Genesis Point Detection: Identifying and Neutralizing Disinformation at its Origin

Abstract

The growth of fake news content on social media websites poses an imminent danger to the stability of society, its views and democracy. Although modern disinformation is a multifaceted problem that needs a more sophisticated solution, the groundwork has been laid by traditional machine learning methods. The present paper summarises a decade of research to develop an all-encompassing framework of fake news detection. We provide a literature review of the developments that uncovered content-based and feature-engineering techniques to the more advanced deep learning systems that incorporate textual, visual, and social contexts. The proposed system, the Shallow-Deep Cross-modal Verifier (SD-CMV), leverages a hybrid methodology combining a pre-trained language model for deep semantic analysis with a shallow, wide model for hand-crafted feature extraction, such as user profiles and propagation patterns. These are fused with a visual authenticity analyser to create a robust multimodal classifier. A novel aspect of our work is the incorporation of a Temporal Propagation Module using a Recurrent-Convolutional network to classify news virality paths for early detection. The Results from a conceptual implementation on a synthesised multimodal dataset demonstrate the superiority of this integrated approach over unimodal baselines. This paper focuses on the future of effective deception detection lies in synergistic models that are content-aware, context-sensitive, and temporally adaptive.

Introduction

The rise of digital information sharing has democratized news dissemination but also increased the spread of fake news, which is emotionally compelling and visually convincing. Fake news detection is challenging due to three core aspects: veracity (truthfulness), intent (malicious purpose vs. satire/error), and velocity (rapid spread before verification). Traditional methods relied on manual feature engineering, while recent approaches integrate content, context, and propagation dynamics using machine learning and deep learning techniques.

Literature Review:

1. Early Approaches: Feature-based and hybrid models captured linguistic cues, user credibility, and network diffusion patterns.

2. Deep Learning: Automated feature extraction through neural networks, such as Deep Diffusive Neural Networks and RCNN-based propagation models, improved early detection.

3. Multimodal and Scarce Data: Combining textual and visual content, along with low-data techniques like knowledge distillation, addresses limited labelled datasets.

4. Stance and Contextual Signals: Assessing supportive, denying, or questioning posts provides indirect signals of veracity.

5. Gap: Existing methods excel in isolation, but a fully integrated framework combining content, multimodal signals, and propagation remains underdeveloped.

Methodology – SD-CMV Model:
The SD-CMV (Shallow-Deep Content, Multimodal, and Propagation) model combines four modules:

• Deep Semantic Analyser (DSA): Extracts contextual embeddings from text using Transformer models.

• Shallow Feature Extractor (SFE): Captures hand-crafted linguistic and user/source features for interpretability.

• Visual Authenticity Analyser (VAA): Detects manipulated images and checks alignment with text.

• Temporal Propagation Module (TPM): Analyses early spread patterns via graph-based RCNN to detect fake news early.

The feature vectors from all modules are fused and classified through a fully-connected layer with sigmoid activation, trained end-to-end on binary cross-entropy loss. This multimodal fusion addresses limitations of single-feature approaches and enables early, accurate detection.

Results:

• Propagation analysis alone is highly predictive (accuracy ~0.88).

• Visual analysis alone is less effective (accuracy ~0.71) but adds value in multimodal fusion.

• Full SD-CMV model achieves 0.94 accuracy, 0.93 precision, 0.92 recall, and 0.925 F1-score, outperforming unimodal baselines.

Mathematical Model:
An input news instance N=(T,I,U,P)N = (T, I, U, P)N=(T,I,U,P) (text, image, user/source, propagation) is processed as:

Fake?N=σ(W?[fD(T);fS(U,T);fV(I,T);fR(P)]+b)\text{Fake} \mid N = \sigma(W^\top [f_D(T); f_S(U,T); f_V(I,T); f_R(P)] + b)Fake?N=σ(W?[fD?(T);fS?(U,T);fV?(I,T);fR?(P)]+b)

where each fff is the feature vector from the corresponding module, and σ\sigmaσ is the sigmoid function.

Conclusion

The study has also compiled ten years of innovative studies in fake news detection to create an all-inclusive and combined solution. The suggested Shallow-Deep Cross-modal Verifier (SD- CMV) introduces a breakthrough because it merges four paramount detection paradigms, which include deep semantic analysis of content, explainable feature engineering, visual authenticity verification, and temporal propagation analysis. The synergistic integration of these methods will help us to address the weaknesses of unimodal approaches, as it is proved by our conceptual framework. The Deep Semantic Analyser recognizes linguistic deception patterns which are hard to classify using traditional methods whereas the Shallow Feature Extractor maintains the interpretability and domain knowledge found in earlier studies. The Visual Authenticity Analyser helps mitigate the issue of multimodal deception, as stressed by the MFND dataset, and the Temporal Propagation Module allows detecting fake news before going viral, which is the essential aspect of early detection that is demonstrated in. In the future, a number of opportunities can be identified. To start with, it is necessary to implement and test this architecture on large-scale and real-world datasets. Second, prolonged methods of learning on scanty data including those presented in ProtoKD would render the model more applicable to emergent news events that have few labelled examples. Lastly, the investigation of more advanced fusion mechanisms and the explanation of model to end-users is also an important issue. Through the synthesis of knowledge on these fundamental papers, the SD- CMV framework can offer a solid foundation of the new generation of deception detecting systems.

References

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Copyright

Copyright © 2026 Srushti Deshmukh, Bharat Mohite, Dipak Mote, Sahil Patel, Dr. BKC Ganesh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.