{"id":374966,"date":"2017-03-29T11:28:15","date_gmt":"2017-03-29T18:28:15","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=374966"},"modified":"2018-10-16T21:57:18","modified_gmt":"2018-10-17T04:57:18","slug":"psychology-job-loss-using-social-media-data-characterize-predict-unemployment","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/psychology-job-loss-using-social-media-data-characterize-predict-unemployment\/","title":{"rendered":"The Psychology of Job Loss: Using Social Media Data to Characterize and Predict Unemployment"},"content":{"rendered":"<p align=\"LEFT\">Using data from social media, we study the relationship between the macroeconomic shock of employment instability and psychological well-being. We analyze more than 1.2B Twitter posts from over 230,000 U.S. users who either lost or gained a new job over a period spanning five years, from 2010 to 2015. First we quantify the magnitude and length of effects of\u00a0 job loss\/gain on\u00a0psychological variables such as anxiety, sadness, and anger. We then define a behavioral macroeconomic model that leverages these changes in psychological state to predict levels of unemployment in the U.S. Our results show that our psychological well-being measures are leading indicators, predicting economic indices weeks in advance with higher accuracy than baseline models. Taken together, these findings suggest that by capturing the human experience of a shock like job loss, social media data can augment current economic models to generate a better understanding of the overall causes and consequences of macroeconomic performance.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Using data from social media, we study the relationship between the macroeconomic shock of employment instability and psychological well-being. We analyze more than 1.2B Twitter posts from over 230,000 U.S. users who either lost or gained a new job over a period spanning five years, from 2010 to 2015. First we quantify the magnitude and 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