Inferring air pollution by sniffing social media
S. Mei, H. Li, J. Fan, X. Zhu and C. R. Dyer, Proc. 2014 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining, 2014.

Abstract

The first step to deal with the significant issue of air pollution in China and elsewhere in the world is to monitor it. While more physical monitoring stations are built, current coverage is limited to large cities with most other places undermonitored. In this paper we propose a complementary approach to monitor Air Quality Index (AQI): using machine learning models to estimate AQI from social media posts. We propose a series of progressively more sophisticated machine learning models, culminating in a Markov Random Field model that utilizes the text content in social media as well as the spatiotemporal correlation among cities and days. Our extensive experiments on Sina Weibo data from 108 cities during a one-month period demonstrate the accurate AQI prediction performance of our approach.