In the face of world-changing events, narratives on the messaging platform Telegram, including in- stances of disinformation, tend to arise and evolve at high speeds. However, key signals of this process, including newly emerging or idiosyncratic concepts, often elude traditional, top-down analyses. Addressing the need for inductive approaches to narrative evolution on Telegram, this paper operationalizes quantitative methods from the field of corpus-based discourse analysis. On a technical and methodological level, the paper discusses how data from Telegram’s messages and images can be collected and preprocessed for the purposes of a ‘keyness’ (Log Ratio) analysis that surfaces salient nouns and verbs for further investigation. On an empirical level, this method is then applied to a case study of 225 predominantly Dutch-speaking Tele- gram channels (spanning the period March 2017- March 2022), revealing some of the dynamics that govern their recent shift from propagating narra- tives about the coronavirus pandemic to narratives concerning the war in Ukraine. This case study is accompanied by an interactive demonstrator that enables readers to further explore the processed dataset. The paper concludes with a reflection on the status of and future avenues for this ‘distant reading’ approach in relation to established interpretative practices.
