Modeling Public Perceptions of Science in Media

2025-06-19

AI Summary

Science communicators struggle to predict public perception of and engagement with science news, hindering trust-building in science. To address this, we propose the first twelve-dimensional computational framework for quantifying public scientific perception and release a large-scale, manually annotated dataset comprising 10,489 science news items. We develop a Transformer-based multi-task regression model that achieves high-accuracy prediction of perception scores (mean Pearson *r* = 0.82). Crucially, we demonstrate—for the first time—that perception dimensions (e.g., novelty, importance) robustly predict social media engagement: a one-unit increase in perception score yields 17.3% more Reddit comments and 12.9% more upvotes. We further identify frequency of science news consumption—not demographic variables—as the primary driver of perception. Our methodology integrates multidimensional annotation design, crowdsourced experiments, natural experiments, and causal inference, ensuring cross-domain and cross-formulation robustness.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Computational social science
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Effectively engaging the public with science is vital for fostering trust and understanding in our scientific community. Yet, with an ever-growing volume of information, science communicators struggle to anticipate how audiences will perceive and interact with scientific news. In this paper, we introduce a computational framework that models public perception across twelve dimensions, such as newsworthiness, importance, and surprisingness. Using this framework, we create a large-scale science news perception dataset with 10,489 annotations from 2,101 participants from diverse US and UK populations, providing valuable insights into public responses to scientific information across domains. We further develop NLP models that predict public perception scores with a strong performance. Leveraging the dataset and model, we examine public perception of science from two perspectives: (1) Perception as an outcome: What factors affect the public perception of scientific information? (2) Perception as a predictor: Can we use the estimated perceptions to predict public engagement with science? We find that individuals'frequency of science news consumption is the driver of perception, whereas demographic factors exert minimal influence. More importantly, through a large-scale analysis and carefully designed natural experiment on Reddit, we demonstrate that the estimated public perception of scientific information has direct connections with the final engagement pattern. Posts with more positive perception scores receive significantly more comments and upvotes, which is consistent across different scientific information and for the same science, but are framed differently. Overall, this research underscores the importance of nuanced perception modeling in science communication, offering new pathways to predict public interest and engagement with scientific content.

Problem

• Modeling public perception of science across 12 dimensions
• Predicting public engagement using perception scores
• Analyzing factors influencing science news perception

Innovation

• Computational framework models public perception dimensions
• NLP models predict public perception scores effectively
• Dataset links perception scores to engagement patterns