ArticlePreventive medicine2019
Feasibility of using social media to monitor outdoor air pollution in London, England.
Article in Preventive medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Outdoor Artificial Light at Night and Insomnia-Related Social Media Posts.JAMA network open · 2024Article
- Linking coastal environmental and health observations for human wellbeing.Frontiers in public health · 2023Article
- Public Concern about Air Pollution and Related Health Outcomes on Social Media in China: An Analysis of Data from Sina Weibo (Chinese Twitter) and Air Monitoring Stations.International journal of environmental research and public health · 2022Article
- Visualization and Analysis of Air Pollution and Human Health Based on Cluster Analysis: A Bibliometric Review from 2001 to 2021.International journal of environmental research and public health · 2022Review
- The impact of media reports on energy and environmental efficiency in China: evidence from modified dynamic DEA with undesirable outputs.Cost effectiveness and resource allocation : C/E · 2021Article
- Using Social Media Mining and PLS-SEM to Examine the Causal Relationship between Public Environmental Concerns and Adaptation Strategies.International journal of environmental research and public health · 2021Article
- Association of "#covid19" Versus "#chinesevirus" With Anti-Asian Sentiments on Twitter: March 9-23, 2020.American journal of public health · 2021Article
- Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis.International journal of environmental research and public health · 2020Article
- A scoping review of the use of Twitter for public health research.Computers in biology and medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Air pollution is a serious public health concern. Innovative and scalable methods for detecting harmful air pollutants such as PM2.5 are necessary. This study assessed the feasibility of using social media to monitor outdoor air pollution in an urban area by comparing data from Twitter and validating it against established air monitoring stations. Data were collected from London, England from July 29, 2016 to March 17, 2017. Daily mean PM2.5 data was downloaded from the LondonAir platform consisting of 26 air pollution monitoring sites throughout Greater London. Publicly available tweets geo-located to Greater London containing air pollution terms were captured from the Twitter platform. Tweets with media URL links were excluded to minimize influence of news stories. Sentiment of the tweets was examined as negative, positive, or neutral. Cross-correlation analyses were used to compare the relationship between trends of tweets about air pollution and levels of PM2.5 over time. There were 16,448 tweets without a media URL link, with a mean of 498.42 (SD = 491.08) tweets per week. A significant cross-correlation coefficient of 0.803 was observed between PM2.5 data and the non-media air pollution tweets (p < 0.001). The cross-correlation coefficient was highest between PM2.5 data and air pollution tweets with negative sentiment at 0.816 (p < 0.001). Discussions about air pollution on Twitter reflect particle PM2.5 pollution levels in Greater London. This study highlights that social media may offer a supplemental source to support the detection and monitoring of air pollution in a densely populated urban area.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.