Effective Prediction and Important Counseling Experience for Perceived Helpfulness of Social Question and Answering-Based Online Counseling: An Explainable Machine Learning Model

Abstract

The social question answering based online counseling (SQA-OC) is easy access for people seeking professional mental health information and service, has become the crucial pre-consultation and application stage toward online counseling. However, there is a lack of efforts to evaluate and explain the counselors' service quality in such an asynchronous online questioning and answering (QA) format efficiently. This study applied the notion of perceived helpfulness as a public's perception of counselors' service quality in SQA-OC, used computational linguistic and explainable machine learning (XML) methods suited for large-scale QA discourse analysis to build an predictive model, explored how various sources and types of linguistic cues [i.e., Linguistic Inquiry and Word Count (LIWC), topic consistency, linguistic style similarity, emotional similarity] contributed to the perceived helpfulness. Results show that linguistic cues from counselees, counselors, and synchrony between them are important predictors, the linguistic cues and XML can effectively predict and explain the perceived usefulness of SQA-OC, and support operational decision-making for counselors. Five helpful counseling experiences including linguistic styles of “talkative”, “empathy”, “thoughtful”, “concise with distance”, and “friendliness and confident” were identified in the SQA-OC. The paper proposed a method to evaluate the perceived helpfulness of SQA-OC service automatically, effectively, and explainable, shedding light on the understanding of the SQA-OC service outcome and the design of a better mechanism for SQA-OC systems.