University of Kwazulu-Natal

Fake News Detection Using Machine Learning Geeksforgeeks

Bonisiwe Shabane
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fake news detection using machine learning geeksforgeeks

Fake news on different platforms is spreading widely and is a matter of serious concern, as it causes social wars and permanent breakage of the bonds established among people. A lot of research is already going on focused on the classification of fake news. Here we will try to solve this issue with the help of machine learning in Python. Before starting the code, download the dataset by clicking the link. The shape of the dataset can be found by the below code. As the title, subject and date column will not going to be helpful in identification of the news.

So, we can drop these column. This repository contains a comprehensive project for detecting fake news using machine learning techniques and various natural language processing techniques. The project includes data analysis, model training, and a web application for real-time fake news detection. The machine learning model is designed to classify news articles as either real or fake based on their content. We aim to develop a machine learning program to identify when a news source may be producing fake news. The model will focus on identifying fake news sources, based on multiple articles originating from a source.

Once a source is labeled as a producer of fake news, we can predict with high confidence that any future articles from that source will also be fake news. Focusing on sources widens our article misclassification tolerance, because we will have multiple data points coming from each source. The intended application of the project is for use in applying visibility weights in social media. Using weights produced by this model, social networks can make stories that are highly likely to be fake news less visible. The repository is organized into the following directories and files: A full training dataset with the following attributes:

To ensure we keep this website safe, please can you confirm you are a human by ticking the box below. If you are unable to complete the above request please contact us using the below link, providing a screenshot of your experience. Uncover The Secrets Of Building A Fake News Detection Project With Machine Learning In This Comprehensive Project Tutorial. | ProjectPro { "@context": "https://schema.org", "@type": "BlogPosting", "image": [ "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Fake_News_Detection_Project.png?w=576&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/False_News_Detection_Project_Github.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/False_News_Detection_Dataset.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/False_News_Detection_Dataset_Import.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Reading_Training_Dataset_For_Detecting_False_News.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Histogram_For_Detecting_False_News.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Graph_For_False_News_Data.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Text_Preprocessing_For_False_News.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Using_Tokenizer_For_Detecting_False_News.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Preprocessing_False_News_Using_Tokenizer.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Preprocessed_Datasets_For_False_News.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Activation_Functions_For_False_News.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Early_Stopping_Method_For_False_News.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Training_Logs_For_False_News.png?w=1242&dpr=1.3", "https://dezyre.gumlet.io/images/blog/fake-news-detection-project/Evaluating_False_News_Dataset.png?w=1242&dpr=1.3" ], "@id": "https://www.projectpro.io/article/fake-news-detection-project/854" } Uncovering the truth has never been easier!

Learn how machine learning algorithms can help combat fake news with our fake news detection project tutorial! Imagine a scenario where a false news story spreads rapidly on social media, claiming that a particular medication is a cure for a deadly disease. People start hoarding the medication, causing scarcity and preventing those who need it from accessing it. This example scenario shows one of the several real-world risks of fake news. The rapid spread of fake news has become a major issue worldwide. The spread of false and misleading news has led to significant social and economic consequences, impacting industries from finance to healthcare.

For example, in 2020, during the COVID-19 pandemic, several countries witnessed a spike in false news about the virus, leading to confusion and panic among people. Misinformation and fake news can have a long-term impact, especially when people rely on accurate information to make critical decisions. The need for detecting fake news has never been more crucial. Machine learning techniques can help us detect fake news efficiently and accurately. Using natural language processing techniques, machine learning algorithms can accurately detect and categorize true and false news. ML systems may distinguish between true news and false news by analyzing patterns in the language and sources used in news reports.

This blog will explore a fake news detection project using machine learning and discuss how machine learning algorithms can efficiently detect and distinguish false news from real news. We will also explore the key machine-learning algorithms used to identify false and true news and real-world use cases of fake news detection. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. Fake news is a type of misinformation that can mislead readers, influence public opinion, and even damage reputations. Detecting fake news prevents its spread and protects individuals and organizations.

Media outlets often use these models to help filter and verify content, ensuring that the news shared with the public is accurate. In this article we'll build a deep learning model using TensorFlow in Python to detect fake news from text. We will be building the model with following steps to make our model: The libraries we will be using are numpy, pandas, scikit learn and tenserflow We will be using fake news dataset, which contains News text and corresponding label (FAKE or REAL). Dataset can be downloaded from this link.

The project aims to develop a machine-learning model capable of identifying and classifying any news article as fake or not. The distribution of fake news can potentially have highly adverse effects on people and culture. This project involves building and training a model to classify news as fake news or not using a diverse dataset of news articles. We have used four techniques to determine the results of the model. Fake news has become a significant issue in today's digital age, where information spreads rapidly through various online platforms. This project leverages machine learning algorithms to automatically determine the authenticity of news articles, providing a valuable tool to combat misinformation.

We have used a labelled dataset containing news articles along with their corresponding labels (true or false). The dataset is divided into two classes: Before running the code, make sure you have the following libraries and packages installed: You can install these dependencies using pip:

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