Social‑media archives are frequently used to study resistance to climate action, yet lexical items, response counters and observed participants do not capture the same social process. We processed all files in four publicly available Twitter and Reddit archives without sampling, applying transparent non‑exclusive lexical rules. The analysis links frame co‑occurrence to source‑specific temporal and response models, then separates changes due to returning authors from those caused by participant turnover. On Reddit, renewable‑energy terms often appear together with cost‑related language, while narrower backlash phrases sharply reduce cross‑platform differences and even reverse the sign of the Paris Agreement contrast in submissions. Cross‑discourse history does not improve primary‑context forecasts. Denial or hoax terms receive higher likes on Twitter, whereas Reddit response associations depend on frame, outcome and author specification. Around the 2019 global climate strike, both returning‑author expression and newcomer turnover contributed to a rise in protest‑language shares. An archive endpoint blocks the corresponding Climate Twitter migration inference. Most crossed‑cluster estimates lack confidence intervals, and joint author‑month response covariance estimates fail, limiting formal inference. These findings demonstrate that operational definitions, platform‑specific response fields and observation boundaries shape what can be claimed about climate backlash.
Review