Assessing Users' Mental Status from their Journaling Behavior through Chatbots

Abstract

Chatbots (conversational agents) are increasingly receiving attention in mental health domains because they elicit honest self-disclosure about personal experiences and emotions. Although such self-disclosure contents can be useful for gauging mental status, little research has addressed how to automatically assess mental status from self-disclosures to a chatbot. If a chatbot can automatically assess the mental status of users, it can help them improve their mental wellness or facilitate access to mental professionals. In this paper, we examine whether indicators that identify depression from written texts (e.g., social media posts) are also useful for assessing mental status from disclosures to a chatbot. We first ran a study with 30 participants who engaged in daily journaling with a chatbot that prompted them to record their moods and experiences for three weeks. We then divided the participants' self-disclosure data into three groups based on their mental state changes before and after the study: improved vs. deteriorated vs. no change. Comparing the data among the three groups, participants whose mental states deteriorated during the study gradually used fewer positive emotion and concrete words but more negative emotion words when describing their daily experiences and feelings to the chatbot.