Estimating Telecommuting Rates in the US Using Twitter Sentiment Analysis

Abstract

The COVID-19 pandemic had a significant impact on virtually every human activity. Millions of workers around the globe from eligible professions stayed at home working as part of the measures taken to contain the virus’ spread. The change in transportation demand associated to this phenomenon poses a challenge for cities, especially regarding public transportation, where the decrease in demand arose critical questions on how to assess decreased ridership and potential rebound effects. With this in mind, we ask: can we obtain real-time demand change estimates using social media data? Hence, the aim of this work is to take social media unstructured information and transform it into structured insights that can offer almost real-time estimates on demand trends associated with telecommuting. To achieve this, we obtained around 50,000 geo-tagged tweets relevant to telecommuting in the US. With that, we leveraged transformers Machine Learning methods to fine-tune a language model capable of automatically assigning a sentiment to tweets on this topic. We used the time evolution of the obtained sentiments as covariates in time series forecasting models to estimate telecommuting rates at both the national and state levels, observing a drastic improvement over the estimates without such covariates. Our major finding indicates that it is possible to structure social media data in order to use it to obtain demand change estimates, and that the accuracy of such estimates is going to depend heavily on how much people discuss the topic in question in a determined geography. This finding is in line with others that have found alternative ways of obtaining insights on transportation data, and hence, is a relevant contribution towards real-time data-driven approaches for transportation demand assessment.