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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.
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Vijay Kumar (20/11/EC/021), Nikhil Kumar (20/11/EC/052), Abhishek Singh (20/11/EC/037)
Importance of Studying Fake News Propagation Influence on Society:
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.
NLP Techniques for Fake News Detection 1.Text Classification
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
Graph Representation
Visual-Semantic Embeddings: Projects text and image features into a shared space to identify inconsistencies.
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.