Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data

Anbar Aizenman (), Connor M. Brennan, Tomaz Cajner, Cynthia Doniger and Jacob Williams

No 2023-035, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: We present an indicator of job loss derived from Twitter data, based on a fine-tuned neural network with transfer learning to classify if a tweet is job-loss related or not. We show that our Twitter-based measure of job loss is well-correlated with and predictive of other measures of unemployment available in the official statistics and with the added benefits of real-time availability and daily frequency. These findings are especially strong for the period of the Pandemic Recession, when our Twitter indicator continues to track job loss well but where other real-time measures like unemployment insurance claims provided an imperfect signal of job loss. Additionally, we find that our Twitter job loss indicator provides incremental information in predicting official unemployment flows in a given month beyond what weekly unemployment insurance claims offer.

Keywords: Job Loss; Natural Language Processing; Neural Networks (search for similar items in EconPapers)
JEL-codes: J63 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2023-05-23
New Economics Papers: this item is included in nep-big
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2023-35

DOI: 10.17016/FEDS.2023.035

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