Curating Climate Change: Information Flows Across and Within Social Media Platforms

This dissertation explores how climate change information spreads on X (formerly Twitter) and what content from YouTube travels to X, focusing on climate change related tweets posted during November 2022. Using content analysis, machine learning, and case studies, about 114,000 tweets and their embedded YouTube videos were analyzed. The findings show that not all climate-related content is equally shared, some information is more likely to spread than others. The findings also indicate that users with a moderate number of followers are the ones connecting YouTube and X, rather than those with the highest follower counts on X. The findings are interpreted through the frameworks of curation logics and network gatekeeping. The implications of this dissertation call for recognizing that both user choices and platform algorithms play a role in shaping which climate messages receive the most attention. This dissertation contributes to the limited research on cross-platform climate communication and provides ideas for improving how climate information is shared online.