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FAKE NEWS detection approaches - Coggle Diagram

- - - - Crowd-sourced fact-checking allows users to discuss and comment on the accuracy of news content, but it may be influenced by political bias and competing interests.
      - Computational-oriented fact-checking involves automatic systems categorizing news items as true or false, often utilizing structured knowledge graphs and open web sources. Examples include applications using structured knowledge graphs and open web sources.
      - Expert-oriented fact-checking relies on domain experts to analyze data and documents, as seen on websites like PolitiFact and Snopes. However, this method can be time-consuming.
  - - - Textual-based features extract linguistic, syntactic, and sentiment attributes from news text. Fake news often uses extreme sentiments and aggressive language, with headlines featuring unusual punctuation and capitalization. In contrast, real news headlines are typically shorter and exhibit more varied vocabulary.
      - Visual-based features are essential for detecting fake news as multimedia content proliferates on social media. Leveraging features like diversity, clustering, and coherence scores aids in distinguishing fake news images. Given humans' tendency to believe and remember news with images, effective analysis of visual content is crucial for combating misinformation.
    - - Network-based features, including propagation, interaction, and diffusion networks, analyze social media's information dissemination, crucial for rumor detection and identifying fake news propagation patterns. These features incorporate resharing, message dissemination, and user engagement metrics to assess how news spreads and interacts, enhancing fake news detection models' effectiveness.
      - User-based features analyze social media users' characteristics and behavior, crucial for detecting fake news by assessing user credibility, behavior patterns, and profile details. These features evaluate trustworthiness, engagement metrics, and profile authenticity to distinguish between genuine and fake users and enhance fake news detection models' effectiveness.
  - - - Image-based approaches utilize deep learning architectures like CNNs and VAEs to analyze visual content, detecting fake news by extracting features from images and employing transfer learning methods for enhanced performance. Additionally, MVNNs fuse frequency and pixel domain data, enabling comprehensive analysis of images for improved fake news detection accuracy.
      - Social Context-based Approaches: These approaches incorporate features derived from social network interactions to assess the credibility of news sources. User-based features analyze user profiles and behavior patterns to detect anomalies indicative of fake news dissemination.
      - Text-based approaches utilize methods like CNNs, GANs, and LSTMs to analyze textual content, identifying linguistic patterns and sentiment cues indicative of fake news, thereby enhancing detection accuracy. These models extract features and capture emotional signals, enabling advanced text analysis for improved fake news detection.
    - - Feature fusion models integrate textual and visual features using attention mechanisms or multi-channel CNNs, enhancing fake news detection accuracy through joint learning of representations. Hierarchical architectures like HMCAN capture multi-modal context information, improving detection accuracy with comprehensive analysis of textual and visual data.
      - Pre-trained Model Integration: Some models integrate pre-trained language models like BERT or XLNet with visual features extracted from images to encode textual content effectively before combining it with visual data.
      - Cross-modal similarity approaches, exemplified by the SAFE model, evaluate textual and visual feature similarities to detect fake news, enhancing accuracy through cross-modal connections. Incorporating a cross-modal similarity extractor, SAFE infers connections between news text and images, bolstering detection accuracy with comprehensive analysis.