Design and Implementation of a Fake News Detection System Using Machine Learning

ML-based system that classifies Nigerian news articles and social media posts as real or fake.

About this project

This project trains a machine learning model (logistic regression, SVM, and BERT) on a labelled dataset of Nigerian news articles to classify content as real or fake, and exposes the model via a web interface where users paste a URL or text.

Suggested tech stack

Python
scikit-learn
Transformers / BERT
Flask

Chapters 1–5 outline

Chapter 1

Introduction: background to fake news detection, statement of the problem (viral misinformation on Nigerian social media drives ethnic tension and political instability), aim and objectives, research questions, scope, significance of the study, and definition of terms.

Chapter 2

Literature Review: theoretical framework, review of related works on fake news detection, gaps in existing studies, and a summary positioning this project.

Chapter 3

Methodology / System Analysis and Design: Python + scikit-learn + transformers + Flask. Includes data collection method, system requirements, use-case and architecture diagrams (or population, sample size, and instrument).

Chapter 4

Implementation and Results: a deployed classifier with accuracy/precision/recall reported on a held-out Nigerian dataset. Presentation of findings, testing, evaluation, and discussion of results.

Chapter 5

Summary, Conclusion and Recommendations: key findings, contribution to knowledge, limitations, and recommendations for further research.

Get this project done — chapters, code, defence support

Final Year writes the full project for you. Original content, on time, with chat support up to defence day.

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