Exploring Hyperbolic Hierarchical Structure for Multimodal Rumor Detection (2025)

Abstract: The rise of multimodal content on social platforms has led to the rapid spread of complex and persuasive false narratives, combining of text and images. Traditional rumor detection models attempt to identify such content by relying on textual cues or employing shallow multimodal fusion techniques. However, these methods often assume a simplistic one-to-one alignment between modalities, overlooking the richer hierarchical relationships across modalities, failing to capture the layered structure of meaning. In this paper, we present RumorCone, a novel method that employs hyperbolic geometry in order to preserve hierarchical, non-linear relationships, rather than representing them at a flat semantic level. First, RumorCone decomposes image and text content into three levels: base, mid, and high-level abstractions, and embeds them in hyperbolic space to model their tree-like semantic structure. Second, a dynamic hyperbolic multimodal attention mechanism aligns features across modalities and levels, and a flexible fusion strategy adjusts the contribution of each modality based on alignment quality. Our experiments indicate the importance of hierarchical semantic modeling for robust and interpretable multimodal rumor detection.

## introduction the rapid spread of rumors across social media platforms has emerged as a serious societal challenge, particularly due to the complex nature of multimodal content that integrates both textual and visual elements. recent advancements in multimodal rumor detection have demonstrated potential in utilizing multiple data sources; yet, current models often fail to sufficiently represent the semantic interactions between modalities, particularly in aligning images with corresponding texts. ## method our rumorcone foot_0 framework addresses our hypothesis by explicitly modeling the hierarchical relationships between image and text content across multiple levels of semantic abstraction. unlike prior work that relies on flat semantic representations, rumorcone leverages hyperbolic geometry to preserve the natural tree-like structure of semantic relationships. the proposed architecture, shown in fig. 2 , consists of three main components: (1) multimodal feature extraction at multiple abstraction levels, (2) multi-modal feature fusion in hyperbolic space (3) parallel processing pathways for rumor detection. ## conclusion in this paper, we presented the rumorcone framework, a novel approach to multimodal rumor detection that leverages hierarchical semantic alignment and hyperbolic geometry. the primary objective of rumorcone is to address the challenges posed by the complex nature of multimodal content, particularly the misalignment between textual and visual features that characterize many rumors. by modeling these semantic relationships across multiple abstraction levels, the framework improves the ability to detect subtle inconsistencies inherent in deceptive content. we introduced and thoroughly evaluated rumorcone on two benchmark datasets, m r 2 -e and m r 2 -c, which incorporate both textual and visual content from various social media platforms. our experiments demonstrated that rumorcone outperforms state-of-the-art methods in multimodal rumor detection, achieving superior performance across multiple evaluation metrics.