1
|
Wang C, Bai YX, Li XW, Lin LT. Effects of extreme temperatures on public sentiment in 49 Chinese cities. Sci Rep 2024; 14:9954. [PMID: 38688992 PMCID: PMC11061318 DOI: 10.1038/s41598-024-60804-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/26/2024] [Indexed: 05/02/2024] Open
Abstract
The rising sentiment challenges of the metropolitan residents may be attributed to the extreme temperatures. However, nationwide real-time empirical studies that examine this claim are rare. In this research, we construct a daily extreme temperature index and sentiment metric using geotagged posts on one of China's largest social media sites, Weibo, to verify this hypothesis. We find that extreme temperatures causally decrease individuals' sentiment, and extremely low temperature may decrease more than extremely high temperature. Heterogeneity analyses reveal that individuals living in high levels of PM2.5, existing new COVID-19 diagnoses and low-disposable income cities on workdays are more vulnerable to the impact of extreme temperatures on sentiment. More importantly, the results also demonstrate that the adverse effects of extremely low temperatures on sentiment are more minor for people living in northern cities with breezes. Finally, we estimate that with a one-standard increase of extremely high (low) temperature, the sentiment decreases by approximately 0.161 (0.272) units. Employing social media to monitor public sentiment can assist policymakers in developing data-driven and evidence-based policies to alleviate the adverse impacts of extreme temperatures.
Collapse
Affiliation(s)
- Chan Wang
- School of Economics, Guangdong University of Finance and Economics, Guangzhou, 510320, People's Republic of China
| | - Yi-Xiang Bai
- School of Economics, Guangdong University of Finance and Economics, Guangzhou, 510320, People's Republic of China.
| | - Xin-Wu Li
- School of Economics, Nankai University, Tianjin, 300071, People's Republic of China
| | - Lu-Tong Lin
- School of Economics, Guangdong University of Finance and Economics, Guangzhou, 510320, People's Republic of China
- School of Economics, Nankai University, Tianjin, 300071, People's Republic of China
| |
Collapse
|
2
|
Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks. J Biomed Inform 2022; 133:104145. [PMID: 35908625 DOI: 10.1016/j.jbi.2022.104145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/27/2022] [Accepted: 07/15/2022] [Indexed: 11/21/2022]
Abstract
In many countries, mental health issues are among the most serious public health concerns. National mental health statistics are frequently collected from reported patient cases or government-sponsored surveys, which have restricted coverage, frequency, and timeliness. Many domains of study, including public healthcare and biomedical informatics, have recently adopted social media data as a feasible real-time alternative to traditional methods of gathering representative information at the population level in a variety of contexts. However, because of the limits of fundamental natural language processing tools and labeled corpora in countries with limited natural language resources, such as Thailand, implementing social media systems to monitor mental health signals could be challenging. This paper presents LAPoMM, a novel framework for monitoring real-time mental health indicators from social media data without using labeled datasets in low-resource languages. Specifically, we use cross-lingual methods to train language-agnostic models and validate our framework by examining cross-correlations between the aggregate predicted mental signals and real-world administrative data from Thailand's Department of Mental Health, which includes monthly depression patients and reported cases of suicidal attempts. A combination of a language-agnostic representation and a deep learning classification model outperforms all other cross-lingual techniques for recognizing various mental signals in Tweets, such as emotions, sentiments, and suicidal tendencies. The correlation analyses discover a strong positive relationship between actual depression cases and the predicted negative sentiment signals as well as suicide attempts and negative signals (e.g., fear, sadness, and disgust) and suicidal tendency. These findings establish the effectiveness of our proposed framework and its potential applications in monitoring population-level mental health using large-scale social media data. Furthermore, because the language-agnostic model utilized in the methodology is capable of supporting a wide range of languages, the proposed LAPoMM framework can be easily generalized for analogous applications in other countries with limited language resources.
Collapse
|
3
|
Sukhwal PC, Kankanhalli A. Determining containment policy impacts on public sentiment during the pandemic using social media data. Proc Natl Acad Sci U S A 2022; 119:e2117292119. [PMID: 35503914 PMCID: PMC9171635 DOI: 10.1073/pnas.2117292119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 02/22/2022] [Indexed: 11/18/2022] Open
Abstract
Stringent containment and closure policies have been widely implemented by governments to prevent the transmission of COVID-19. Yet, such policies have significant impacts on people’s emotions and mental well-being. Here, we study the effects of pandemic containment policies on public sentiment in Singapore. We computed daily sentiment values scaled from −1 to 1, using high-frequency data of ∼240,000 posts from highly followed public Facebook groups during January to November 2020. The lockdown in April saw a 0.1 unit rise in daily average sentiment, followed by a 0.2 unit increase with partially lifting of lockdown in June, and a 0.15 unit fall after further easing of restrictions in August. Regarding the impacts of specific containment measures, a 0.13 unit fall in sentiment was associated with travel restrictions, whereas a 0.18 unit rise was related to introducing a facial covering policy at the start of the pandemic. A 0.15 unit fall in sentiment was linked to restrictions on public events, post lock-down. Virus infection, wearing masks, salary, and jobs were the chief concerns found in the posts. A 2 unit increase in these concerns occurred even when some restrictions were eased in August 2020. During pandemics, monitoring public sentiment and concerns through social media supports policymakers in multiple ways. First, the method given here is a near real-time scalable solution to study policy impacts. Second, it aids in data-driven and evidence-based revision of existing policies and implementation of similar policies in the future. Third, it identifies public concerns following policy changes, addressing which can increase trust in governments and improve public sentiment.
Collapse
Affiliation(s)
| | - Atreyi Kankanhalli
- Department of Information Systems and Analytics, National University of Singapore, 117417 Singapore
| |
Collapse
|
4
|
KARACA YE, ASLAN S. Sentiment Analysis of Covid-19 Tweets by using LSTM Learning Model. COMPUTER SCIENCE 2021. [DOI: 10.53070/bbd.990421] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
5
|
Banda JM, Tekumalla R, Wang G, Yu J, Liu T, Ding Y, Artemova E, Tutubalina E, Chowell G. A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration. EPIDEMIOLOGIA 2021; 2:315-324. [PMID: 36417228 PMCID: PMC9620940 DOI: 10.3390/epidemiologia2030024] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/14/2022] Open
Abstract
As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.
Collapse
Affiliation(s)
- Juan M. Banda
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA;
| | - Ramya Tekumalla
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA;
| | - Guanyu Wang
- Missouri School of Journalism, University of Missouri, Columbia, MO 65201, USA;
| | - Jingyuan Yu
- Department of Social Psychology, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain;
| | - Tuo Liu
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany;
| | - Yuning Ding
- Language Technology Lab, Universität Duisburg-Essen, 47057 Duisburg, Germany;
| | - Ekaterina Artemova
- Faculty of Computer Science, Higher School of Economics—National Research University, 101000 Moscow, Russia;
| | - Elena Tutubalina
- Faculty of Chemistry, Kazan Federal University, 420008 Kazan, Russia;
| | - Gerardo Chowell
- Department of Population Health Sciences, Georgia State University, Atlanta, GA 30303, USA;
| |
Collapse
|
6
|
Rehman MU, Shafique A, Khalid S, Driss M, Rubaiee S. Future Forecasting of COVID-19: A Supervised Learning Approach. SENSORS (BASEL, SWITZERLAND) 2021; 21:3322. [PMID: 34064735 PMCID: PMC8150959 DOI: 10.3390/s21103322] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 05/01/2021] [Accepted: 05/06/2021] [Indexed: 12/18/2022]
Abstract
A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy.
Collapse
Affiliation(s)
- Mujeeb Ur Rehman
- Department of Electrical Engineering, Riphah International University, Islamabad 46000, Pakistan; (A.S.); (S.K.)
| | - Arslan Shafique
- Department of Electrical Engineering, Riphah International University, Islamabad 46000, Pakistan; (A.S.); (S.K.)
| | - Sohail Khalid
- Department of Electrical Engineering, Riphah International University, Islamabad 46000, Pakistan; (A.S.); (S.K.)
| | - Maha Driss
- RIADI Laboratory, University of Manouba, Manouba 2010, Tunisia;
- IS Department, College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia
| | - Saeed Rubaiee
- Department of Industrial and Systems Engineering, College of Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia;
| |
Collapse
|
7
|
Satu MS, Khan MI, Mahmud M, Uddin S, Summers MA, Quinn JMW, Moni MA. TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets. Knowl Based Syst 2021; 226:107126. [PMID: 33972817 PMCID: PMC8099549 DOI: 10.1016/j.knosys.2021.107126] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 05/01/2021] [Accepted: 05/03/2021] [Indexed: 01/31/2023]
Abstract
COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.
Collapse
Affiliation(s)
- Md Shahriare Satu
- Department of Management Information Systems, Noakhali Science & Technology University, Noakhali, 3814, Bangladesh
| | - Md Imran Khan
- Department of Computer Scienc & Engineering, Gono Bishwabidyalay, Savar, Dhaka, 1344, Bangladesh
| | - Mufti Mahmud
- Department of Computer Science, and Medical Technology Innovation Facility, Nottingham Trent University, Clifton Campus, Clifton, Nottingham - NG11 8NS, UK
| | - Shahadat Uddin
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW 2008, Australia
| | - Matthew A Summers
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW 2010, Australia
| | - Julian M W Quinn
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW 2010, Australia
| | - Mohammad Ali Moni
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW 2010, Australia.,WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia
| |
Collapse
|
8
|
Song X, Petrak J, Jiang Y, Singh I, Maynard D, Bontcheva K. Classification aware neural topic model for COVID-19 disinformation categorisation. PLoS One 2021; 16:e0247086. [PMID: 33600477 PMCID: PMC7891716 DOI: 10.1371/journal.pone.0247086] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 01/25/2021] [Indexed: 11/26/2022] Open
Abstract
The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation. The COVID-19 disinformation categories could be used for a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation; b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation. This paper presents: 1) a corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories; 2) a classification-aware neural topic model (CANTM) designed for COVID-19 disinformation category classification and topic discovery; 3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source.
Collapse
Affiliation(s)
- Xingyi Song
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Johann Petrak
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
- Austrian Research Institute for Artificial Intelligence, Vienna, Austria
| | - Ye Jiang
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Iknoor Singh
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
- Panjab University, Chandigarh, India
| | - Diana Maynard
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Kalina Bontcheva
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| |
Collapse
|
9
|
COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18010282. [PMID: 33401512 PMCID: PMC7795453 DOI: 10.3390/ijerph18010282] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 12/22/2020] [Accepted: 12/28/2020] [Indexed: 01/06/2023]
Abstract
Today's societies are connected to a level that has never been seen before. The COVID-19 pandemic has exposed the vulnerabilities of such an unprecedently connected world. As of 19 November 2020, over 56 million people have been infected with nearly 1.35 million deaths, and the numbers are growing. The state-of-the-art social media analytics for COVID-19-related studies to understand the various phenomena happening in our environment are limited and require many more studies. This paper proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) machine learning and other methods for the analysis of Twitter data in Arabic with the aim to detect government pandemic measures and public concerns during the COVID-19 pandemic. The tool is described in detail, including its architecture, five software components, and algorithms. Using the tool, we collect a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February 2020 to 1 June 2020. We detect 15 government pandemic measures and public concerns and six macro-concerns (economic sustainability, social sustainability, etc.), and formulate their information-structural, temporal, and spatio-temporal relationships. For example, we are able to detect the timewise progression of events from the public discussions on COVID-19 cases in mid-March to the first curfew on 22 March, financial loan incentives on 22 March, the increased quarantine discussions during March-April, the discussions on the reduced mobility levels from 24 March onwards, the blood donation shortfall late March onwards, the government's 9 billion SAR (Saudi Riyal) salary incentives on 3 April, lifting the ban on five daily prayers in mosques on 26 May, and finally the return to normal government measures on 29 May 2020. These findings show the effectiveness of the Twitter media in detecting important events, government measures, public concerns, and other information in both time and space with no earlier knowledge about them.
Collapse
|