Search Results (3)

The rapid growth of social media and online information-sharing platforms facilitates the spread of rumors. Accurate rumor detection to minimize manual verification efforts remains a critical research challenge. While multimodal rumor detection leveraging both text and visual data has gained increasing attention due to the diversification of social media content, existing approaches face the following three key limitations: (1) yhey prioritize lexical features of text while neglecting inherent logical inconsistencies in rumor narratives; (2) they treat textual and visual features as independent modalities, failing to model their intrinsic connections; and (3) they overlook semantic incongruities between text and images, which are common in rumor content. This paper proposes a dual-chain multimodal feature learning framework for rumor detection to address these issues. The framework comprehensively extracts rumor content features through the following two parallel processes: a basic semantic feature extraction module that captures fundamental textual and visual semantics, and a logical connection feature learning module that models both the internal logical relationships within text and the cross-modal semantic alignment between text and images. The framework achieves the multi-level fusion of text–image features by integrating modal alignment and cross-modal attention mechanisms. Extensive experiments on the Pheme and Weibo datasets demonstrate that the proposed method performs better than baseline approaches, confirming its effectiveness in detecting multimodal rumors. Full article

In the era of self-media, the spontaneity and anonymity of information dissemination have led to a surge in rumors, posing significant challenges to cybersecurity. This paper introduces a novel dual-layer $VR-SH{I}_1{I}_{2}R$ rumor control model for studying collaborative rumor-debunking efforts. Utilizing mathematical modeling and simulation methods, we propose key thresholds for rumor propagation from both theoretical and simulation perspectives, and explore optimal methods for rumor control. Our model is validated with real data from actual cases, confirming its accuracy and the effectiveness. The study shows that without intervention, rumors will spread rapidly. Both constant and dynamically optimized control significantly slow down the spread of rumors. However, dynamic optimization control significantly reduces control costs compared to fixed control schemes. Moreover, we find that controlling only the media layer is insufficient. These findings highlight the importance of meticulous approaches to rumor control in the digital age. Full article

The spread of rumors in online social networks (OSNs) has caused a serious threat to the normal social order. In order to describe the rumor-spreading dynamics in OSNs during emergencies, a novel model with consideration of fans versus ordinary people is proposed in this paper. In contrast to previous studies, we consider the case that two rumors exist simultaneously. It is assumed that one is an entertainment rumor that fans care about, and the other is a common rumor. First, we derive the mean-field equations that describe the dynamics of this dual rumor propagation model and obtain the threshold parameter. Secondly, after finding the necessary and sufficient conditions for the existence of equilibriums, we examine the equilibrium’s local and global stability. Finally, simulations are used to explain how various parameters affect the process of spreading rumors. Full article