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NeedFull——一个用于研究纽约州新冠疫情期间人类需求的推文分析平台。

NeedFull - a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State.

作者信息

Long Zijian, Alharthi Rajwa, Saddik Abdulmotaleb El

机构信息

Multimedia Communications Research LaboratoryUniversity of Ottawa Ottawa ON K1N 6N5 Canada.

Department of Computer ScienceTaif University Taif 26571 Saudi Arabia.

出版信息

IEEE Access. 2020 Jul 22;8:136046-136055. doi: 10.1109/ACCESS.2020.3011123. eCollection 2020.

DOI:10.1109/ACCESS.2020.3011123
PMID:34812341
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8545340/
Abstract

Governments and municipalities need to understand their citizens' psychological needs in critical times and dangerous situations. COVID-19 brings lots of challenges to deal with. We propose NeedFull, an interactive and scalable tweet analysis platform, to help governments and municipalities to understand residents' real psychological needs during those periods. The platform mainly consists of four parts: data collection module, data storage module, data analysis module and data visualization module. The four parts interact with each other and provide users with a thorough human needs analysis based on their queries. We employed the proposed platform to investigate the reaction of people in New York State to the ongoing worldwide COVID-19 pandemic.

摘要

政府和市政当局需要在关键时期和危险情况下了解公民的心理需求。新冠疫情带来了诸多挑战需要应对。我们提出了NeedFull，一个交互式且可扩展的推文分析平台，以帮助政府和市政当局了解居民在这些时期的真实心理需求。该平台主要由四个部分组成：数据收集模块、数据存储模块、数据分析模块和数据可视化模块。这四个部分相互协作，并根据用户的查询为其提供全面的人类需求分析。我们使用该平台来调查纽约州民众对全球范围内新冠疫情的反应。

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TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.

本文引用的文献

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Social Media Elements, Ecologies, and Effects.社交媒体元素、生态和影响。
Annu Rev Psychol. 2020 Jan 4;71:471-497. doi: 10.1146/annurev-psych-010419-050944. Epub 2019 Sep 13.
TClustVID：一种用于研究新冠疫情推文主题和情感的新型机器学习分类模型。
Knowl Based Syst. 2021 Aug 17;226:107126. doi: 10.1016/j.knosys.2021.107126. Epub 2021 May 6.