COVID-19 Surveiller: toward a robust and effective pandemic surveillance system based on social media mining
| العنوان: | COVID-19 Surveiller: toward a robust and effective pandemic surveillance system based on social media mining |
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| المؤلفون: | Jule Ahmar, Xiusi Chen, Liqi Zhao, Yan-Ru Jhou, Sabrina Liu, Wei Wang, Jyun-Yu Jiang, Po-Chun Yang, Yichao Zhou |
| المصدر: | Philosophical transactions. Series A, Mathematical, physical, and engineering sciences Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, vol 380, iss 2214 |
| بيانات النشر: | The Royal Society, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Coronavirus disease 2019 (COVID-19), General Science & Technology, General Mathematics, Internet privacy, General Physics and Astronomy, Bioengineering, social media mining, Social media mining, Leverage (negotiation), Pandemic, Data Mining, Humans, Social media, Aetiology, pandemic surveillance, natural language processing, Pandemics, Research Articles, business.industry, SARS-CoV-2, Prevention, General Engineering, Outbreak, COVID-19, Articles, Good Health and Well Being, Knowledge graph, Networking and Information Technology R&D (NITRD), knowledge graph, Business, Social Media, 2.4 Surveillance and distribution |
| الوصف: | The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available athttp://scaiweb.cs.ucla.edu/covidsurveiller/.This article is part of the theme issue ‘Data science approachs to infectious disease surveillance’. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1471-2962 1364-503X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdb5d721b756f5c4194bcc2c60453fbc http://europepmc.org/articles/PMC8607148 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....fdb5d721b756f5c4194bcc2c60453fbc |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14712962 1364503X |
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