Covid-19 疫苗接种:全球社交媒体用户对政府沟通的态度分析

Ajay Kumar Singh, A. Tripathi, Priti Jagwani, Noopur Agrawal
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引用次数: 0

摘要

在全球大流行病肆虐的情况下,政府、卫生机构和行政当局面临的主要挑战是以最简单的形式和语言向公众传播和宣传从未听说过的发病率、病毒学和免疫学知识。然而,要做到这一点,他们必须能够适当预测公众对有关疾病、预防措施和采用用户感知既定原则的特定传播内容的看法和反应。本文研究了用户对 Covid-19 疫苗接种的看法。文章使用 Python 对社交媒体 Twitter 全球用户数据集进行了情感分析。该数据集可在 kaggle.com 上获取,由 2020 年 12 月至 2021 年 2 月期间的 51,393 条推文组成,具有超过 15 个特征。大多数人(60.8%)对疫苗接种持中立态度,23.9%的人持积极态度。此外,为了对上述分析进行评估,还进行了模型评估的机器学习流水线过程。这一过程包括将数据集分为训练和测试两部分,然后确定各种评估参数,包括混淆矩阵、精确度、召回率和 F1 分数。准确率为 97.1%,说明该模型性能优越。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Covid-19 Vaccination: An Attitude Analysis of Global Users of Social Media Towards Government Communication
Amidst a global pandemic, the key challenge before governments, health institutions and administrative authorities is to communicate and inform the general public about the never-heard of morbidity, virology and immunity in their simplest form and language. However, this can only be possible when they can appropriately predict the perceptions and reactions of public to a given set of communications regarding the disease, preventive measures and the adoption of established principles of users’ perceptions. This article is a study of the users’ perceptions about Covid-19 vaccination. It conducts sentiment analysis in Python on a dataset of global users of the social media channel Twitter. The dataset available at kaggle.com, comprising 51,393 tweets from December 2020 to February 2021 with more than fifteen features, was put to test. The majority of the people (60.8%) expressed their neutral sentiments towards vaccination, while 23.9% had a positive opinion. Further, in order to evaluate the aforementioned analysis, the machine learning pipeline process of model evaluation is also performed. This process includes a split of dataset into training and testing, followed by determining various evaluation parameters including confusion matrix, precision, recall and F1-score. The accuracy of 97.1% depicts the outperformance of the model.
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