Feasibility of using social media to monitor outdoor air pollution in London, England

Hswen, Yulin; Qin, Qiuyuan; Brownstein, John S.; Hawkins, Joseph E.

doi:10.1016/j.ypmed.2019.02.005

Cited by 41 publications

(26 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With such large volume of real-time data, social media has become an important data source in both industry and academia. It is extensively used in a wide range of topics, including monitoring outdoor air pollution in London [3], modeling rumor spreading [4,5], preventing sensitive information attacks, achieving disease surveillance [6], detecting natural disasters [7], etc.…”

Section: Introductionmentioning

confidence: 99%

Understanding Social Media Beyond Text: A Reliable Practice on Twitter

Hou

Han

2020

Preprint

View full text Add to dashboard Cite

Social media has been broadly applied in many applications in sales, marketing, event detection, etc. With high-volume and real-time data, social media has also been used for disaster responses. However, distinguishing between rumors and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. Furthermore, the rich information is not only included in the short text content but also embedded in the images, videos. In this paper, to address the emerging challenge of disaster response, we introduce a reliable framework for disaster information understanding and response with a practice on Twitter. The framework integrates both textual and imagery content from tweets in hope to fully utilize the information. The text classifier is built to remove noises, which can achieve 0.92 F1-score in classifying individual tweet. The image classifier is constructed by fine-tuning pre-trained VGG-F network, which can achieve 90\% accuracy. The image classifier serves as a verifier in the pipeline to reject or confirm the detected events. The evaluation indicates that the verifier can significantly reduce false positive events. We also explore Twitter-based drought management system and infrastructure monitoring system to further study the impacts of imagery content on event detection systems and we are able to pinpoint additional benefits which can be gained from social media imagery content.

show abstract

Section: Introductionmentioning

confidence: 99%

Understanding Social Media Beyond Text: A Reliable Practice on Twitter

Hou

Han

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The association between energy and environmental pollution and energy and environment efficiency have been widely studied (Hu and Wang, 2006;Yeh et al, 2010;Song et al, 2013;Wu et al, 2015;Li and Du, 2015;Meng et al, 2016;Yao et al, 2016;Abbas et al, 2017;Qin et al, 2017;Feng et al, 2017;Hu et al, 2017;Mehmeti et al, 2018), with some studies identifying the factors that affect energy efficiency (Bampatsou et al, 2013;Li et al, 2013;Lin and Du, 2015;Li and Lin, 2017;Du et Al., 2017;Wang et al, 2017;Jebali et al, 2017), and others exploring the relationship between air pollution, the environment and the mass media (McCreery, 2010;Mayer, 2012;Chen, 2014;Kay et al, 2015;Hswen et al, 2019). Some scholars have also studied the effects of news media reports on improving the public's awareness of environmental protection and environmental protection (Lowe and Morrison, 1984;McCreery, 2010;Niklas et al, 2011;Marianne and Mason, 2012;Wang et.…”

Section: Introductionmentioning

confidence: 99%

“…examined the corporate environmental reporting of 100 companies in promoting social responsibility for groups such as employees and shareholders and members outside the group such as consumers, Chen (2014) analyzed reports from China's official English-language newspaper, the China Daily, on air pollution in China in 2013, and Wang et al (2015)studied Chinese social media to monitor air quality trends and related public perceptions and responses, finding that the information on Weibo reflected the true level of particle pollution Kay et al (2015). took Sina Weibo as a research object and concluded that it effectively promoted public environmental awareness,Kerrie and Claire (2018) found that the Australian news media played an important role in protecting coral reefs, andHswen et al (2019) found that social media could aid in environmental monitoring.…”

mentioning

confidence: 99%

Impacts on Energy and Environmental Efficiency by Media Report – Modifies Undesirable Dynamic DEA

Liu

Lin

Chiu³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Rapid economic growth in China has resulted in a commensurate increase in energy consumption, which in turn has caused an increase in environmental pollution problems. Past research has mainly focused on energy and environmental efficiency analyses with little consideration of the influence of media influence on environmental protection. Further, most studies have used static models and have ignored the dynamic changes over time. Methods : To go some way to filling this research gap, this study developed a modified undesirable Dynamic DEA model that included air quality index (AQI) and CO2 indicators to explore the relationship between energy, the environment and media efficiency in 31 Chinese cities from 2013 to 2016. Results: It was found that: 1. Chongqing, Guangzhou, Nanjing and Shanghai had efficiencies of 1, but Lanzhou, Shijiazhuang, Taiyuan, Xining and Yinchuan needed significant improvements; 2. while Chongqing, Guangzhou, Kunming, Nanning and Shanghai had relatively high media efficiency, the other cities had low efficiency and required improvements; 3. the CO 2 emissions efficiency in most cities was better than the air quality index efficiency; and 4. media reports in most cities were found to have a more positive impact on CO 2 emissions efficiency than AQI efficiency. Conclusions: As environmental awareness enhances the health of civilians and promotes economic growth, the news media needs to promote environmental protection, and should increase its environmental pollution coverage. The quality of media reports on environmental pollution and especially on air pollution need to be improved. Therefore, environmental pollution and awareness media coverage needs to be increased.

show abstract

“…As an increasing number of people are voicing their feelings and opinions about their living environments on social media, it has proven feasible and effective to use social media data to monitor the quality of the environment, including air pollution, which has long been a serious public health issue [8][9][10]. Performing a sentiment analysis on social media data to extract public opinions also provides a new data source and a new method for tourism research [11].…”

Section: Introductionmentioning

confidence: 99%

Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

et al. 2019

View full text Add to dashboard Cite

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

show abstract

Feasibility of using social media to monitor outdoor air pollution in London, England

Cited by 41 publications

References 34 publications

Understanding Social Media Beyond Text: A Reliable Practice on Twitter

Understanding Social Media Beyond Text: A Reliable Practice on Twitter

Impacts on Energy and Environmental Efficiency by Media Report – Modifies Undesirable Dynamic DEA

Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

Contact Info

Product

Resources

About