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Introduction:

Fake news has become a pervasive issue in today’s society, with the spread of misinformation and disinformation on social media platforms posing significant challenges to the public discourse and democratic processes. As the volume and complexity of fake news continue to grow, there is a pressing need for effective tools and techniques to detect and combat the spread of false information. In recent years, graph neural networks (GNNs) have emerged as a powerful tool for analyzing and extracting patterns from graph-structured data, making them a promising solution for fake news detection.

This thesis aims to explore the use of GNNs for fake news detection, leveraging the inherent relationships and structures present in social media networks to distinguish between real and fake news stories. By utilizing GNNs, we seek to improve the accuracy and efficiency of fake news detection algorithms, ultimately contributing to the development of more robust and reliable mechanisms for identifying and combating fake news online.

Table of Contents:

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of fake news detection
2.2 Graph neural networks
2.3 Applications of GNNs in social network analysis
2.4 Techniques for fake news detection
2.5 Previous studies on fake news detection using GNNs
2.6 Limitations of existing approaches
2.7 Challenges in fake news detection
2.8 Ethical considerations in fake news detection
2.9 Future research directions
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Data collection and preprocessing
3.2 Graph construction
3.3 Model architecture
3.4 Training and evaluation
3.5 Performance metrics
3.6 Comparative analysis
3.7 Experimental setup
3.8 Data analysis
3.9 Validity and reliability
3.10 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of experimental results
4.2 Comparison with existing approaches
4.3 Interpretation of results
4.4 Implications for fake news detection
4.5 Insights from GNN-based approach
4.6 Future research directions
4.7 Recommendations for industry practitioners
4.8 Contributions to the field
4.9 Limitations and constraints
4.10 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to fake news detection
5.3 Implications for future research
5.4 Practical applications
5.5 Concluding remarks
5.6 Areas for further investigation

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