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Introduction:

Fake news has become a prevalent issue in today’s digital age, spreading misinformation and leading to negative consequences. Fake news detection and classification have become essential in order to mitigate the impact of false information on society. This thesis aims to delve into the various techniques and technologies used in detecting and classifying fake news, with the goal of providing a comprehensive understanding of the current methods and future possibilities in this field.

Table of Contents:

Chapter 1: Introduction
– Background of study
– Problem statement
– Objectives of study
– Research questions
– Significance of study
– Scope of study
– Limitations of study

Chapter 2: Literature Review
– Definition and characteristics of fake news
– History and impact of fake news
– Techniques for fake news detection and classification
– Machine learning algorithms for fake news detection
– Challenges in fake news detection and classification
– Previous studies on fake news detection and classification

Chapter 3: Research Methodology
– Research design
– Data collection methods
– Data analysis techniques
– Experimental setup
– Evaluation metrics
– Ethical considerations

Chapter 4: Discussion of Findings
– Results of fake news detection and classification experiments
– Analysis of performance metrics
– Comparison of different techniques
– Discussion on the future of fake news detection and classification

Chapter 5: Conclusion and Summary
– Summary of key findings
– Implications of the study
– Recommendations for future research
– Conclusion

Thesis Overview:

Fake news detection and classification have become crucial in today’s information age, where misinformation can spread rapidly through social media and other online platforms. This thesis aims to provide a comprehensive overview of the current techniques and technologies used in detecting and classifying fake news, as well as exploring the challenges and future possibilities in this field. By examining the history, impact, and characteristics of fake news, as well as discussing the various machine learning algorithms and approaches used for detection, this thesis aims to contribute to the ongoing efforts to combat the spread of misinformation. Through a thorough literature review, research methodology, and discussion of findings, this thesis will provide valuable insights into the field of fake news detection and classification, with the ultimate goal of promoting a more informed and trustworthy online environment for all users.

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