Tweet Analysis for Enhancement of COVID-19 Epidemic Simulation: A Case Study in Japan

Tran, Vu; Matsui, Tomoko

doi:10.3389/fpubh.2022.806813

Cited by 6 publications

(6 citation statements)

References 48 publications

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“…Information spontaneously provided by users on social media could be used to complement pharmacovigilance activities (50) and syndromic surveillance activities (51). Twitter reactions have been found to be correlated with COVID-19 epidemic waves in Japan (52).…”

Section: Discussionmentioning

confidence: 99%

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model

Cheatham

Kummervold

Parisi

et al. 2022

Front. Public Health

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Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy.

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Section: Discussionmentioning

confidence: 99%

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model

Cheatham

Kummervold

Parisi

et al. 2022

Front. Public Health

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“…Figure 2 reveals a repetitive phenomenon: the reactions on Twitter form a wave shape corresponding to each wave of COVID-19. Tran and Matsui ( 27 ) hypothesized such a phenomenon on the basis of behavioral changes observed from Apple mobility trends reports. This phenomenon is also potentially due to excessive negative information exposure ( 13 , 14 ) and heightened risk perception of social media users.…”

Section: Methodsmentioning

confidence: 99%

“…Chew et al ( 26 ) mentioned the use of Twitter data as a source of emotional responses toward COVID-19, but they did not perform emotion analysis on the data. Tran and Matsui ( 27 ) considered the tweet count of emoji-using tweets but did not perform a breakdown analysis of each emoji.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 case prediction using emotion trends via Twitter emoji analysis: A case study in Japan

Tran

Matsui

2023

Front. Public Health

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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.

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“…To our best knowledge, previous research has only attempted to integrate basic indicators or indices, overlooking the dynamic, intricate information contained in user-generated content 8 , 12 , 31 . Incorporating these valuable signals into neural networks remains a challenge but has the potential to provide a more comprehensive view of the pandemic and its potential impacts 1 , 9 , 32 .…”

Section: Related Workmentioning

confidence: 99%

MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Tran,

Hy,

Jiang

et al. 2024

Sci Rep

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Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework, MGLEP, that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators.

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Tweet Analysis for Enhancement of COVID-19 Epidemic Simulation: A Case Study in Japan

Cited by 6 publications

References 48 publications

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model

COVID-19 case prediction using emotion trends via Twitter emoji analysis: A case study in Japan

MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

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