Algorithmic Teenagers’ Depression Detection on Social Media and Automated Instant Engagement Using Therapy Bot Powered by Multimodal Deep Learning and Psychotherapy Intervention

Olubayo Adekanmbi, Mobolurin Ademinola Adekanmbi, Mofolusayo Oluwademilade Adekanmbi, Oluwatoyin Oyenike Adekanmbi

Keywords: Depression, Detection, Bot, Therapy, Multimodal, Deep Learning, Instant Intervention, Algorithmic

TL;DR: Algorithmic approaches to detecting depression among teenagers on social media using multimodal deep learning and the deployment of an automated instant bot therapy., as well as

Abstract:

Social media is a large and growing feature of teen life across the world. While some research suggests that social media are partly
to blame for growing rates of mental illness among teens, social media can also play a positive role in promoting teen mental health
by giving teens new ways to socialize and feel part of a community.

In this work, we propose a framework for developing system that can further enhance the upsides of social media use: a computational model that uses social media data to predict depression, as part of a detection-and-intervention loop that engages the user in positive conversations when dynamic indicators of depression present themselves in their social media activity.

Our framework uses three pillars of multimodal Content, Behavioral, and Contextual data drawn from users’ social media feeds in order to provide timely detection and intervention services via a chatbot. This multimodal architecture allows us to envision chat features that are precise and responsive to the behavior that triggers the detection. We present a review of the state of the art in depression detection
systems, and then proceed to explain our system, which builds upon successes in deep learning-based detection systems, as well as
placing these tools in the new dynamic setting of online depression detection that enables our chatbot to initiate therapeutic interactions with social media users.