Predicting Zika Prevention Techniques Discussed on Twitter: An Exploratory Study

Soumik Mandal, Manasa Rath, Yiwei Wang, Braja Gopal Patra
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引用次数: 3

Abstract

Social media platforms are widely seen as a valuable medium to spread a wide range of information including charitable causes and health awareness. But given the flexibility provided by the social media platforms, it is important to ensure that the right kind of information is delivered to the right audience when needed. The pilot study presented in this paper considered a sample of Zika related tweets that were classified into different prevention techniques. The classification categories were drawn from the guidelines by CDC. Training a logistic regression model on the annotated data we found the accuracy to be 72%. The findings are significant in studying the effectiveness of social media platforms in spreading the right kind of information in time. This in turn can be useful in informing health care officials to take necessary steps with the help of real-time communication for such unfortunate events in future.
推特上讨论的预测寨卡病毒预防技术:一项探索性研究
社交媒体平台被广泛视为传播各种信息的宝贵媒介,包括慈善事业和健康意识。但考虑到社交媒体平台提供的灵活性,确保在需要的时候将正确的信息传递给正确的受众是很重要的。本文中提出的试点研究考虑了与寨卡病毒相关的推文样本,这些推文被分类为不同的预防技术。分类类别是根据疾病预防控制中心的指南绘制的。在标注的数据上训练逻辑回归模型,我们发现准确率为72%。这一发现对于研究社交媒体平台在及时传播正确信息方面的有效性具有重要意义。这反过来又有助于通知卫生保健官员在实时通信的帮助下采取必要的步骤,以便将来发生此类不幸事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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