Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A
Cross-Country Comparative Study
- URL: http://arxiv.org/abs/2101.07695v1
- Date: Tue, 19 Jan 2021 15:51:53 GMT
- Title: Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A
Cross-Country Comparative Study
- Authors: Tiziana Carpi, Airo Hino, Stefano Maria Iacus, Giuseppe Porro
- Abstract summary: This study analyzes the impact of the COVID-19 pandemic on the subjective well-being as measured through Twitter data indicators for Japan and Italy.
Overall, the subjective well-being dropped by 11.7% for Italy and 8.3% for Japan in the first nine months of 2020 compared to the last two months of 2019.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study analyzes the impact of the COVID-19 pandemic on the subjective
well-being as measured through Twitter data indicators for Japan and Italy. It
turns out that, overall, the subjective well-being dropped by 11.7% for Italy
and 8.3% for Japan in the first nine months of 2020 compared to the last two
months of 2019 and even more compared to the historical mean of the indexes.
Through a data science approach we try to identify the possible causes of this
drop down by considering several explanatory variables including, climate and
air quality data, number of COVID-19 cases and deaths, Facebook Covid and flu
symptoms global survey, Google Trends data and coronavirus-related searches,
Google mobility data, policy intervention measures, economic variables and
their Google Trends proxies, as well as health and stress proxy variables based
on big data. We show that a simple static regression model is not able to
capture the complexity of well-being and therefore we propose a dynamic elastic
net approach to show how different group of factors may impact the well-being
in different periods, even over a short time length, and showing further
country-specific aspects. Finally, a structural equation modeling analysis
tries to address the causal relationships among the COVID-19 factors and
subjective well-being showing that, overall, prolonged mobility
restrictions,flu and Covid-like symptoms, economic uncertainty, social
distancing and news about the pandemic have negative effects on the subjective
well-being.
Related papers
- Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable.
Existing susceptibility studies heavily rely on self-reported beliefs.
We propose a computational approach to model users' latent susceptibility levels.
arXiv Detail & Related papers (2023-11-16T07:22:56Z) - Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths.
The pandemic has impacted and even changed human behavior in almost every aspect.
Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
arXiv Detail & Related papers (2023-03-23T17:19:26Z) - Causal Analysis and Prediction of Human Mobility in the U.S. during the
COVID-19 Pandemic [0.0]
Since the increasing outspread of COVID-19 in the U.S., most states have enforced travel restrictions resulting in sharp reductions in mobility.
This study develops an analytical framework that determines and analyzes the most dominant factors impacting human mobility and travel in the U.S. during this pandemic.
arXiv Detail & Related papers (2021-11-24T05:15:12Z) - #StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on
Spatial-temporal Dynamic Graphs [23.67939019353524]
COVID-19 has caused lasting damage to almost every domain in public health, society, and economy.
Existing studies rely on the aggregation of traditional statistical models and epidemic spread theory.
We propose a novel framework, Social Media enhAnced pandemic knowledge based on the extracted events and relationships.
arXiv Detail & Related papers (2021-08-08T15:46:05Z) - COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms [12.864257751458712]
We use self-reported symptoms survey data to understand trends in the spread of COVID-19.
From our studies, we try to predict the likely % of the population that tested positive for COVID-19 based on self-reported symptoms.
We forecast that % of the population having COVID-19-like illness (CLI) and those tested positive as 0.15% and 1.14% absolute error respectively.
arXiv Detail & Related papers (2020-12-21T00:37:24Z) - Steering a Historical Disease Forecasting Model Under a Pandemic: Case
of Flu and COVID-19 [75.99038202534628]
We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist.
Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
arXiv Detail & Related papers (2020-09-23T22:35:43Z) - A Data-driven Understanding of COVID-19 Dynamics Using Sequential
Genetic Algorithm Based Probabilistic Cellular Automata [4.36572039512405]
This study proposes that for an accurate data-driven modeling of this infection spread, cellular automata provides an excellent platform.
Elaborate analyses for COVID-19 statistics of forty countries from different continents have been performed.
The substantial predictive power of this model has been established with conclusions on the key players in this pandemic dynamics.
arXiv Detail & Related papers (2020-08-27T09:53:21Z) - The Past, Present, and Future of COVID-19: A Data-Driven Perspective [4.373183416616983]
We report results on our development and deployment of a web-based integrated real-time operational dashboard as an important decision support system for COVID-19.
We conducted data-driven analysis based on available data from diverse authenticated sources to predict upcoming consequences of the pandemic.
We also explored correlations between pandemic spread and important socio-economic and environmental factors.
arXiv Detail & Related papers (2020-08-12T19:03:57Z) - Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US.
We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
arXiv Detail & Related papers (2020-06-23T03:36:19Z) - Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries.
Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
arXiv Detail & Related papers (2020-06-05T02:04:25Z) - When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and
Policy Assessment using Compartmental Gaussian Processes [111.69190108272133]
coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures.
Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential.
This paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context.
arXiv Detail & Related papers (2020-05-13T18:21:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.