Amid growing concerns about the proliferation and belief in false or misleading information, the study addresses the need for automated detection in the public domain. It revisits and replicates scattered findings using a comprehensive, content-oriented, and feature-based approach. This method reliably identifies deceptive news-like content and highlights the importance of individual features in guiding the prediction algorithm. Employing explainable machine learning, the study explores content patterns for disinformation detection. Results from a tree-based approach on real-world data indicate that content-related characteristics can—when used in combination—facilitate the early detection of deceptive news-like articles. The study concludes by discussing the practical implications of computationally detecting the malicious language of disinformation.