KGF ake N et: A Knowledge Graph-Enhanced Model for Fake News Detection (2025)

Abstract: The proliferation of fake news on social media has intensified the spread of misinformation, promoting societal biases, hate, and violence. While recent advancements in Generative AI (GenAI), particularly large language models (LLMs), have shown promise, these models often need more structured representation for accurate verification, as they rely on pre-trained data patterns without access to real-time or validated information. This study presents a framework that utilizes Open Information Extractor 6 (OpenIE6) to extract triplet relationships (subject-predicate-object) from statements and justifications to compute the cosine similarity between the Knowledge Graphs (KGs) of the statements and their supporting justification to precisely measure the relevance and alignment between them. This similarity feature is integrated with an attention mechanism over GenAI-generated embeddings to enhance the model’s ability to capture semantic features accurately. In addition, a Multi-Layer Perceptron (MLP) classifier is employed to integrate all features, resulting in a 4% improvement in accuracy and a 5% increase in F1-score over state-of-the-art LLM-based approaches.

## related work identifying fake news has evolved through extensive research, progressing from traditional factchecking approaches to advanced machine learning and deep learning techniques. initial methods primarily relied on manual fact-checking and information retrieval, but as the volume of online misinformation increased, the demand for automated solutions became imperative. research in this area has largely focused on combining natural language processing (nlp) with machine learning to identify linguistic and thematic patterns indicative of fake news. for example, latent dirichlet allocation (lda) @xcite has been employed to reveal hidden topics within news content, highlighting patterns that suggest deceptive intent. this early content-based approach has provided a foundational technique for detecting inconsistencies in fabricated stories. supervised learning algorithms, including support vector machines (svms) and random forests, have also demonstrated effectiveness in misinformation classification, leveraging labeled data to identify fake news. the advent of deep learning methods, such as convolutional neural networks (cnns) and bidirectional long short-term memory networks (bil-stms), further improved detection by capturing nuanced textual features. introducing pre-trained language models (plms) like bert @xcite @xcite @xcite ) marked a significant advancement in the field, as these models harness vast corpora to recognize complex language structures, capturing subtler cues of misinformation. recent studies have also explored the role of influential users in amplifying misinformation, contributing valuable perspectives for detection systems targeting social network effects. ## methodology this section describes the methodology used to develop our proposed model, kgnewsnet, shown in figure 1 . the model integrates multiple layers of textual analysis, knowledge graph embeddings, and metadata, including sentiment analysis, to improve the accuracy of fake news detection. ## experiments and results in this section, we present the experimental setup and results of our proposed model, kgnewsnet, as well as its comparison with other state-of-theart models for fake news detection. the experiments are conducted on the liar-plus dataset, and the results demonstrate how the integration of statement, justification, and external knowledge representations leads to significant performance improvements. we use accuracy and f1-score as the evaluation metrics to benchmark kgnewsnet's performance. ## conclusion this paper presents kgnewsnet, a model for fake news detection that harnesses statements, justifications, metadata, and external knowledge graph embeddings to enhance classification performance. the results indicate that incorporating external knowledge sources and meticulously extracting features from both statements and justifications are pivotal in advancing fake news detection accuracy. while the model achieves strong overall performance, there remain opportunities for improvement, particularly in addressing complex financial statements and nuanced claims requiring intricate reasoning.