International Journal of Computational Linguistics Research Hathairat Ketmaneechairat 17 2 2026 https://doi.org/10.6025/ijclr/2026/17/2/78-99 https://www.dline.info/jcl/fulltext/v17n2/jclv17n2_2.pdf The rapid proliferation of misleading information on social media poses significant challenges to digital ecosystems, driven by sensational narratives, emotional framing, and strategic engagement tactics. Existing research often examines linguistic patterns, user engagement, and propagation dynamics in isolation, limiting comprehensive understanding. This study introduces a unified analytical framework that integrates linguistic, behavioral, and contextual features to model and classify misleading versus authentic content on Reddit. Analyzing a dataset of 2,344 posts across twelve subreddits, we engineered 86 multidimensional features encompassing linguistic structure, sentiment, stylistic markers, clickbait indicators, and TF-IDF representations. A Linear Support Vector Machine (LinearSVC) was employed for multi-class classification across true, satire, imposter, and misleading categories. Descriptive and statistical analyses revealed that authentic content attracts the highest average engagement, while misleading posts exhibit significantly greater verbosity and clickbait prevalence, strategically mimicking credible narratives. The baseline classifier achieved 88.7% weighted accuracy, effectively distinguishing satire and imposter content, yet struggled with misleading posts due to their deliberate lexical overlap with authentic news. Inferential tests confirmed statistically significant differences in engagement metrics, content length, and clickbait usage across categories. These findings demonstrate that virality is decoupled from factual accuracy and highlight the limitations of traditional machine learning in detecting nuanced deception. Future research should incorporate transformer-based architectures, temporal engagement modeling, and network propagation analysis to enhance robust misinformation detection in dynamic online communities.