新闻情绪可预测美国国内迁移，准确率超90%。
News Sentiment as a Predictor for American Domestic Migration
- 用《纽约时报》情绪得分训练神经网络预测迁徙
- 模型误差仅±900人，显著高于随机水平
- 适合政策制定者与商业选址参考
本文深入研究了美国全国性报纸新闻情绪对国内跨州迁移趋势的影响。基于2010至2020年《纽约时报》数据，计算平均情绪分数，并输入神经网络模型。随后采用逻辑回归模型预测跨州迁移，结果显示模型平均误差仅为±900人。由于模型输入未包含任何迁移数据，其预测结果完全依赖新闻情绪信息，表明新闻情绪可独立作为迁移行为的预测因子。该发现具有重要意义，说明媒体影响力可被用于预判人口流动，有助于政府和企业更精准理解人们迁移的动因。
原文摘要 · Abstract (English)
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.
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