Fake News Detection with Machine Learning

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Fake News Detection with Machine Learning In this paper I experiment the possibility to detect fake news based only on textual infor- mation by applying traditional machine learning techniques[5, 6, 7] 

In this Guided Project, you will:

Create a pipeline to remove stop-words ,perform tokenization and padding.

Understand the theory and intuition behind Recurrent Neural Networks and LSTM

Train the deep learning model and assess its performance

In this hands-on project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This project could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news related articles. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

Understand the Problem Statement and business case

Import libraries and datasets

Perform Exploratory Data Analysis

Perform Data Cleaning

Visualize the cleaned data

Prepare the data by tokenizing and padding

Understand the theory and intuition behind Recurrent Neural Networks

Understand the theory and intuition behind LSTM

Build and train the model

Assess trained model performance

In this paper we present the solution to the task of fake news detection by using Deep Learning architectures. Gartner research

This work proposes the use of machine learning techniques to detect Fake news. Three popular methods are used in the experiments:

Detecting Fake News using Machine Learning and Deep Learning Algorithms. Abstract: Social media interaction especially the news spreading around 

Weak Supervision for Fake News Detection via Reinforcement Learning

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