Fake-News-Detection-AIML-Project

An AI/ML project to detect Fake News (based upon supervised learning algorithm techniques)

Problem Statement

The rapid growth of social media and online news sources has made it increasingly difficult to discern between authentic and fabricated content. Fake news often mimics the format and language of legitimate news, making manual identification a daunting task. Furthermore, the sheer volume of information circulating on the internet exacerbates the challenge of detecting and debunking misinformation in real-time. Consequently, there is a critical need for automated systems capable of effectively distinguishing between factual and deceptive news articles.

Objective

The main objective of the Fake News Detection project is to harness the power of artificial intelligence and machine learning to combat the proliferation of misinformation and promote a more trustworthy and transparent media ecosystem. By developing robust detection algorithms and deploying user-friendly applications, the project aims to contribute towards building a healthier information environment conducive to informed decision-making and civic engagement.

About Dataset

The project uses an Amalgamation of two CSV files as one dataset.

• First CSV file contains a collection of Fake News.
• Second CSV file contains a collection of True News.

The dataset has been taken from here.

Methodology

The project Fake News Detection uses the following methodology/approach:

1. Data Collection
2. EDA (Exploratory Data Analysis)
3. Model Development
4. Model Evaluation
5. Testing

Conclusion

• The proliferation of fake news poses a formidable challenge to society, necessitating innovative approaches for detection and mitigation.
• Through our AI/ML-based solution, we aim to contribute towards combating misinformation and promoting a more informed and resilient society.
• By leveraging advanced machine learning techniques and unsupervised learning algorithms, we aspire to develop a reliable and scalable tool for detecting fake news, thereby safeguarding the integrity of information in the digital age.

Future Scope

1. Continuous Model Improvement
2. Multimodal Analysis
3. Social Media Integration
4. Localization and Multilingual Support
5. Deep Fake Detection

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