Geo-textual rumor detection in location-based social media by decomposing spatial subspaces

The rapid proliferation of location-based social media platforms has greatly accelerated the dissemination of geo-tagged information, but it has also facilitated the widespread propagation of localized rumors. Geo-textual Rumor Detection (GRD) has therefore become an important research topic in geoinformatics aimed at automatically identifying deceptive content tied to specific geographical contexts. However, most existing GRD methods rely on learning static patterns from offline datasets, which limits their ability to generalize to emergent local events characterized by rapidly evolving spatial-temporal information distributions. To better understand this limitation, we conduct preliminary analyses of model fitting behaviors during training and identify two critical issues: imbalanced fitting between real and fake classes, and low-rank feature representations caused by the model’s tendency to overfit to homogeneous real patterns. These phenomena directly lead to the severe loss of vital spatial information, which significantly constrains the model’s capacity to capture the diverse spatial and textual patterns inherent in localized rumors. To address these challenges, we propose a novel framework named Decomposing Orthogonal Spatial Subspaces for Emergent Geo-textual rumor detection (Doseg). Our approach decomposes model transformation matrices via singular value decomposition, explicitly separating linguistic semantic, geographical spatial information-aligned, and localized event-specific spatial components while enforcing orthogonality constraints to enhance spatial feature diversity. Extensive experiments on benchmark geo-textual datasets with strict spatial-temporal splits demonstrate that our method substantially improves detection performance and increases the number of dominant principal components in feature representations, leading to stronger generalization for emergent geo-textual rumor scenarios within the geospatial ecosystem.