Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs
By Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang, Jiebo Luo. ACM Multimedia, 2017
810 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/20d27c336bef081adf7faffd42721b77a3c92508
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Shengsheng Qian
- Xinyi Zhou
- Yixuan Chen
- Yang Wu
- Shivangi Singhal
- Reza Zafarani
- Jinguang Wang
- Dongsheng Li
Citations in the corpus
- Cross-modal Ambiguity Learning for Multimodal Fake News Detection (2022, methodology): “[15], which has been widely used in prior multimodal fake news detection works.”
- MVAE: Multimodal Variational Autoencoder for Fake News Detection (2019, methodology): “The Weibo dataset, used in [8] for the task of fake news detection consists of data collected from Xinhua News Agency, an authoritative news source of China andWeibo, a Chinese microblogging website.”
- Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021, methodology): “The Weibo dataset is collected by Jin et al. (2017).”
- 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, methodology): “All the Multi-modal approaches take the information from multiple modalities into account, including VQA [2], NeuralTalk [30] and att-RNN [13].”
- SpotFake: A Multi-modal Framework for Fake News Detection (2019, background): “Works [19], [20], [21] are the most notable studies in multimodal fake news detection.”
- Hierarchical Multi-modal Contextual Attention Network for Fake News Detection (2021, methodology): “• att_RNN [12]: att_RNN is a novel Recurrent Neural Network with an attention mechanism to fuse multimodal features for effective rumor detection.”