A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation

• URL: http://arxiv.org/abs/2609.00403v1
• Date: Mon, 31 Aug 2026 21:36:43 GMT
• Title: A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation
• Abstract summary: This paper presents a multi-branch fusion framework for detecting and characterising the propagation of health misinformation in online social networks (OSNs)<n>The framework fuses transformer-based semantics with rhetorical cues, stance representations, and psychologically motivated proxies.<n> Experiments on three benchmark datasets, Constraint, COVID--19_FNIR, and Monkeypox, show strong classification performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a multi-branch fusion framework for detecting and characterising the propagation of health misinformation in online social networks (OSNs). Grounded in the Elaboration Likelihood Model (ELM) and the Theory of Planned Behaviour (TPB), the model fuses transformer-based semantics with rhetorical cues, stance representations, and psychologically motivated proxies in a unified multi-task architecture. In addition to binary classification, we introduce the Cognitive Propagation Score (CPS), an interpretable post-hoc auxiliary score computed from psychologically motivated, text-derived cues capturing argument complexity, emotional intensity, and content-derived virality potential, to support diffusion-risk reasoning when engagement ground truth is incomplete or unavailable. Experiments on three benchmark datasets, Constraint, COVID--19\_FNIR, and Monkeypox, show strong classification performance, achieving ROC--AUC up to 0.9999 on COVID--19\_FNIR, while propagation-oriented ranking achieves near-perfect agreement when engagement-derived supervision is available (Monkeypox, Spearman's $ρ= 0.9952$) and similarly high ranking alignment under proxy-based supervision on COVID--19\_FNIR ($ρ= 0.9954$). Compared with representative literature baselines, the fusion model improves detection on Constraint and COVID--19\_FNIR, while Monkeypox remains more challenging, reflecting domain- and signal-specific differences. Ablation analysis further indicates that psychological and rhetorical branches provide complementary gains beyond semantic embeddings. Overall, the framework bridges cognitive theory and neural modelling to improve transparency and to support scalable misinformation monitoring, with future work required to validate CPS against human-centred diffusion judgements.

Related papers

• BERTopic-Virality Prioritisation: A Scalable Framework for Thematic and Comparative Analysis of COVID-19 and Monkeypox Misinformation on Twitter [0.0]
  We introduce BERTopic-VP, a virality-prioritised topic-modelling framework.<n>It combines contextual embedding-based clustering (BERTopic) with a post hoc Virality Prioritisation layer.<n>The framework achieves strong classification performance, with F1 up to 0.950 and ROC-AUC up to 0.989.<n>It supports monitoring-oriented early warning by surfacing low-volume but high-risk narratives for analyst review.
  arXiv  Detail & Related papers  (2026-08-16T11:42:37Z)
• Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
  We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
  arXiv  Detail & Related papers  (2026-07-24T04:26:47Z)
• Agentic SABRE: An Uncertainty-Aware Neuro-Symbolic Multi-Agent Framework for Adaptive Ransomware Detection [0.8564319625930894]
  Ransomware has evolved into a complex, adaptive, and fast-moving adversary category.<n>We present Agentic SABRE, an uncertainty-aware, neuro-symbolic, multi-agent framework for adaptive ransomware detection.
  arXiv  Detail & Related papers  (2026-07-05T13:08:41Z)
• Quantifiable Uncertainty: A Stochastic Consensus Multi-Agent RAG Framework for Robust Malware Detection [1.2183405753834562]
  We present MAGMA, a framework that decouples malware analysis into semantic code retrieval and probabilistic verification.<n>We show that MAGMA achieves a 98.4% detection rate, substantially exceeding existing solutions.
  arXiv  Detail & Related papers  (2026-05-08T18:46:24Z)
• NeuroTrace: Inference Provenance-Based Detection of Adversarial Examples [1.096626056612224]
  We introduce NeuroTrace, a framework for analyzing inference provenance through Inference Provenance Graphs (IPGs)<n>IPGs are heterogeneous graphs that capture both activation behavior and parameter-induced dataflow during a model's forward pass.<n>We evaluate IPG-based detectors for adversarial example detection under intra-attack, multi-attack, and cross-threat transfer settings.
  arXiv  Detail & Related papers  (2026-04-15T22:23:40Z)
• BROTHER: Behavioral Recognition Optimized Through Heterogeneous Ensemble Regularization for Ambivalence and Hesitancy [39.407739937584104]
  Ambivalence and Hesitancy (A/H) in naturalistic video settings remains a significant challenge in affective computing.<n>We propose a highly regularized, multimodal fusion pipeline to predict A/H at the video level.
  arXiv  Detail & Related papers  (2026-03-15T12:53:41Z)
• AdaFusion: Prompt-Guided Inference with Adaptive Fusion of Pathology Foundation Models [49.550545038402184]
  We propose AdaFusion, a novel prompt-guided inference framework.<n>Our method compresses and aligns tile-level features from diverse models.<n>AdaFusion consistently surpasses individual PFMs across both classification and regression tasks.
  arXiv  Detail & Related papers  (2025-08-07T07:09:31Z)
• Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis [55.13545823385091]
  Federated reinforcement learning (FedRL) enables collaborative learning while preserving data privacy by preventing direct data exchange between agents.<n>In real-world applications, each agent may experience slightly different transition dynamics, leading to inherent model mismatches.<n>We show that even moderate levels of information sharing significantly mitigate environment-specific errors.
  arXiv  Detail & Related papers  (2025-03-21T18:06:28Z)
• Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data [65.28160163774274]
  We apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.
  arXiv  Detail & Related papers  (2022-11-09T14:48:13Z)
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models [103.64435911083432]
  We present a novel contrastive learning strategy called it Proactive Pseudo-Intervention (PPI) PPI leverages proactive interventions to guard against image features with no causal relevance. We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability.
  arXiv  Detail & Related papers  (2020-12-06T20:30:26Z)
• COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing [68.68882022019272]
  COVI-AgentSim is an agent-based compartmental simulator based on virology, disease progression, social contact networks, and mobility patterns. We use COVI-AgentSim to perform cost-adjusted analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features.
  arXiv  Detail & Related papers  (2020-10-30T00:47:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.