  Social media platforms hold valuable insights, yet extracting essential
information can be challenging. Traditional top-down approaches often struggle
to capture critical signals in rapidly changing events. As global events evolve
swiftly, social media narratives, including instances of disinformation, become
significant sources of insights. To address the need for an inductive strategy,
we explore a niche social media platform GAB and an established messaging
service Telegram, to develop methodologies applicable on a broader scale. This
study investigates narrative evolution on these platforms using quantitative
corpus-based discourse analysis techniques. Our approach is a novel mode to
study multiple social media domains to distil key information which may be
obscured otherwise, allowing for useful and actionable insights. The paper
details the technical and methodological aspects of gathering and preprocessing
GAB and Telegram data for a keyness (Log Ratio) metric analysis, identifying
crucial nouns and verbs for deeper exploration. Empirically, this approach is
applied to a case study of a well defined event that had global impact: the
2023 Wagner mutiny. The main findings are: (1) the time line can be
deconstructed to provide useful data features allowing for improved
interpretation; (2) a methodology is applied which provides a basis for
generalization. The key contribution is an approach, that in some cases,
provides the ability to capture the dynamic narrative shifts over time with
elevated confidence. The approach can augment near-real-time assessment of key
social movements, allowing for informed governance choices. This research is
important because it lays out a useful methodology for time series relevant
info-culling, which can enable proactive modes for positive social engagement.
