Effects of PM2.5 on People’s Emotion: A Case Study of Weibo (Chinese Twitter) in Beijing

PM2.5 not only harms physical health but also has negative impacts on the public’s wellbeing and cognitive and behavioral patterns. However, traditional air quality assessments may fail to provide comprehensive, real-time monitoring of air quality because of the sparse distribution of air quality monitoring stations. Overcoming some key limitations of traditional surface monitoring data, Web-based social media platforms, such as Twitter, Weibo, and Facebook, provide a promising tool and novel perspective for environmental monitoring, prediction, and evaluation. This study aims to investigate the relationship between PM2.5 levels and people’s emotional intensity by observing social media postings. This study defines the “emotional intensity” indicator, which is measured by the number of negative posts on Weibo, based on Weibo data related to haze from 2016 and 2017. This study estimates sentiment polarity using a recurrent neural networks model based on LSTM (Long Short-Term Memory) and verifies the correlation between high PM2.5 levels and negative posts on Weibo using a Pearson correlation coefficient and multiple linear regression model. This study makes the following observations: (1) Taking the two-year data as an example, this study recorded the significant influence of PM2.5 levels on netizens’ posting behavior. (2) Air quality, meteorological factors, the seasons, and other factors have a strong influence on netizens’ emotional intensity. (3) From a quantitative viewpoint, the level of PM2.5 varies by 1 unit, and the number of negative Weibo posts fluctuates by 1.0168 units. Thus, it can be concluded that netizens’ emotional intensity is significantly positively affected by levels of PM2.5. The high correlation between PM2.5 levels and emotional intensity and the sensitivity of social media data shows that social media data can be used to provide a new perspective on the assessment of air quality.

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A long short-term memory approach to predicting air quality based on social media data

Atmospheric Environment ◽

10.1016/j.atmosenv.2020.117411 ◽

2020 ◽

Vol 237 ◽

pp. 117411

Author(s):

Weixin Zhai ◽

Chengqi Cheng

Keyword(s):

Social Media ◽

Air Quality ◽

Short Term Memory ◽

Short Term ◽

Social Media Data ◽

Term Memory ◽

Long Short Term Memory ◽

Media Data

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

Sustainability ◽

10.3390/su11185070 ◽

2019 ◽

Vol 11 (18) ◽

pp. 5070 ◽

Cited By ~ 3

Author(s):

Yuguo Tao ◽

Feng Zhang ◽

Chunyun Shi ◽

Yun Chen

Keyword(s):

Machine Learning ◽

Social Media ◽

Content Analysis ◽

Air Quality ◽

Sentiment Analysis ◽

Social Media Data ◽

Sina Weibo ◽

Emotion Words ◽

Tourist Destinations ◽

Media Data

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.

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Depression Analysis from Social Media Data in Bangla Language using Long Short Term Memory (LSTM) Recurrent Neural Network Technique

2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) ◽

10.1109/ic4me247184.2019.9036528 ◽

2019 ◽

Cited By ~ 2

Author(s):

Abdul Hasib Uddin ◽

Durjoy Bapery ◽

Abu Shamim Mohammad Arif

Keyword(s):

Neural Network ◽

Social Media ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Short Term ◽

Social Media Data ◽

Term Memory ◽

Long Short Term Memory ◽

Media Data

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Sarcastic user behavior classification and prediction from social media data using firebug swarm optimization-based long short-term memory

The Journal of Supercomputing ◽

10.1007/s11227-021-04028-4 ◽

2021 ◽

Author(s):

E. Karthik ◽

T. Sethukarasi

Keyword(s):

Social Media ◽

Short Term Memory ◽

User Behavior ◽

Short Term ◽

Swarm Optimization ◽

Social Media Data ◽

Term Memory ◽

Long Short Term Memory ◽

Media Data

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Web visibility of Romanian universities – an analysis based on website analytics and social media data

Proceedings of the 4th International Conference on Higher Education Advances (HEAd'18) ◽

10.4995/head18.2018.8036 ◽

2018 ◽

Author(s):

Andreea Mirica ◽

Iulia Elena Toma

Keyword(s):

Social Media ◽

General Public ◽

Public Universities ◽

Pearson Correlation ◽

Nonparametric Test ◽

Public And Private ◽

Social Media Data ◽

Available Technology ◽

Media Data ◽

Web Visibility

There are many aspects that influence the importance of a university. Among these, web visibility is taking advantage of available technology, focusing on the benefits of internet and social media. This paper provides an analysis of the Romanian public and private universities websites, using alexa.com, a tool for website traffic analysis. Moreover, the official social media accounts of universities (Facebook, Twitter, Google+, Flickr, YouTube and Instagram) were analysed, collecting several data such as: the number of likes and followers, the number of subscribers and the number of posts. The analysis was performed using ANOVA and Nonparametric Test for the presence via websites and Spearman and Pearson correlation to assess the correlation between social media and website traffic. In general, public universities from Romania have more visibility and a higher number of links to their websites compared to private ones. Based on the fact that Facebook and site content are often related, it should be noted that activity on Facebook may improve website rankings for Romanian universities.

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An LSTM Model for Predicting Cross-Platform Bursts of Social Media Activity

Information ◽

10.3390/info10120394 ◽

2019 ◽

Vol 10 (12) ◽

pp. 394

Author(s):

Neda Hajiakhoond Bidoki ◽

Alexander V. Mantzaris ◽

Gita Sukthankar

Keyword(s):

Social Media ◽

Short Term Memory ◽

Fundamental Problem ◽

Social Media Data ◽

Burst Analysis ◽

Cross Platform ◽

Media Platform ◽

Long Short Term Memory ◽

Media Data ◽

Activity Information

Burst analysis and prediction is a fundamental problem in social network analysis, since user activities have been shown to have an intrinsically bursty nature. Bursts may also be a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross platform social media data may be valuable for predicting bursts within a single social media platform. A Long-Short-Term-Memory (LSTM) model is proposed in order to capture the temporal dependencies and associations based upon activity information. The data used to test the model was collected from Twitter, Github, and Reddit. Our results show that the LSTM based model is able to leverage the complex cross-platform dynamics to predict bursts. In situations where information gathering from platforms of concern is not possible the learned model can provide a prediction for whether bursts on another platform can be expected.

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