Are Economists Open to AI? Text as Data as Survey on Professional Sentiment and Academic Research Trends
2026-06-01 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors created a method called TaDaS that turns large amounts of internet text into survey-like data by matching questions and answers based on their meaning. They used this to study how economists discuss AI by linking forum posts with research publications. Their method measured professional attitudes like openness and negativity and found AI discussions were initially less open and more negative, but attitudes improved over time as AI gained attention in research. This approach helps analyze professional opinions without the costs and biases of traditional surveys.
semantic retrievalsurvey biastext miningprofessional sentimentAI in economicsdata linkingnatural language processingforum analysisresearch publicationsattitude scoring
Authors
Yi Wang, Lei Ge
Abstract
Traditional surveys are costly, hard to reconstruct retrospectively, and vulnerable to self-presentation bias. Raw internet text is abundant but noisy, weakly structured, and platform-selected. We introduce TaDaS (Text as Data as Survey), a framework that converts naturally occurring text into survey-like evidence by linking a question corpus to an answer corpus through cross-dataset semantic retrieval. TaDaS first screens a reference question corpus to construct focal and comparable semantic neighborhoods. It then maps unstructured observations from an answer corpus onto these neighborhoods and scores the attitudes expressed in the resulting discourse. We apply the framework to economists' reactions to AI by linking 1.3 million research-related posts from Economics Job Market Rumors with 53,585 elite economics and finance publications. Publication-side topics define the research frontier; forum-side replies reveal professional sentiment along six dimensions: openness, negativity, toxicity, arrogance, curiosity, and confusion. AI-related discussion is less open and more negative in cross-section, but the interaction evidence points in a favorable direction on all six dimensions as AI becomes more visible in elite journals. The findings show how TaDaS can recover scalable, retrospective, and non-reactive measures of professional sentiment from existing text archives.