News Sentiment as a Predictor for American Domestic Migration

TL;DR

This study demonstrates that US news sentiment from national newspapers can effectively predict interstate migration trends in the US, offering a new tool for policymakers and businesses to understand migration drivers.

Contribution

It introduces a neural network and logistic regression approach to predict migration based solely on news sentiment data, highlighting the press's influence on migration patterns.

Findings

Model achieved high accuracy with a margin of error of +/- 900 citizens.

Sentiment data alone can predict migration trends without exposure to migration data.

Press sentiment significantly correlates with interstate migration patterns.

Abstract

This paper goes into depth on the effect that US News Sentiment from national newspapers has on US interstate migration trends. Through harnessing data from the New York Times between 2010 and 2020, an average sentiment score was calculated, allowing for data to be entered into a neural network. Then a logistic regression model was used to predict interstate migration. The results indicate the model was highly accurate as the mean margin of error was +/- 900 citizens. The predictions from the model were compared with the US Census data from 2010 to 2020 that was used to train the model. Since the input for the model was not exposed to any migration data, the model clearly demonstrated that its results were drawn from sentiment data alone. These findings are significant as they indicate that the role of the press could be used as a predictor for domestic migration which can help the government and businesses understand better what is influencing people to move to certain places.