Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election

The development of social media platforms has revolutionized the speed and manner in which information is disseminated, leading to both beneficial and detrimental effects on society. While these platforms facilitate rapid communication, they also accelerate the spread of rumors and extremist speech, impacting public perception and behavior significantly. This issue is particularly pronounced during election periods, where the influence of social media on election outcomes has become a matter of global concern. With the unprecedented number of elections in 2024, against this backdrop, the election ecosystem has encountered unprecedented challenges. This study addresses the urgent need for effective rumor detection on social media by proposing a novel method that combines semantic analysis with graph neural networks. We have meticulously collected a dataset from PolitiFact and Twitter, focusing on politically relevant rumors. Our approach involves semantic analysis using a fine-tuned BERT model to vectorize text content and construct a directed graph where tweets and comments are nodes, and interactions are edges. The core of our method is a graph neural network, SAGEWithEdgeAttention, which extends the GraphSAGE model by incorporating first-order differences as edge attributes and applying an attention mechanism to enhance feature aggregation. This innovative approach allows for the fine-grained analysis of the complex social network structure, improving rumor detection accuracy. The study concludes that our method significantly outperforms traditional content analysis and time-based models, offering a theoretically sound and practically efficient solution.

A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoods

Commonsense knowledge-graphs (CKGs) are important resources towards building machines that can 'reason' on text or environmental inputs and make inferences beyond perception. While current CKGs encode world knowledge for a large number of concepts and have been effectively utilized for incorporating commonsense in neural models, they primarily encode declarative or single-condition inferential knowledge and assume all conceptual beliefs to have the same likelihood. Further, these CKGs utilize a limited set of relations shared across concepts and lack a coherent knowledge organization structure resulting in redundancies as well as sparsity across the larger knowledge graph. Consequently, today's CKGs, while useful for a first level of reasoning, do not adequately capture deeper human-level commonsense inferences which can be more nuanced and influenced by multiple contextual or situational factors. Accordingly, in this work, we study how commonsense knowledge can be better represented by -- (i) utilizing a probabilistic logic representation scheme to model composite inferential knowledge and represent conceptual beliefs with varying likelihoods and (ii) incorporating a hierarchical conceptual ontology to identify salient concept-relevant relations and organize beliefs at different conceptual levels. Our resulting knowledge representation framework can encode a wider variety of world knowledge and represent beliefs flexibly using grounded concepts as well as free-text phrases. As a result, the framework can be utilized as both a traditional free-text knowledge graph and a grounded logic-based inference system more suitable for neuro-symbolic applications. We describe how we extend the PrimeNet knowledge base with our framework through crowd-sourcing and expert-annotation, and demonstrate its application for more interpretable passage-based semantic parsing and question answering.