一项研究:在德里使用社交媒体分析基孔肯雅热

Shivani Jain, Alankrita Aggarwal, Sandeep Mittal
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引用次数: 0

摘要

基孔肯雅热是一种难以治疗的感染,2016年在德里造成了损失。在这种情况下,检测和预防媒介传播疾病在德里的爆发一直是政府关注的主要原因。为了分析这种流行病的爆发,作者利用了通过Twitter生成的非结构化数据。推特是一个社交媒体平台,每天都会产生大量与疫情相关的信息。该信息用于分析德里地区疫情爆发的影响。在本文中,作者讨论了用于分析和挖掘社交媒体信息的各种机器学习技术的相关研究。在这方面,作者还对社交媒体文本数据所涉及的步骤进行了分类和探索,以提供正在进行的疫情的图像视图。最后,文章讨论了挖掘社交媒体数据所面临的挑战。
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A Study: Chikungunya Using Social Media Analytics in Delhi
Chikungunya, an infection which is difficult to treat, took a toll on Delhi in year 2016. In that scenario, detection and prevention of vector-borne diseases outbreak in Delhi have been a major cause of concern for government. For analyzing this epidemic outbreak, the authors have utilized the unstructured data generated through Twitter. Twitter is a social media platform that generates vast amount of epidemic-related information every day. This information is used to analyze the effect of epidemic outbreak in Delhi region. In this paper, the authors discussed an associated study of various machine learning techniques for analyzing and mining social media information. In this, the authors have also categorized and explore the steps involved in social media textual data to provide a pictorial view of the ongoing outbreak. Finally, the article discussed the challenges faced for mining social media data.
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