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    <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.</abstract>
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%0 Conference Proceedings
%T KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News Detection
%A Kumar, Anuj
%A Kumar, Pardeep
%A Yadav, Abhishek
%A Ahlawat, Satyadev
%A Prasad, Yamuna
%Y Gesese, Genet Asefa
%Y Sack, Harald
%Y Paulheim, Heiko
%Y Merono-Penuela, Albert
%Y Chen, Lihu
%S Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi, UAE
%F kumar-etal-2025-kgfakenet
%X 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.
%U https://aclanthology.org/2025.genaik-1.12/
%P 109-122

Markdown (Informal)

[KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News Detection](https://aclanthology.org/2025.genaik-1.12/) (Kumar et al., GenAIK 2025)

• KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News Detection (Kumar et al., GenAIK 2025)

ACL

• Anuj Kumar, Pardeep Kumar, Abhishek Yadav, Satyadev Ahlawat, and Yamuna Prasad. 2025. KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News Detection. In Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK), pages 109–122, Abu Dhabi, UAE. International Committee on Computational Linguistics.