cite this

Review mining to discover user experience issues in mental health and wellbeing chatbots

Abstract

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Student theses

Analytics, visualisation and machine learning of general practitioner prescribing using open health data

Real world data driven insights in mental health sciences

Cite this