Deep Learning for NLP – Part 10

Deep Learning for NLP – Part 10, available at $44.99, has an average rating of 4.4, with 19 lectures, based on 5 reviews, and has 68 subscribers.

You will learn about Deep Learning for Natural Language Processing Fake news detection Knowledge based fake news detection Style based fake news detection Propagation based fake news detection Credibility based fake news detection DL for NLP This course is ideal for individuals who are Beginners in deep learning or BTech and Masters students who have done a basic course in deep learning or Social science students with an inclination towards data science or Python developers interested in data science concepts or Masters or PhD students who wish to learn deep learning concepts quickly or Deep learning engineers and developers or Employees of Social media companies It is particularly useful for Beginners in deep learning or BTech and Masters students who have done a basic course in deep learning or Social science students with an inclination towards data science or Python developers interested in data science concepts or Masters or PhD students who wish to learn deep learning concepts quickly or Deep learning engineers and developers or Employees of Social media companies.

Enroll now: Deep Learning for NLP – Part 10

Summary

Title: Deep Learning for NLP – Part 10

Price: $44.99

Average Rating: 4.4

Number of Lectures: 19

Number of Published Lectures: 19

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: ₹1,199

Quality Status: approved

Status: Live

What You Will Learn

Deep Learning for Natural Language Processing
Fake news detection
Knowledge based fake news detection
Style based fake news detection
Propagation based fake news detection
Credibility based fake news detection
DL for NLP

Who Should Attend

Beginners in deep learning
BTech and Masters students who have done a basic course in deep learning
Social science students with an inclination towards data science
Python developers interested in data science concepts
Masters or PhD students who wish to learn deep learning concepts quickly
Deep learning engineers and developers
Employees of Social media companies

Target Audiences

Beginners in deep learning
BTech and Masters students who have done a basic course in deep learning
Social science students with an inclination towards data science
Python developers interested in data science concepts
Masters or PhD students who wish to learn deep learning concepts quickly
Deep learning engineers and developers
Employees of Social media companies

Fake news is now viewed as one of the greatest threats to democracy, journalism, and freedom of expression. The reach of fake news was best highlighted during the critical months of the 2016 U.S. presidential election campaign. During that period, the top twenty frequently-discussed fake election stories generated 8.7M shares, reactions, and comments on Facebook, ironically, more than the 7.4M for the top twenty most-discussed election stories posted by 19 major news websites. Research has shown that compared to the truth, fake news on Twitter is typically retweeted by many more users and spreads far more rapidly, especially for political news. Our economies are not immune to the spread of fake news either, with fake news being connected to stock market fluctuations and large trades. For example, fake news claiming that Barack Obama, the 44th President of the United States, was injured in an explosion wiped out $130 billion in stock value in 2017. These events and losses have motivated fake news research and sparked the discussion around fake news, as observed by skyrocketing usage of terms such as “post-truth” – selected as the international word of the year by Oxford Dictionaries in 2016.

The many perspectives on what fake news is, what characteristics and nature fake news or those who disseminate it share, and how fake news can be detected motivate the need for a comprehensive introduction and in-depth analysis, which this course aims to develop. This course is divided into three sections.

In the first section, I will introduce fake new detection, and discuss topics like “what is fake news and related areas”, “how to manually identify fake news”, “why detect fake news” and “efforts by various organisations towards fighting fake news”. In the second section we will focus on various types of fake news detection methods. Specifically I will talk about four different fake news detection methods which are knowledge based fake news detection, style based fake news detection, propagation based fake news detection and credibility based fake news detection. Lastly in the third section I’ll talk about other perspectives and topics related to fake news detection including fake news detection datasets, explainable fake news detection, concerns around fake news detection and research opportunities.

Hope you will enjoy this course and find the ideas useful for your work.

Course Curriculum

Chapter 1: Introduction to Fake News Detection

Lecture 1: Course Plan

Lecture 2: What is fake news and related areas

Lecture 3: How to manually identify fake news?

Lecture 4: Why detect fake news?

Lecture 5: Efforts towards fighting disinformation

Chapter 2: Methods for fake news detection

Lecture 1: Knowledge based FND – 1

Lecture 2: Knowledge based FND – 2

Lecture 3: Stance Detection

Lecture 4: Style based FND

Lecture 5: Multi-modal fake news detection

Lecture 6: Propagation based FND – 1

Lecture 7: Propagation based FND – 2

Lecture 8: Propagation based FND – 3

Lecture 9: Propagation based FND – 4

Lecture 10: Credibility based FND

Chapter 3: Other aspects of fake news detection

Lecture 1: Fake news datasets and tools

Lecture 2: Explainable FND

Lecture 3: FND Concerns and Research Opportunities

Lecture 4: Summary

Instructors

Manish Gupta
Principal Applied Researcher

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