Yoga-Veganism: Correlation Mining of Twitter Health Data
This work provides a method for automatically mining health-related discussions from social media to understand public interests, though it appears incremental in applying existing topic modeling techniques to a new dataset.
The researchers analyzed Twitter health data to identify popular topics and discover hidden correlations, finding a specific correlation between yoga and veganism. They evaluated their approach by comparing topic modeling results against manually annotated ground truth data.
Nowadays social media is a huge platform of data. People usually share their interest, thoughts via discussions, tweets, status. It is not possible to go through all the data manually. We need to mine the data to explore hidden patterns or unknown correlations, find out the dominant topic in data and understand people's interest through the discussions. In this work, we explore Twitter data related to health. We extract the popular topics under different categories (e.g. diet, exercise) discussed in Twitter via topic modeling, observe model behavior on new tweets, discover interesting correlation (i.e. Yoga-Veganism). We evaluate accuracy by comparing with ground truth using manual annotation both for train and test data.