Abstract

The most important innovation globally is the internet, which is used by a huge number of people all around the world. People use the internet for different purposes. By using different social media platforms people share content and news. Thus, some people use these internet platforms to share false news or fake news among people. Propaganda against a person, group, organization, or political party may be present in this news. For an individual, it is difficult to distinguish between false and true news over the internet. Therefore, machine learning is the smart solution that can identify fake news on the internet automatically. The goal of this project was to stop misinformation from spreading on X (Twitter). Considering that X (Twitter) is a significant real-time information source as well as a haven for the quick spread of false information, this study attempts to address the threats fake news poses to democratic processes, public opinion, and social stability. The research aims to develop strong detection systems that can reliably discern between authentic news and fake information by utilizing machine learning algorithms, natural language processing methods, and network analysis. This study carries out fake news detection using machine learning algorithms. In conclusion, the project on fake news detection on X (Twitter) represents a significant step toward mitigating the pervasive issue of misinformation on social media platforms. By leveraging advanced machine learning techniques and natural language processing, the project aims to build robust models capable of identifying fake news with high accuracy. Through comprehensive data collection, rigorous preprocessing, and continuous model evaluation, the project addresses the challenges associated with the dynamic and diverse nature of content on X (Twitter).

Library of Congress Subject Headings

Fake news; X (Social networking service); Natural language processing (Computer science); Machine learning

Publication Date

1-20-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

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