October 5, 2025Open Access

Application of Artificial Intelligence in Chatbot and Social Media Community for a Mental Health Monitoring Web-Application

DKDavid Oluwaseun KajewoleUniversity of Johannesburg OUObumneme Obiajulu UmeonwukaRochester Institute of Technology ZAZakka AugustineFederal University of Agriculture

Key Points

The application shows that users felt 'happier' and 'somewhat mentally relieved' after interacting with the chatbot.
Among 13 users, 31% reported feeling increased happiness, indicating the chatbot's potential effectiveness.
The development utilized the MERN stack to create a web application integrating chatbot and social media features.
User recommendations for the app emphasize its potential for enhanced mental health monitoring and support.

Abstract

Mental health is a major challenge for modern society affecting people of working age and their families, jobs, and communities. To provide continuous emotional support, personalized assistance, and remote mental health tracking, chatbots and social media platforms are increasingly using Artificial Intelligence (AI). There is little or no previous literature that has documented a fully functional mental health monitoring web-app that uses AI to provide all these features: chatbot, e-community, video conferencing, and email automation. Chatbots are software systems that offer various interactive online services, including people with mental health needs and have been successfully integrated into the field of mental healthcare. Similarly, social media can help monitor the mental health situation by extracting information from posts for sentiment analysis as people often post their feelings on social media, so analysing these posts can reveal their mood, emotion, cognition, or mental state. This paper solves the problem sentiment by using the MERN stack to develop an interactive mental health monitoring application which features: social media community, chatbot named ‘Dave the HappyBot’, Email automation and video conferencing. The results showed that among the 13 users of the web-app from a performance indices class of A, B, C, D, and E, majority indicated that they felt ‘happier’ and ‘somewhat mentally relieved’ after interacting with the Chatbot. The A class recorded 18% responses, B class obtained 31%, the C class got 19%, the D class achieved 26%, and the E class gained 6%. The users were open to recommending the App to their peer and meeting their psychologist for constant mental health monitoring sessions. This paper can be further improved documenting the developments of web-app with more users who possess higher knowledge of software development and have a history of mental health struggle. Higher utility of the MERN stack would also be an improvement.

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