Summary
Measuring resistance to climate action on social media is tricky because different signs like words used, user reactions, and who is posting capture different things. The authors studied entire Twitter and Reddit archives related to climate topics, using clear rules to count how certain words appear and linked these to responses and user activity over time. They found that the platform and how the data is collected affect what counts as climate backlash, showing that simple counts of words or reactions don’t tell the whole story. Their work helps explain why different studies might see different patterns of opposition on social media.
What this means in practice
- •For social media analysts: Build better tools to measure climate opposition by accounting for platform-specific language and user activity patterns found in Twitter and Reddit archives.
- •For public policy advisors: Use nuanced measures of online climate backlash to improve understanding of public response dynamics around climate events and agreements.
Abstract
Social media archives are often used to study resistance to climate action, but words, response counters and observed participants do not measure the same social process. We examine four supplied Twitter and Reddit archives by processing all registered files without sampling and applying transparent, non-exclusive lexical rules. The study links frame co-occurrence to source-specific temporal and response models, then separates event-period changes among returning authors from participant turnover. Renewable-energy terms accompany cost-related language on Reddit, yet narrower backlash phrases sharply reduce cross-source contrasts and reverse the sign of the Paris Agreement contrast in submissions. Cross-discourse history does not improve eligible primary-context forecasts. Denial/hoax terms are associated with higher recorded Twitter likes, whereas Reddit response associations depend on frame, outcome and author specification. Around the 2019 global climate strike, returning-author expression and participant turnover both contribute to increased protest-language shares. An archive endpoint prevents the corresponding Climate Twitter migration inference. Most crossed-cluster estimates lack released intervals, and joint author/month response covariance estimates fail, restricting formal inference. These results show how operational definitions, platform-specific response fields and observation boundaries shape what can be claimed about climate backlash. The contribution is an archive-based account of these measurement consequences, rather than a measure of individual opposition, persuasion or advocacy-induced backlash.