Cardiovascular Disease Risk Prediction via Social Media

TL;DR

This study demonstrates that sentiment analysis of social media posts, specifically tweets, can effectively predict cardiovascular disease risk, outperforming traditional demographic-based methods.

Contribution

The paper introduces a novel CVD-related keyword dictionary and applies sentiment analysis combined with machine learning to predict CVD risk from social media data.

Findings

Sentiment analysis of tweets improves CVD risk prediction accuracy.

Emotion-based analysis outperforms demographic data alone.

NLP and ML techniques can be effective tools for public health monitoring.

Abstract

Researchers use Twitter and sentiment analysis to predict Cardiovascular Disease (CVD) risk. We developed a new dictionary of CVD-related keywords by analyzing emotions expressed in tweets. Tweets from eighteen US states, including the Appalachian region, were collected. Using the VADER model for sentiment analysis, users were classified as potentially at CVD risk. Machine Learning (ML) models were employed to classify individuals' CVD risk and applied to a CDC dataset with demographic information to make the comparison. Performance evaluation metrics such as Test Accuracy, Precision, Recall, F1 score, Mathew's Correlation Coefficient (MCC), and Cohen's Kappa (CK) score were considered. Results demonstrated that analyzing tweets' emotions surpassed the predictive power of demographic data alone, enabling the identification of individuals at potential risk of developing CVD. This research highlights the potential of Natural Language Processing (NLP) and ML techniques in using tweets to identify individuals with CVD risks, providing an alternative approach to traditional demographic information for public health monitoring.