It 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.