Semantic Analysis¶
Semantic analysis in misinformation detection refers to extracting and understanding the linguistic and semantic properties of text to identify false or misleading claims. This includes analyzing linguistic patterns, relationship between entities and topics, lexical sophistication, emotional language, and consistency in claims across documents.
Approach¶
Early semantic approaches relied on hand-crafted features (e.g., count of hedging language, sentiment polarity, named entities). Modern approaches use neural embeddings (Word2Vec, GloVe) and pre-trained language models (BERT, RoBERTa, GPT) to capture deeper semantic relationships and context.
Large language models excel at semantic understanding by leveraging learned representations of language, enabling detection of subtle semantic inconsistencies, contextual anomalies, and nuanced claims that traditional linguistic features miss.
Key papers¶
- Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for (2025) — InSIDE: Combines semantic and intent signals for fake news detection. Models semantic signals via heterogeneous graphs capturing local narrative flow (sliding-window sentence connections) and global context (entity-mediated cross-sentence edges). Shows semantic analysis alone fails to distinguish subtle deceptive content when writing patterns shift; intent-semantic joint learning improves robustness.
- Yi et al. (2025) — Challenges and Innovations in LLM-Powered Fake News Detection — comprehensive survey of LLM-based semantic approaches
- Oshikawa et al. (2020) — NLP methods in fake news detection
Related topics¶
- Natural Language Processing (foundational techniques)
- Large Language Models (modern semantic encoders)
- Semantic similarity and relatedness (comparing semantic similarity between claims)
- Multimodal fake news detection (semantic analysis of multiple modalities)