Narratives of Distrust: Climate Change, Disinformation, and the Dynamics of Right-Wing Media

This study examines climate change narratives within the Midnight Rider Telegram channel, an alternative media platform characterized by minimal content moderation and high susceptibility to disinformation. Using inductive thematic analysis, we identify four interrelated narrative themes: Faultfinding, Disinformation, Elitism, and Appeal to Emotion. This channel was selected for analysis due to data availability around the COP period, the number of subscribers, and its association with right-wing followers. Our research focuses on how these narratives were constructed in this media Telegram channel between November 2, 2023, and January 10, 2024, around the time of COP28. We filtered messages using specific keywords relevant to climate change. For our analysis, we decided to focus exclusively on channel messages and short videos (shorter than 10 minutes) and to exclude images, due to the low number of images and their minimal text content. We transcribed the videos using the AI-driven video editor Descript (https://www.descript.com/) and merged the transcribed video content and the text messages into one document in chronological order. Thematic analysis was conducted using MAXQDA, a qualitative data analysis software, to identify patterns and themes in the collected data. The process involved familiarizing ourselves with the data, generating initial codes, searching for themes, reviewing and refining these themes, and producing a report on the findings. We ultimately identified nine codes, which were then grouped into four overarching themes.