it through interdisciplinary approaches. FN Detection (FND) involves various techniques, such as Machine Learning (ML), Deep Learning (DL), hybrid models, graph-based methods, explainable AI (XAI), and Natural Language Processing (NLP) techniques. Different types of FN include clickbait, satire, hoaxes, disinformation, stance-based content, etc., to ease its spread to a larger audience. Various components of FN refer to its key elements, such as creators, content, context, and goals. The propagation pathway highlights how FN spreads through platform algorithms, virality patterns, engagement metrics, and its societal impact. Different features used to identify FN include linguistic and syntax patterns, sentiment, network structure, knowledge-based elements, temporal and posting behaviors, etc. In addition, its countermeasures aim to mitigate the impact through AI content moderation, policy and regulation, fact-checking tools, and raising public awareness. The theoretical foundations of FND can be understood through information theory and network dynamics [2; 3]. When we model information dissemination as signals propagating through a complex network \(G=(V,E)\), where nodes \(v_{i}\in V\) represent users and edges \(e_{ij}\in E\) represent interactions such as shares, likes, or retweets, we observe that misinformation often exhibits distinct propagation patterns that differ significantly from authentic news. From a probabilistic perspective, the challenge of detecting FN becomes a classification problem in which, given a news article \(X\), we must estimate the posterior probability \(P(Y\mid X)\). This determines whether the content is genuine (\(Y=1\)) or fabricated (\(Y=0\)). What makes this challenge particularly intriguing from a computational point of view is that FN operates as a distorted signal, a noisy representation of reality that has been intentionally manipulated through bias injection, emotional amplification, or contextual distortion. This signal processing perspective transforms FND into an anomaly detection problem, where we must identify whether a given information signal deviates from the expected distribution of truthful content. The complexity is further increased since FN often exploits cognitive biases, emotional triggers, and social dynamics to achieve viral propagation. It makes it statistically more likely to spread than factual information. Traditional approaches for FND are found to be inadequate to address the scale and sophistication of modern misinformation campaigns [4]. Although manual fact-checking is accurate, its feasibility cannot match the speed of content creation and distribution [5]. Rule-based systems and crowdsourcing platforms such as PolitiFact [6] and ClaimBuster [7]  
Figure 1: A Holistic Perspective on Fake News. are valuable but suffer from scalability limitations, subjective interpretations, and language barriers that prevent comprehensive coverage in the global landscape. Classical ML approaches using hand-crafted features such as \(n\) grams, Term Frequency-Inverse Document Frequency (TF-IDF) vectors, and sentiment scores capture surface-level patterns but do not understand the nuanced contextual relationships and implicit semantic structures that characterize sophisticated misinformation. The emergence of transformer-based architectures, particularly Bidirectional Encoder Representations from Transformers (BERT) and its variants, has revolutionized NLP. Their use of powerful self-attention mechanisms can capture long-range dependencies and contextual relationships within text. For a token sequence \(X=\{w_{1},w_{2},\ldots,w_{n}\}\), BERT generates contextual embeddings \(E_{i}=f(w_{i}\mid w_{<i},w_{>i})\) that incorporate bidirectional context. It enables the model to understand subtle linguistic cues that can indicate deception or manipulation. When these contextualized representations are processed through sophisticated classification heads, such models demonstrate remarkable performance improvements over traditional approaches.