Health Monitoring on Social Media over Time
Sumit Sidana, Shashwat Mishra, Sihem Amer-Yahia, Marianne Clausel, Massih-Reza Amini
Laboratoire d'Informatique de Grenoble
700, avenue Centrale
38058 Saint-Martin d'Hérès
Social media has become a major source for analyzing all aspects
of daily life. Thanks to dedicated latent topic analysis
methods such as the Ailment Topic Aspect Model (ATAM),
public health can now be observed on Twitter. In this work,
we are interested in monitoring people's health over time.
Recently, Temporal-LDA (TM-LDA) was proposed for efficiently
modeling general-purpose topic transitions over time.
In this paper, we propose Temporal Ailment Topic Aspect
(TM-ATAM), a new latent model dedicated to capturing
transitions that involve health-related topics. TM-ATAM
learns topic transition parameters by minimizing the prediction
error on topic distributions between consecutive posts
at different time and geographic granularities. Our experiments
on an 8-month corpus of tweets show that it largely
outperforms its predecessors.