To improve climate communication, it is crucial to understand how different audiences respond to pro-climate messages, particularly in information environments where accurate information competes with climate-contrarian disinformation. Here, we analyze responses to real-world climate change information and their downstream consequences for climate change concern and information search behavior. We gathered a diverse set of real-world pro-climate messages from the social media platform Twitter/X. These pro-climate messages were evaluated on several dimensions by a representative sample of the UK population (N = 503), and collective evaluations showed substantial variance. Message evaluations from conservative and liberal participants showed substantial covariation, suggesting that both political subgroups agreed about the relative ranking of the messages. This was despite robust main effects for various individual difference measures, including political orientation, on message evaluations as demonstrated by specification curve analysis. Coupling the data with results from a large cross-cultural experiment (N = 2226), showed that crowd-based message evaluations shared strong commonalities across nations. These evaluations, in turn, predicted shifts in climate concern and climate information search behavior, with more positively evaluated messages decreasing the likelihood of subsequent engagement with climate-contrarian disinformation. Alongside the empirical data, we release the ClimateChirp database containing validated pro-climate stimuli for future climate communication research. Our results suggest that despite individual differences in responses to climate communication, crowd-based message evaluations capture an inherent message effectiveness that generalizes across audience segments and nations.

This database provides all messages from the ClimateChirp database. It also features metrics provided by (the former) Twitter researcher API, empirical reference values from crowd-sourced message ratings and a content-based classification of all messages based on a taxonomy for pro-climate messages.