发帖
雷达卡
摘要翻译：
这项研究的目的是进行数据集分布分析、分类性能分析和关于人们在推特上发布的关于四种疾病特征的主题分析：症状、传播、预防和治疗。自然语言处理和机器学习技术的结合被用来确定人们在推特上发布的关于寨卡病毒的内容。具体来说，建立了一个两阶段分类器系统，以查找寨卡病毒的相关推文，然后将这些推文归类到四个疾病类别。然后使用潜在dirichlet分配(LDA)检查每个疾病类别的Tweets，以确定每个疾病特征的五个主要tweet主题。结果共收集推文1234605条。男性和女性的推文相似（分别为28%和23%）。该分类器对训练和检验数据的相关性(F=0.87和0.99）和疾病特征(F=0.79和0.90）均有较好的效果。每个类别的五个主题被发现和讨论，重点是症状类别。通过这个过程，我们展示了如何发现错误信息，以便公共卫生官员可以用错误信息回应推文。
---
英文标题：
《What Are People Tweeting about Zika? An Exploratory Study Concerning
Symptoms, Treatment, Transmission, and Prevention》
---
作者：
Michele Miller, Dr. Tanvi Banerjee, RoopTeja Muppalla, Dr. William
Romine, Dr. Amit Sheth
---
最新提交年份：
2017
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Social and Information Networks 社会和信息网络
分类描述：Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
涵盖社会和信息网络的设计、分析和建模，包括它们在联机信息访问、通信和交互方面的应用，以及它们作为数据集在这些领域和其他领域的问题探索中的作用，包括与社会和生物科学的联系。这类网络的分析和建模包括ACM学科类F.2、G.2、G.3、H.2和I.2的主题；计算应用包括H.3、H.4和H.5中的主题；计算和其他学科接口的应用程序包括J.1-J.7中的主题。关于计算机通信系统和网络协议（例如TCP/IP）的论文通常更适合网络和因特网体系结构（CS.NI）类别。
--
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
--
---
英文摘要：
The purpose of this study was to do a dataset distribution analysis, a classification performance analysis, and a topical analysis concerning what people are tweeting about four disease characteristics: symptoms, transmission, prevention, and treatment. A combination of natural language processing and machine learning techniques were used to determine what people are tweeting about Zika. Specifically, a two-stage classifier system was built to find relevant tweets on Zika, and then categorize these into the four disease categories. Tweets in each disease category were then examined using latent dirichlet allocation (LDA) to determine the five main tweet topics for each disease characteristic. Results 1,234,605 tweets were collected. Tweets by males and females were similar (28% and 23% respectively). The classifier performed well on the training and test data for relevancy (F=0.87 and 0.99 respectively) and disease characteristics (F=0.79 and 0.90 respectively). Five topics for each category were found and discussed with a focus on the symptoms category. Through this process, we demonstrate how misinformation can be discovered so that public health officials can respond to the tweets with misinformation.
---
PDF链接：
https://arxiv.org/pdf/1701.07490
京公网安备 11010802022788号
