Attempts at Enhancing eVision’s Influenza Forecasting Using Social Media

Navid Shaghaghi, Yash Kamdar, Ron Huang, A. Calle, Jaidev Mirchandani, Michael Castillo
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引用次数: 1

Abstract

Prediction of the spread of infectious diseases such as the seasonal Influenza is of utmost importance in the preparation for and mitigation of the severity of their impact. eVision (short for Epidemic Vision) is a machine learning time series forecaster under research and development by Santa Clara University’s EPIC (Ethical, Pragmatic, and Intelligent Computing) and BioInnovation & Design laboratories. Since eVision’s Long Short-Term Memory (LSTM) neural network makes use of Influenza related keywords in Google Trends as prediction features, it stands to reason that further feature selection from trending keywords relating to the flu in social media posts could enhance its prediction. After close examination, the only social media platforms that prove capable of supplying relevant data for time series analysis are the Twitter micro-blogging and Reddit social news aggregation and discussion forum platforms; as other social media platforms are either meant for sharing images and videos, or private multi-cast communication rather than public broadcasting and discourse. However, due to the burstiness of flu related Reddit posts, no useful feature for time series forecasting can be extracted from that platform; and Twitter, which has been examined for Influenza forecasting by numerous other researchers with successful results, poses a number of obstacles such as changes in policy as well as placing features behind expensive paywalls through the disabling of existing free APIs. Regardless however, the results obtained by the addition of Twitter data as another feature in eVision’s LSTM resulted in an almost negligible predictive improvement as delineated in this paper.
利用社交媒体增强eVision流感预测的尝试
对季节性流感等传染病的传播进行预测,对于防备和减轻其严重影响至关重要。evvision(流行病视觉的缩写)是由圣克拉拉大学EPIC(伦理、实用和智能计算)和生物创新与设计实验室研发的机器学习时间序列预测器。由于eVision的长短期记忆(LSTM)神经网络利用谷歌趋势中与流感相关的关键词作为预测功能,因此从社交媒体帖子中与流感相关的趋势关键词中进一步选择特征可以增强其预测功能。经过仔细研究,能够提供相关数据进行时间序列分析的社交媒体平台只有Twitter微博和Reddit社交新闻聚合论坛平台;因为其他社交媒体平台要么是为了分享图片和视频,要么是私人多播通信,而不是公共广播和话语。然而,由于Reddit上与流感相关的帖子层出不穷,无法从该平台提取出有用的时间序列预测功能;Twitter已经被许多其他研究人员用于流感预测,并取得了成功的结果,但它提出了许多障碍,比如政策的变化,以及通过禁用现有的免费api将功能置于昂贵的付费墙之后。然而,无论如何,通过在eVision的LSTM中添加Twitter数据作为另一个特征所获得的结果导致了本文所描述的几乎可以忽略不计的预测改进。
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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