Inferring air pollution by sniffing social media

Abstract

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.

Environmental justice in the age of big data: challenging toxic blind spots of voice, speed, and expertise

“…In fact, the majority of big data falls into this category (Kitchin 2013). Mei et al (2014) 'sniffed' social media (Twitter) data about air pollution in China and found correlations between high air pollution readings and high social media activity with key words related to pollution. This technique is particularly useful in areas that do not have official reports about air pollution levels, even though these areas may be less likely to have high or reliable levels of social media activity.…”

Promises and Challenges of Big Data Computing in Health Sciences

“…This air quality monitory system can be publicly accessed at http://urbanair.msra.cn/. Mei et al [36] estimate air quality from social media posts (e.g., Sina Weibo text context). Honicky et al [37] suggested to attach sensors to GPS-enabled cell phones and used them to collect air pollution information.…”

Forecasting smog-related health hazard based on social media and physical sensor

“…Although social media has been widely applied in investigating natural disasters and public health problems, there are very few studies that use social media to investigate smog disasters, not to mention smog-related health hazard. Mei et al [23] was one of the earliest studies for smog disaster analysis with social media, but it aimed to infer the smog severity in the cities where no air quality stations were deployed, which was not related to smog-related health hazard. Our previous work [24] utilized Chinese tweets on Weibo to analyze the correlation between smog disasters and public health statuses, but did not study smog-related health hazard forecasting.…”