arXiv:2305.09620v4 Announce Type: replace-cross Abstract: Nationally representative surveys track public opinion, yet they ask only a limited set of questions each year, limiting its potential to capture historical changes. To fill this gap, we develop a large language model (LLM)-based framework for predicting missing responses in repeated cross-sectional surveys by incorporating embeddings for questions, respondents, and survey periods. We introduce two new applications of LLMs to survey research: retrodiction (predicting year-level missing opinions) and unasked opinion prediction (predictin
The increasing sophistication and accessibility of large language models (LLMs) are enabling novel applications in data analysis and social science research, pushing the boundaries of what's possible in predicting human behavior at scale.
This development allows for more granular and historical tracking of public sentiment without the logistical limitations of traditional surveys, offering richer insights for policy, commerce, and social analysis.
The ability to 'retrodict' and predict unasked opinions using AI changes how we understand historical public opinion and anticipate future trends, potentially making traditional survey methods more efficient and comprehensive.
- · Social science researchers
- · Market research firms
- · Political strategists
- · AI/ML developers
- · Traditional polling organizations (if they don't adapt)
- · Small-scale survey initiatives
- · Analysis reliant solely on limited historical survey data
Researchers can now infer public opinion on questions that were never explicitly asked, filling historical data gaps.
This improved understanding of public sentiment can lead to more responsive policy-making and targeted commercial strategies.
The application of AI to infer nuanced public opinion could raise new ethical questions regarding data privacy, bias in models, and the potential for manipulation if misused.
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