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Text classification

Text classification is the task of automatically assigning one or more labels or categories to a text document or sequence (e.g., a headline, article, tweet, or comment). It is a foundational task in NLP and misinformation detection.

Classification approaches

Hand-crafted features + traditional ML: Feature engineering (word frequencies, sentiment lexicons, linguistic patterns) combined with classifiers like SVM, logistic regression, or random forests. Effective but labor-intensive and domain-specific.

Word embeddings + shallow networks: Pre-trained embeddings (word2vec, GloVe) fed to fully connected networks or simple RNNs. Reduces feature engineering while capturing semantic information.

Deep neural networks: CNNs for local n-gram patterns, RNNs/LSTMs for sequential dependencies, attention mechanisms for interpretability. Automatically learn hierarchical feature representations.

Transformer-based models: BERT, GPT, and other pre-trained language models achieve state-of-the-art results by capturing bidirectional context and fine-tuning on task-specific data.

Applications in misinformation detection

  • Fake news detection: Classifying articles or headlines as true/false or misleading/legitimate
  • Clickbait detection: Identifying sensationalized or misleading headlines
  • Rumor stance classification: Categorizing comments as supporting, denying, or querying a rumor
  • Satire detection: Identifying satirical news intended to deceive
  • Propaganda detection: Detecting propaganda techniques in text

Common datasets and benchmarks

  • LIAR: 12.8K political statements labeled True, Mostly True, Half True, Mostly False, False
  • FakeNewsNet: Multi-domain fake news dataset with articles and social context
  • Claim verification: Datasets for evidence retrieval and natural language inference
  • Stance detection: Twitter- and Reddit-based datasets (SemEval, RumEval)

Key papers