We propose building an intelligent system that examines social media users by monitoring public posts (e.g. tweets, shares, replies) through a system of various state-of-the-art (deep-learning-based) AI triggers to identify and assign risk scores to users (e.g. patients) and to deeply study opioid usage among pregnant women, to take preventative actions for at-risk users, i.e. relapsing, abuse, addiction, and susceptibility to use.
While Twitter and Reddit are great sources for studying patients with opioid usage and substance abuse, identifying the right users who pose the risk of harmful side effects from opioids or addiction is extremely challenging. This is primarily due to the habits and style in which users post content online – social media posts are generally unreliable and full of false information that has zero relevance to the study – hence, traditional text mining tools don’t work. Furthermore, traditional social media analytics tools focus on branding and impressions instead of case studies. In fact, numerous case studies conducted by health agencies (e.g. for studying epidemics, adverse reactions, etc.) from Twitter data have failed to reach any conclusive findings due to the vast noise that floods social media feeds.
To save the time-to-operation and the cost of hiring data scientists and engineers, we can reduce the numerous challenges of building a data mining engine by configuring our COTS product ThinkTrends Social. ThinkTrends (www.thinktrends.co) is a data mining engine that can be configured to capture and study structured and unstructured data (e.g. social media), with easy-to-use data labelling, business intelligence, and AI & DL automation tools all in one place. ThinkTrends Social is a custom version with in-built social connectors (i.e. Twitter and Reddit). This creates an ideal data science environment to conduct case studies from real-time social media data, and freely build/share/replicate deep learning models to filter the junk.
The initial phase will use two forms of AI/deep learning: computer vision to detect from images and videos on social media (i.e. detect stages of pregnancy from public posts, and recognise facial sentiments from photos); and by utilising the latest natural language understanding (NLU), we can train AI to learn patient attitudes, vulnerability to relapse, intents, and authenticity of Twitter posts by analysing against past posts/retweets. The AI triggers will categorise sentiments, emotions, and intents from patients into potential risk factors from public posts. Ultimately, the analyst will use dashboards to cohesively study posts and quickly find at-risk users/patients to suggest preventive measures well ahead of time if needed.