寨卡病毒是什么？关于症状、治疗、传播和预防的探索性研究
What Are People Tweeting about Zika? An Exploratory Study Concerning Symptoms, Treatment, Transmission, and Prevention
摘要：
这项研究的目的是做一个数据集分布分析，分类性能分析，以及关于人们在推特上谈论四个疾病特征的主题分析：症状，传播，预防和治疗。结合使用自然语言处理和机器学习技术来确定人们对寨卡的推文。具体来说，建立了一个两阶段分类器系统，以找到与寨卡病毒相关的推文，然后将其分类为四种疾病类别。然后使用潜在狄利克雷分配（LDA）检查每个疾病类别中的推文，以确定每个疾病特征的五个主要推文主题。结果收集了1，234，605条推文。男性和女性的推文相似（分别为28%和23%）。分类器在训练和测试数据的相关性（分别为F=0.87和0.99）和疾病特征（分别为F=0.79和0.90）上表现良好。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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
