Comparing BERT Sentence-Pair Classification and Few-Shot LLM Prompting for Detecting Threat and Solution Framing in German Climate News
arXiv:2606.26489v1 Announce Type: new Abstract: News media play a central role in shaping public perceptions of climate change, and whether coverage emphasizes threats or solutions has measurable effects on audience engagement and policy support. Automated detection of these framing patterns at the sentence level would allow researchers to analyze large corpora that are infeasible to code manually. We present a systematic comparison of two approaches for classifying sentences from German-language climate news articles as threat-oriented, solution-oriented, both, or neither. The first approach
The proliferation of advanced AI models like BERT and LLMs allows for more sophisticated and automated analysis of large text corpora, making such research feasible and timely.
This research provides tools to quantify how climate change is framed in media, offering insights into public perception and policy support, which is critical for climate communication strategies.
Researchers can now more efficiently analyze vast amounts of news data to detect framing patterns, moving beyond manual coding and enabling large-scale, data-driven studies on climate narratives.
- · Climate scientists and researchers
- · Media analysis firms
- · Advocacy groups
- · AI/NLP developers
- · Manual data coders
- · Organizations relying on anecdotal media analysis
Automated detection of threat and solution framing in climate news becomes more widespread.
Improved understanding of how media framing influences public opinion and policy engagement regarding climate change.
More targeted and effective climate communication strategies developed leveraging real-time media analysis insights.
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Read at arXiv cs.CL