Temporal Bursts and Structural Persistence in Online Misinformation Networks
Pit Pichappan
International Journal of Computational Linguistics Research, Vol. 17, No. 2, 2026.
DOI: https://doi.org/10.6025/ijclr/2026/17/2/59-77
Link: https://www.dline.info/jcl/fulltext/v17n2/jclv17n2_1.pdf
Abstract:
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 (2015â€“2024, n=1,455) across
multiple analytical dimensions. Statistical and network analyses reveal that misinformation engagement
follows a heavily skewed, heavy-tailed distribution (Gini ï‚» 0.71), with a pronounced visibility credibility
mismatch (Spearmanâ€™s p ï‚» 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.