Fake News Detection Based on Cross-Modal Ambiguity Computation and Multi-Scale Feature Fusion
Source abstract
Abstract
With the rapid growth of social media, the spread of fake news has become a growing problem, misleading the public and causing significant harm. As social media content is often composed of both images and text, the use of mu... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. A parsimonious language model of social media credibility across disparate events
2. Fake news detection on social media10.1145/3137597.3137600 ↗
3. Novel visual and statistical image features for microblogs news verification10.1109/TMM.2016.2617078 ↗
4. NSEP: early fake news detection via news semantic environment perception10.1016/j.ipm.2023.103594 ↗
5. What drives unverified information sharing and cyberchondria during the COVID-19 pandemic?10.1080/0960085X.2020.1770632 ↗
6. Partisan polarization is the primary psychological motivation behind political fake news sharing on twitter10.1017/S0003055421000290 ↗
7. Fake news detection: taxonomy and comparative study10.1016/j.inffus.2023.102140 ↗
8. Rumor detection with field of linear and non-linear propagation
9. Multimodal fusion with recurrent neural networks for rumor detection on microblogs
10. SpotFake: a multi-modal framework for fake news detection
11. EANN: event adversarial neural networks for multi-modal fake news detection
12. Wu · Findings of the association for computational linguistics. Association for Computational Linguistics · 2021 · 2560
13. Cross-modal ambiguity learning for multimodal fake news detection
14. MVAE: multimodal variational autoencoder for fake news detection
15. Multi-modal knowledge-aware event memory network for social media rumor detection
16. Multimodal fusion with BERT and attention mechanism for fake news detection
17. An attentive survey of attention models10.1145/3465055 ↗
18. Hierarchical multi-modal contextual attention network for fake news detection
19. Supervised contrastive learning for multimodal unreliable news detection in COVID-19 pandemic
20. Infosurgeon: cross-media fine-grained information consistency checking for fake news detection
21. Frequency spectrum is more effective for multimodal representation and fusion: a multimodal spectrum rumor detector
22. Robustness of models addressing information disorder: a comprehensive review and benchmarking study10.1016/j.neucom.2024.127951 ↗
23. Twitter-COMMs: detecting climate, COVID, and military multimodal misinformation
24. A survey on multimodal disinformation detection
25. A duo-generative approach to explainable multimodal COVID-19 misinformation detection
26. Devlin J, Chang MW, Lee K, Toutanova K, Hulburd E, Liu D, et al. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. 2018.
27. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
28. Convolutional neural networks for sentence classification
29. Bootstrapping multi-view representations for fake news detection
30. Finding structure in time10.1207/s15516709cog1402_1 ↗
31. Yang Y, Zheng L, Zhang J, Cui Q, Li Z, Yu PS. TI-CNN: convolutional neural networks for fake news detection. arXiv:1806.00749. 2018.
32. Exploiting multi-domain visual information for fake news detection
33. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. 2014.
34. Learning in the frequency domain
35. Lu · Advances in Neural Information Processing Systems · 2019