Exploring the Role of Visual Content in Fake News Detection
arXiv:2003.05096 · doi:10.1007/978-3-030-42699-6
Abstract
The increasing popularity of social media promotes the proliferation of fake news, which has caused significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area of great concern. With the development of multimedia technology, fake news attempts to utilize multimedia content with images or videos to attract and mislead consumers for rapid dissemination, which makes visual content an important part of fake news. Despite the importance of visual content, our understanding of the role of visual content in fake news detection is still limited. This chapter presents a comprehensive review of the visual content in fake news, including the basic concepts, effective visual features, representative detection methods and challenging issues of multimedia fake news detection. This chapter can help readers to understand the role of visual content in fake news detection, and effectively utilize visual content to assist in detecting multimedia fake news.
arXiv commentThis is a preprint of a chapter published in Disinformation, Misinformation, and Fake News in Social Media: Emerging Research Challenges and Opportunities, edited by Kai, S., Suhang, W., Dongwon, L., Huan, L, 2020, Springer reproduced with permission of Springer Nature Switzerland AG. The final authenticated version is available online at: https://www.springer.com/gp/book/9783030426989. arXiv admin note: text overlap with arXiv:2001.00623, arXiv:1808.06686, arXiv:1903.00788 by other authors
References in corpus (19)
- Adam: A Method for Stochastic Optimization
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- XGBoost: A Scalable Tree Boosting System
- TensorFlow: A system for large-scale machine learning
- On the difficulty of training Recurrent Neural Networks
- Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
- Exposing DeepFake Videos By Detecting Face Warping Artifacts
- Style Transfer from Non-Parallel Text by Cross-Alignment
- Fake News Detection on Social Media: A Data Mining Perspective
- FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media
- A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
- In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
- Breaking the News: First Impressions Matter on Online News
- Deep Multimodal Image-Repurposing Detection
- Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text
- What the Language You Tweet Says About Your Occupation
- Image Credibility Analysis with Effective Domain Transferred Deep Networks
- False News Detection on Social Media
- RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation
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- The influence of coordinated behavior on toxicity
- ExClaim: Explainable Neural Claim Verification Using Rationalization
- Deterrence Effects of Social Media Interventions on Health Misinformation Dissemination by Bots and Humans