International Journal of Computational Linguistics Research Pit Pichappan 17 2 2026 https://doi.org/10.6025/ijclr/2026/17/2/59-77 https://www.dline.info/jcl/fulltext/v17n2/jclv17n2_1.pdf The rapid proliferation of online misinformation poses a critical challenge to digital information ecosystems, driven by coordinated communities and dynamic network interactions. While existing detection models often treat social networks as static or focus narrowly on content, this study investigates the complex interplay between temporal bursts, structural persistence, and community dynamics. Utilizing a novel Misinformation Dynamics Testbed, we analyze a longitudinal Reddit dataset (2015–2024, n=1,455) across multiple analytical dimensions. Statistical and network analyses reveal that misinformation engagement follows a heavily skewed, heavy-tailed distribution (Gini  0.71), with a pronounced visibility credibility mismatch (Spearman’s p  0.32). Temporal patterns are non stationary and episodic, characterized by event driven activity surges rather than linear trends. Structurally, the network exhibits scale free properties and densification driven growth, in which stable core communities show highly volatile interconnections. Cross community analysis uncovers a hybrid propagation mechanism: localized amplification within tightly knit echo chambers, coupled with controlled diffusion through strategic bridge nodes. These findings demonstrate that misinformation operates as a self reinforcing, temporally adaptive process rather than a purely contentdriven phenomenon. Consequently, effective mitigation requires a paradigm shift from static, post-level moderation to dynamic, system level interventions. Prioritizing high centrality hubs, monitoring bridge nodes, and deploying real time burst detection are essential for disrupting propagation pathways. This research underscores the necessity of integrated, network aware frameworks to address the evolving landscape of digital misinformation.