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Fake News Detection and Propagation: An NLP Perspective, Slides of Natural Language Processing (NLP)

A comprehensive overview of fake news detection and propagation, focusing on the role of natural language processing (nlp) in combating misinformation. It explores key aspects of fake news, including its characteristics, linguistic features, and structural patterns. The document delves into various nlp techniques for fake news detection, such as text classification, deep learning approaches, and ensemble methods. It also examines advanced nlp models like bert and gpt-3, along with ethical considerations in fake news detection. The document concludes with a case study on misinformation related to the ukraine conflict and discusses future challenges in combating fake news.

Typology: Slides

2024/2025

Uploaded on 12/08/2024

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Fake News
Propaganda
Vijay Kumar (20/11/EC/021),
Nikhil Kumar (20/11/EC/052),
Abhishek Singh (20/11/EC/037)
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Download Fake News Detection and Propagation: An NLP Perspective and more Slides Natural Language Processing (NLP) in PDF only on Docsity!

Fake News

Propaganda

Vijay Kumar (20/11/EC/021), Nikhil Kumar (20/11/EC/052), Abhishek Singh (20/11/EC/037)

Overview

  • (^) Introduction
  • (^) Importance of Studying Fake News Propagation
  • (^) Why spread Fake news?
  • (^) The Role of NLP in Fighting Misinformation
  • (^) Growing problem in recent years
  • (^) NLP Techniques for Detection
  • (^) Advanced NLP Models in Fake News Detection
  • (^) Stance Detection
  • (^) Network Analysis
  • (^) ETHICAL CONSIDERATIONS
  • (^) Multimodal Fake News Detection
  • (^) Case Study – UKRAINE CONFLICT MISINFORMATION
  • (^) Future Challenges

Importance of Studying Fake News Propagation Influence on Society:

  • (^) Impacts public opinion, elections, and social behavior.
  • (^) Example: Spread of misinformation during elections like the 2016 U.S. Presidential Election. Technological Challenge:
  • (^) Tests NLP and AI capabilities to identify and combat fake news. Interdisciplinary Nature:
  • (^) Combines NLP, psychology, sociology, and network science

The Role of NLP in Fighting

Misinformation

1.Detection: Using linguistic features to identify fake news. 2.Classification: Categorizing types of misinformation (e.g., satire vs. propaganda). 3.Fact-Checking: Automating verification with knowledge bases like Wikipedia. 4.Source Analysis: Evaluating credibility of authors and publishers. 5.Propagation Tracking: Monitoring how fake news spreads across platforms.

Characteristics of

Fake News

  • (^) Lack of credible sources, unusual URLs, and excessive ads.
  • (^) Example: Fake health articles on non-reputable domains.
Structural
Patterns:
  • (^) Use of sensationalism, emotional language, and clickbait headlines.
  • (^) Example: "You Won't Believe What Happens Next!"
Linguistic
Features:
  • (^) Misrepresentation of scientific data or outdated information.
    • (^) Example: False claims about climate change using misinterpreted data. Content-Based Indicators:

NLP Techniques for Fake News Detection 1.Text Classification

  • (^) Support Vector Machines (SVM):
  • (^) Separates fake and real news based on features like TF-IDF vectors.
  • (^) Example: Differentiating between biased and objective headlines.
  • (^) Naive Bayes:
  • (^) Classifies fake news using probabilistic models.
  • (^) Example: Categorizing political propaganda articles. 2. Deep Learning Approaches
  • (^) CNNs : Capture local patterns and hierarchical structures in text.
  • (^) RNNs (e.g., LSTM): Handle long-range dependencies in news narratives.
  • (^) Transformers (e.g., BERT): Identify fake news using contextual embeddings.
  • (^) Example: Detecting fake COVID-19 treatments with BERT models. 3. Ensemble Methods
  • (^) Voting Classifier: Combines predictions from multiple models.
  • (^) Boosting: Sequentially improves weak learners like XGBoost.

Feature Engineering for Fake News Detection 1.Text-Based Features: ⚬ (^) N-grams: Identifies sensational phrases common in fake news. ⚬ (^) Sentiment Analysis: Detects exaggerated emotional tone. ⚬ (^) Example: Articles with overly negative tones about vaccines. 2.Contextual Features: ⚬ (^) Analyze author behavior and social media propagation. ⚬ (^) Example: Tracking bots spreading the same article repeatedly.

Stance Detection in Fake News Propagation

  • (^) Stance Detection: Determines the attitude towards a claim (support, oppose, or neutral).
  • (^) Techniques:
  • (^) Feature-Based: Uses n-grams and sentiment lexicons to detect stance.
  • (^) Neural Networks: Attention mechanisms in models like BERT to focus on the target claim.
  • (^) Graph-Based Models: Analyze relationships between entities in fake news propagation.

Network Analysis in Fake

News

Graph Representation

  • (^) Nodes: Represent entities like users, articles, websites.
  • (^) Edges: Represent relationships like shares or citations. Graph-based Techniques
  • (^) Centrality Measures: Identifies influential nodes (e.g., key social media users spreading fake news).
  • (^) Community Detection: Identifies clusters of users spreading similar content.
Multimodal Fake News Detection

Visual-Semantic Embeddings: Projects text and image features into a shared space to identify inconsistencies.

  • (^) Text-Image Fusion:
  • (^) Detects inconsistencies between text and images (e.g., mismatched headlines and photos). ViLBERT: Model for analyzing both text and images in news articles.
Case Study – UKRAINE CONFLICT
MISINFORMATION

Challenge: Widespread misinformation about the conflict. Approach: 1.Network Analysis: ⚬ (^) Used graph neural networks to track the origin of fake narratives. 2.Temporal Tracking: ⚬ (^) Monitored the emergence of fake stories like "Bioweapon Labs in Ukraine." 3.Multilingual NLP: ⚬ (^) Models like XLM-R analyzed misinformation in Russian, Ukrainian, and English. ⚬ (^) Outcome: Improved real-time fact-checking for war-related content.

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