Exploring the Role of Visual Content in Fake News Detection

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.

This 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 (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• On the difficulty of training Recurrent Neural Networks
• Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
• Fake News Detection on Social Media: A Data Mining Perspective
• Breaking the News: First Impressions Matter on Online News
• 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

Cited by in corpus (6)

• Similarity-Aware Multimodal Prompt Learning for Fake News Detection
• How does fake news spread? Understanding pathways of disinformation spread through APIs
• FakeNewsLab: Experimental Study on Biases and Pitfalls Preventing us from Distinguishing True from False News
• 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