Exploring the Role of Visual Content in Fake News Detection.
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TLDR
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.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 about 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.read more
Citations
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Journal ArticleDOI
A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks
TL;DR: A multimodal fake news detection framework based on Crossmodal Attention Residual and Multichannel convolutional neural Networks (CARMN) is proposed and it is demonstrated that the proposed model outperforms the state-of-the-art methods and learns more discriminable feature representations.
Journal ArticleDOI
Fake news detection based on news content and social contexts: a transformer-based approach
Shaina Raza,Chen Ding +1 more
TL;DR: Zhang et al. as mentioned in this paper proposed a model based on a Transformer architecture, which has two parts: the encoder part to learn useful representations from the fake news data and the decoder part that predicts the future behaviour based on past observations.
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A review on rumour prediction and veracity assessment in online social network
TL;DR: This paper provides deep insight into the various methods used to employ rumour detection and its veracity assessment on multimedia data (Text and Images) with some practical implications.
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A unified approach for detection of Clickbait videos on YouTube using cognitive evidences
TL;DR: Wang et al. as discussed by the authors developed a clickbait video detector (CVD) scheme, which leverages to learn three sets of latent features based on user profiles, video content, and human consensus.
Journal ArticleDOI
A digital media literacy intervention for older adults improves resilience to fake news
Ryan C. Moore,Jeffrey T. Hancock +1 more
TL;DR: The results of a digital literacy intervention for older adults administered during the 2020 U.S. election showed that older adults (Mage = 67) in the treatment condition (N = 143) significantly improved their likelihood of accurately discerning fake from true news from 64% pre-intervention to 85% postintervention as discussed by the authors .
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