Research Article
Building a Tool that Draws from the Collective Wisdom of the Internet to Help Users Respond Effectively to Anxiety-Related Questions
@INPROCEEDINGS{10.1007/978-3-030-99194-4_2, author={Benjamin T. Kaveladze and George I. Kaveladze and Elad Yom-Tov and Stephen M. Schueller}, title={Building a Tool that Draws from the Collective Wisdom of the Internet to Help Users Respond Effectively to Anxiety-Related Questions}, proceedings={Pervasive Computing Technologies for Healthcare. 15th EAI International Conference, Pervasive Health 2021, Virtual Event, December 6-8, 2021, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2022}, month={3}, keywords={Online mental health communities Question answering Big data Online support provision Anxiety}, doi={10.1007/978-3-030-99194-4_2} }- Benjamin T. Kaveladze
George I. Kaveladze
Elad Yom-Tov
Stephen M. Schueller
Year: 2022
Building a Tool that Draws from the Collective Wisdom of the Internet to Help Users Respond Effectively to Anxiety-Related Questions
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-030-99194-4_2
Abstract
Online anxiety support communities offer a valuable and accessible source of informational and emotional support for people around the world. However, effectively responding to posters’ anxiety-related questions can be challenging for many users. We present our work in developing a web-based tool that draws from previous question-response interactions and trusted online informational resources to help users rapidly produce high-quality responses to anxiety-related questions. We describe our efforts in four parts: 1) Creating a machine learning classifier to predict response quality, 2) developing and evaluating a computational question-answering system that learns from previous questions and responses on support forums, 3) developing and evaluating a system to suggest online resources for anxiety-related questions, and 4) interviewing support community moderators to inform further system design. We discuss how this tool might be integrated into online anxiety support communities and consider challenges with the tool’s functionality and implementation. We also provide the dataset we used to train the system to provide opportunities for other researchers to build on this work.
