The COVID-19 pandemic, which began in December 2019, progressed in a complicated manner and thus caused problems worldwide. Seeking clues to the reasons for the complicated progression is necessary but challenging in the fight against the pandemic. We sought clues by investigating the relationship between reactions on social media and the COVID-19 epidemic in Japan. Twitter was selected as the social media platform for study because it has a large user base in Japan and because it quickly propagates short topic-focused messages (“tweets”). Analysis using Japanese Twitter data suggested that reactions on social media and the progression of the COVID-19 epidemic may have a close relationship. Analysis of the data for the past waves of COVID-19 in Japan revealed that the relevant reactions on Twitter and COVID-19 progression are related repetitive phenomena. We propose using observations of the reaction trend represented by tweet counts and the trend of COVID-19 epidemic progression in Japan and a deep neural network model to capture the relationship between social reactions and COVID-19 progression and to predict the future trend of COVID-19 progression. This trend prediction would then be used to set up a susceptible-exposed-infected-recovered model for simulating potential future COVID-19 cases. Experiments to evaluate the potential of using tweets to support the prediction of how an epidemic will progress demonstrated the value of using epidemic-related social media data. Our findings provide insights into the relationship between user reactions on social media, particularly Twitter, and epidemic progression, which can be used to fight pandemics.
IntroductionThe worldwide COVID-19 pandemic, which began in December 2019 and has lasted for almost 3 years now, has undergone many changes and has changed public perceptions and attitudes. Various systems for predicting the progression of the pandemic have been developed to help assess the risk of COVID-19 spreading. In a case study in Japan, we attempt to determine whether the trend of emotions toward COVID-19 expressed on social media, specifically Twitter, can be used to enhance COVID-19 case prediction system performance.MethodsWe use emoji as a proxy to shallowly capture the trend in emotion expression on Twitter. Two aspects of emoji are studied: the surface trend in emoji usage by using the tweet count and the structural interaction of emoji by using an anomalous score.ResultsOur experimental results show that utilizing emoji improved system performance in the majority of evaluations.
The COVID-19 pandemic, which began in December 2019, progressed in a complicated manner and thus caused problems worldwide. Seeking clues to the reasons for the complicated progression is necessary but challenging in the fight against the pandemic. We sought clues by investigating the relationship between reactions on social media and the COVID-19 epidemic in Japan. Twitter was selected as the social media platform for study because it has a large user base in Japan and because it quickly propagates short topic-focused messages ("tweets"). Analysis using Japanese Twitter data suggests that reactions on social media and the progression of the COVID-19 pandemic may have a close relationship. Experiments to evaluate the potential of using tweets to support the prediction of how an epidemic will progress demonstrated the value of using epidemic-related social media data. Our findings provide insights into the relationship between user reactions on social media, particularly Twitter, and epidemic progression, which can be used to fight pandemics.
Extreme weather events can arrive unannounced and cause immense harm for communities. Especially in cities where many people live in close proximity, events like flash flooding, windstorms or even heat waves can cause property damage, overworking of the emergency infrastructure and death. Unfortunately, because climate change continues to alter weather patterns, from subtle local variations to changes in global factors like ocean currents, these events are occurring with increased frequency. There is a great need for accurate monitoring and prediction systems that can help forecast these catastrophes. Monitoring overall changes in the patterns of these events will also help governments and citizens better adapt and plan measures to protect themselves from climate change's inevitable impact. Professor Tomoko Matsui is an expert in the field of statistical spatial-temporal modelling. Matsui is heading up an international team of researchers at the Institute of Statistical Mathematics in Tokyo, Heriot-Watt University in UK and the National Institute for Environmental Studies in Tsukuba to find ways for using a variety of data including low-resolution surface meteorological observation, time-series measurements of ground surface temperature by high-resolution satellite and social media.
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