Novel Visual and Statistical Image Features for Microblogs News Verification

By Zhiwei Jin, Juan Cao, Yongdong Zhang, Jianshe Zhou, Qi Tian. IEEE transactions on multimedia, 2017

417 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/3f126a4c45dbf451d0c81e1d0e89404467d065a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jiliang Tang
2. Yongdong Zhang
3. Huan Liu
4. Suhang Wang
5. Reza Zafarani
6. Xirong Li
7. Jiebo Luo
8. Lu Su

In Sociology and Political Science

1. Xinyi Zhou
2. Kai Shu
3. Reza Zafarani
4. Huan Liu
5. Xinyi Zhou
6. Suhang Wang
7. Dongwon Lee
8. Juan Cao

Citations in the corpus

• A Survey of Fake News (2020, background): “Visual Features in News Content (defined in [Jin et al. 2017])”
• Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “Recently, various visual and statistical features has been extracted for news verification [38].”
• Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021, background): “Visual features are important for news verifi-cation (Jin et al., 2016; Shu et al., 2017), such as clarity score (Jin et al., 2016), the number of images (Wu et al., 2015; Jin et al., 2016).”
• dEFEND: Explainable Fake News Detection (2019, background): “images and videos) to capture the different characteristics for fake news [19].”
• Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs (2017, methodology): “For the rumor detection task on mircoblogs, however, the social context features are widely used in literatures [7, 16, 31] for effective rumor detection.”
• Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, methodology): “…or regression problem, similar to the framework outlined in Problem 2, content features discussed in Section 3 can be combined with image features [Jin et al. 2017a,b; Wang et al. 2018], user features [Jin et al. 2017a; Tacchini et al. 2017], cascade/network features [Kwon et al. 2013; Wu et al.…”
• EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (2018, background): “Visual features have been shown to be an important indicator for fake news detection [15, 27].”
• MVAE: Multimodal Variational Autoencoder for Fake News Detection (2019, positive): “Jin et al. [9] combine visual, textual and social context features, using an attention mechanism to make predictions about fake news.”
• Cross-modal Ambiguity Learning for Multimodal Fake News Detection (2022, background): “VS [14], which explores visual and statistical features of image content to detect fake news.”
• A survey on fake news and rumour detection techniques (2019, background): “Distributional semantics techniques such as word embeddings [67] have proven to be useful in numerous contexts throughout the NLP research field, and have been successfully implemented also for fake news and rumours detection, especially in machine learning and deep learning approaches [51, 63, 81, ”
• Mining Dual Emotion for Fake News Detection (2019, background): “To tackle this issue, researchers have been devoted to developing automatic methods to detect fake news (i.e., designing a classifier to judge a given news piece as real or fake) by leveraging signals from text [5, 32, 34], images [20, 35], or social contexts [17, 24, 26– 28, 39, 40].”