A Person-Centric Prediction Model of Job Loss based on Social Media

更好地了解除了公司的财务压力之外，还有哪些因素可以预测失业，这符合商业和公共政策的利益。这项研究将应用大数据挖掘技术，社交网络和Twitter数据的统计分析，以确定预测失业的个人因素，而不仅仅是公司的业务和经济状况。这个话题对于管理和组织科学学科来说很重要，因为我们对失业的了解大多是在社会和公司层面上（比如工厂关闭）。该项目将有助于更好地了解工作性质的变化是如何影响人们的。研究人员正在采取一种基于混合大数据理论的方法，使用来自流行社交媒体Twitter的信息。设计了两项研究：（1）利用语言学和情感分析从微观、中观和宏观角度识别Twitter数据中的失业预测因子;（2）分析Twitter用户的工作主题网络，推断网络指数作为失业预测因子。在研究（1）和（2）中确定的预测因素集将被单独使用，并一起作为一系列推理模型的输入，这些模型将衡量每个信息域解释的失业方差的大小。该项目将通过展示如何将大数据挖掘与统计分析结合使用来扩展基本的方法论知识，以从推文和社交媒体网络的内容中识别失业的多个预测因素。

AbstractIt is in the interests of business and public policy to better understand what factors predict job loss beyond a company's financial stress. This research will apply big data mining techniques, and social network and statistical analyses of Twitter data to identify personal factors that predict job loss, over and above a company's business and economic conditions. This topic is important for management and organization science disciplines because most of what we know about job loss is at the society and company level (such as plant closings). The project will lead to a better understanding of how the changing nature of work is affecting people.The researchers are taking a hybrid big data theory-based approach, using information derived from the popular social media Twitter. Two studies are devised: (1) Identify job loss predictors from micro, meso and macro perspectives in Twitter data by exploiting linguistic and sentiment analysis; (2) Analyze the job topic networks of Twitter users to infer network indices as predictors of job loss. The sets of predictors identified in studies (1) and (2) will be used independently and together as input to a cascade of inference models that will gauge the amount of variance in job loss explained by each of the information domains. This project will extend fundamental methodological knowledge by demonstrating how big data mining might be used in conjunction with statistical analysis to identify multiple predictors of job loss from the content of tweets and social media networks.