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Tran KT, Hy TS, Jiang L, Vu XS. MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data. Sci Rep 2024; 14:16377. [PMID: 39013976 PMCID: PMC11252387 DOI: 10.1038/s41598-024-67146-y] [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: 11/03/2023] [Accepted: 07/08/2024] [Indexed: 07/18/2024] Open
Abstract
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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Affiliation(s)
- Khanh-Tung Tran
- Department of Computing Science, Umeå University, Umeå, Sweden
- AI Center, FPT Software, Hanoi, Vietnam
| | - Truong Son Hy
- Department of Mathematics and Computer Science, Indiana State University, Terre Haute, USA
| | - Lili Jiang
- Department of Computing Science, Umeå University, Umeå, Sweden
| | - Xuan-Son Vu
- Department of Computing Science, Umeå University, Umeå, Sweden.
- DeepTensor AB, Umeå, Sweden.
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Theocharopoulos PC, Tsoukala A, Georgakopoulos SV, Tasoulis SK, Plagianakos VP. Analysing sentiment change detection of Covid-19 tweets. Neural Comput Appl 2023; 35:1-11. [PMID: 37362564 PMCID: PMC10230484 DOI: 10.1007/s00521-023-08662-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.
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Affiliation(s)
| | - Anastasia Tsoukala
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | | | - Sotiris K. Tasoulis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Vassilis P. Plagianakos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
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Tran V, Matsui T. COVID-19 case prediction using emotion trends via Twitter emoji analysis: A case study in Japan. Front Public Health 2023; 11:1079315. [PMID: 36998279 PMCID: PMC10045477 DOI: 10.3389/fpubh.2023.1079315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
Abstract
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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Affiliation(s)
- Vu Tran
- Risk Analysis Research Center, The Institute of Statistical Mathematics, Tokyo, Japan
- *Correspondence: Vu Tran
| | - Tomoko Matsui
- Risk Analysis Research Center, The Institute of Statistical Mathematics, Tokyo, Japan
- Department of Statistical Modeling, The Institute of Statistical Mathematics, Tokyo, Japan
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Cheatham S, Kummervold PE, Parisi L, Lanfranchi B, Croci I, Comunello F, Rota MC, Filia A, Tozzi AE, Rizzo C, Gesualdo F. Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model. Front Public Health 2022; 10:948880. [PMID: 35968436 PMCID: PMC9372360 DOI: 10.3389/fpubh.2022.948880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
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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Affiliation(s)
- Susan Cheatham
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | | | - Lorenza Parisi
- Department of Human Sciences, Link Campus University, Rome, Italy
| | - Barbara Lanfranchi
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Ileana Croci
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Francesca Comunello
- Department of Communication and Social Research, Sapienza University, Rome, Italy
| | - Maria Cristina Rota
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | - Antonietta Filia
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | - Alberto Eugenio Tozzi
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Caterina Rizzo
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
- Department of Translational Research and New Technologies in Medicine and Surgery, Pisa University, Pisa, Italy
- *Correspondence: Caterina Rizzo
| | - Francesco Gesualdo
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
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Tran V, Matsui T. Tweet Analysis for Enhancement of COVID-19 Epidemic Simulation: A Case Study in Japan. Front Public Health 2022; 10:806813. [PMID: 35433607 PMCID: PMC9008370 DOI: 10.3389/fpubh.2022.806813] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/10/2022] [Indexed: 12/31/2022] Open
Abstract
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.
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Affiliation(s)
- Vu Tran
- Risk Analysis Research Center, The Institute of Statistical Mathematics, Tokyo, Japan
| | - Tomoko Matsui
- Risk Analysis Research Center, The Institute of Statistical Mathematics, Tokyo, Japan.,Department of Statistical Modeling, The Institute of Statistical Mathematics, Tokyo, Japan
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