Context-Aware Fake News Detection Using Deep Learning

Abstract

The pervasive spread of fake news (i.e., intentionally misleading information presented as legitimate news) on social media poses a pressing global challenge, distorting public discourse and undermining societal trust. Fake news is often crafted to imitate credible sources and exploit human judgment while leveraging the ways social media prioritizes and disseminates content. This thesis investigates fake news detection (FND) through deep learning (DL) approaches that extend beyond content-only analysis to incorporate contextual and behavioral signals. The primary objective is to develop scalable, accurate, and interpretable (i.e., capable of providing transparent and human-understandable reasoning for predictions) FND systems that can operate effectively across diverse domains and languages. Early detection methods relied heavily on surface-level or manually engineered content features, which proved insufficient for identifying sophisticated forms of fake news. To address these limitations, this thesis introduces hybrid architectures that combine transformer-based encoders with convolutional, recurrent, and attention mechanisms, integrating semantic, linguistic, stylistic, and context-aware features. These models capture both fine-grained textual cues and broader semantic relationships spanning entire articles, enhancing the ability to differentiate factual content from misleading narratives. Beyond textual signals, the work incorporates user metadata, posting histories, temporal dynamics, and relationships across headlines, article bodies, and comments, strengthening detection in situations where textual evidence alone is ambiguous. Scalability is approached through parameter-efficient fine-tuning, where lightweight adapters are integrated into transformer models. A novel fusion mechanism balances general pre-trained knowledge with task-specific information, while selective layer-wise adaptation focuses on semantically critical layers to maximize efficiency. To improve multilingual coverage, the thesis proposes a hybrid summarization technique that distills salient information and reduces redundancy, enabling robust performance without dependence on translation pipelines. This framework operates effectively in both high-resource and low-resource languages, supporting inclusive and scalable deployment. The thesis also tackles cross-domain generalization, introducing domain-adaptive models that extend FND to diverse topics and formats. One approach reframes FND as a prompt-based zero-shot learning (ZSL) task, employing cloze-style prompts and domain-aware augmentation to improve adaptability without fine-tuning. A second approach employs domain-specific expert subnetworks enriched with topic and entity information, combined with adversarial learning to mitigate domain bias. Experiments on multiple benchmark datasets confirm the effectiveness of these methods, achieving state-of-the-art (SOTA) performance. Results demonstrate that the integration of content, context, and propagation-aware signals substantially enhances FND. In summary, this thesis delivers a suite of DL solutions that advance efficiency, robustness, and adaptability in FND. The work addresses scalability, multilinguality, and cross-domain resilience, offering practical and deployable tools for countering the global threat of fake news across platforms, domains, and languages.