Misinformation Clusters and Virality Dynamics in Online Social Networks: A Topic Modeling and Epidemiological Analysis of Reddit Communities
Pit Pichappan
Journal of Information Technology Review, Vol. 17, No. 2, 2026.
DOI: https://doi.org/10.6025/jitr/2026/17/2/65-79
Link: https://www.dline.info/jitr/fulltext/v17n2/jitrv17n2_2.pdf
Abstract:
The proliferation of misinformation on social media poses significant societal risks, necessitating robust
analytical frameworks. This study presents a comprehensive analysis of misinformation clusters and virality
dynamics within Reddit communities using topic modeling and epidemiological diffusion models. Utilizing a
longitudinal dataset sourced from the Zenodo repository spanning 2012 to 2024, we employed TF-IDF
vectorization and K-means clustering to identify five dominant thematic groups, including Health, US Politics,
and General Discussions. Misinformation detection was enhanced with a fine-tuned DistilBERT model,
achieving 94-96% accuracy and surpassing traditional logistic regression baselines. Results indicate that
while General Discussions contain the highest absolute volume of misinformation, US Politics exhibits the
highest density, creating critical risk zones. Virality metrics reveal a credibility paradox: misleading content
achieves higher upvote ratios than factual news, suggesting stronger audience resonance despite lower
overall reach. Furthermore, an adapted SEIR (Susceptible-Exposed-Infected-Recovered) framework confirms
that information spread follows epidemic-like patterns, with political topics acting as infectious nodes and
general topics providing susceptible populations until saturation. Explainable AI techniques, specifically
LIME, validated model interpretability by highlighting linguistic markers such as "fake" and "hiding." These
findings underscore that misinformation is not randomly distributed but concentrated in specific thematic
clusters where high virality overlaps with high density. Strategic monitoring prioritizing these high-risk
clusters is essential for mitigating information epidemics and developing effective countermeasures against
the evolving landscape of online disinformation. Future research should incorporate temporal dynamics
into the model to predict viral spikes before saturation occurs.