This research introduces MetaRoBERTa, a unified transformer-based representation learning framework, as a robust solution for multiclass misleading news classification, and integrates claims, contextual information, justification text, and speaker-related credibility metadata into a single textual representation.
Abstract
Misleading information appearing on various digital platforms poses serious risks to public trust. Recently developed approaches to misleading news detection rely primarily on binary classification; however, real-world fact-checking scenarios demand multiclass formulations that capture varying degrees of truthfulness. To address this gap, our research introduces MetaRoBERTa, a unified transformer-based representation learning framework, as a robust solution for multiclass misleading news classification. MetaRoBERTa integrates claims, contextual information, justification text, and speaker-related credibility metadata into a single textual representation, enabling end-to-end learning without explicit linguistic or behavioral feature engineering. The proposed approach achieves an accuracy of 80.44% on the semantically rich LIAR2 dataset, when justification text is available, surpassing state-of-the-art results. Ablation studies further show that the complete regularized training strategy yields an improvement over a cross-entropy baseline that is nominally significant at the 0.05 level before correction for multiple comparisons, with individual components contributing small, consistently positive gains, and per-class analysis reveals dataset-dependent behavior that provides practical guidance for model selection and deployment.
A hybrid transformer-based ensemble model for automated fake news identification using the FakeNewsNet dataset is proposed and Experimental results show that the ensemble model achieves an accuracy of approximately 93%, outperforming the individual constituent models.
E. C. Babu, G. Sukanya· International Journal for Re...· 0 citations
HEF-XFND is proposed, a hybrid explainable feature-fusion framework that combines sparse lexical evidence, contextual transformer representations, source-level credibility indicators, and calibrated ensemble learning that addresses three recurring limitations in fake-news research.
Raju M, Subalakshmi Kannan, P. P.· International journal of res...· 0 citations
This study examines the effectiveness of two transformer-based architectures—BERT and DeBERTa—for identifying fake news using only textual information from headlines and article bodies and achieves strong performance on FakeDiverse corpus, demonstrating the need for enhanced generalization strategies as well as domain adaptation.
A. Kumar, A. S, Akshara G. Bhat et al.· Scientific Reports· 0 citations
This paper introduces AQFND (Adaptive and Trust-aware Fake News Detector), an end-to-end model that combines dense contextual semantic features provided by a frozen LLM encoder with statistical lexical features (TF-IDF) using a complexity-aware dynamic gating mechanism.
Hemang Thakar, Brijesh Bhatt· Journal of Trends in Compute...· 0 citations
Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag. We ask whether network structure can improve news reliability classification, taking a domain-level approach that shifts the focus from individual articles to source reliability. From URL-sharing patterns in Telegram chats, we build a statistically validated domain co-sharing network and find assortative mixing by reliability: low-reliability domains group together, as do reliable ones. Exploiting this structure, we compare Graph Neural Networks against network-unaware baselines using both content-aware features (multilingual text embeddings) and content-agnostic features (spreading dynamics). GNNs consistently outperform Multi-Layer Perceptrons on identical features, with GraphSAGE best in both settings (accuracy 0.63 with content, 0.53 without), a 13-14% relative gain over the network-unaware baseline. Network topology thus systematically improves domain reliability assessment, and remains effective even when content analysis is infeasible.
Raphaela Keßler, R. D. Ventzke, V. Priesemann et al.· 0 citations
A robust preprocessing pipeline incorporating Google Translation, spaCy-based NER with hyphenated-word normalization, and a future-event-aware fallback logic is proposed, achieving a 40 percent reduction in inference latency and closing the linguistic generalization gap left by previous works.
Rishabh Kumar, Aditya Kumar· Journal of Frontiers in Mult...· 0 citations
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