This document discusses the use of machine learning to detect fake news, which is defined as misleading information presented as news to manipulate public opinion. It outlines the challenges in distinguishing fake news from genuine content and presents objectives to develop a scalable and accurate detection system. The proposed methodology includes data collection, preprocessing, and feature extraction using techniques like tf-idf.

1. Fake News Detection Using
Machine Learning
Combating Misinformation with
Advanced Algorithms
Presented by: Md Moazzam Ali
Date: [Insert Date]

2. Introduction
• What is Fake News?
• - False or misleading information presented as
news.
• - Often created to manipulate public opinion.
• Why is it important to detect fake news?
• - Prevents misinformation.
• - Protects the credibility of information
sources.

3. Problem Statement
• Challenges of Fake News Detection:
• - Rapid dissemination of fake news through
social media.
• - Difficulty in distinguishing between genuine
and fake content.
• - Need for scalable and accurate detection
mechanisms.

4. Objectives
• - Develop a system to classify news as fake or
real.
• - Use machine learning algorithms for
detection.
• - Ensure high accuracy and scalability of the
system.

5. Proposed Methodology
• 1. Data Collection:
• - Collect datasets of real and fake news
articles.
• 2. Data Preprocessing:
• - Clean and tokenize text data.
• - Remove stop words and perform
stemming.
• 3. Feature Extraction:
• - Use techniques like TF-IDF or Count

6. Dataset
• - Source: Kaggle
• - Size: (25321, 5)
• - Features:
• - Headline
• - Body Text
• - Label (Fake or Real)