A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks
Tài liệu tham khảo
Ajao, 2018, Fake news identification on twitter with hybrid cnn and rnn models, 226
Antol, 2015, Vqa: visual question answering, 2425
Ba, J. L., Kiros, J. R., & Hinton, G. E. (2016). Layer normalization.
Boididou, 2016, Verifying multimedia use at mediaeval 2016
Bondielli, 2019, A survey on fake news and rumour detection techniques, Information Sciences, 497, 38, 10.1016/j.ins.2019.05.035
Cao, J., Guo, J., Li, X., Jin, Z., Guo, H., & Li, J. (2018). Automatic rumor detection on microblogs: a survey.
Cao, 2020, Exploring the role of visual content in fake news detection, 141
Castillo, 2011, Information credibility on twitter, 675
Chen, 2018, Call attention to rumors: deep attention based recurrent neural networks for early rumor detection, 40
Chen, 2019, Attention-residual network with cnn for rumor detection, 1121
Cui, 2019, Same: Sentiment-aware multi-modal embedding for detecting fake news, 4148
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding.
Dungs, 2018, Can rumour stance alone predict veracity?, 3360
Guo, 2018, Rumor detection with hierarchical social attention network, 943
Gupta, 2013, Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy, 729
Gupta, 2012, Evaluating event credibility on twitter, 153
Jin, 2017, Multimodal fusion with recurrent neural networks for rumor detection on microblogs, 795
Jin, 2016, News verification by exploiting conflicting social viewpoints in microblogs, 2972
Jin, 2017, Novel visual and statistical image features for microblogs news verification, IEEE Transactions on Multimedia, 19, 598, 10.1109/TMM.2016.2617078
Kakol, 2017, Understanding and predicting web content credibility using the content credibility corpus, Information Processing & Management, 53, 1043, 10.1016/j.ipm.2017.04.003
Karimi, 2018, Multi-source multi-class fake news detection, 1546
Ke, 2015, False rumors detection on sina weibo by propagation structures, 651
Khattar, 2019, Mvae: multimodal variational autoencoder for fake news detection, 2915
Kim, 2014, Convolutional neural networks for sentence classification, 1746
Kochkina, 2018, All-in-one: multi-task learning for rumour verification, 3402
Lazer, 2018, The science of fake news, Science, 359, 1094, 10.1126/science.aao2998
Ma, 2016, Detecting rumors from microblogs with recurrent neural networks, 3818
Ma, 2018, Detect rumor and stance jointly by neural multi-task learning, 585
Ma, 2018, Rumor detection on twitter with tree-structured recursive neural networks, 1980
Ma, 2019, Detect rumors on twitter by promoting information campaigns with generative adversarial learning, 3049
van der Maaten, 2008, Visualizing data using t-SNE, Journal of Machine Learning Research, 9, 2579
Mikolov, 2013, Distributed representations of words and phrases and their compositionality, 3111
Oshikawa, R., Qian, J., & Wang, W. Y. (2018). A survey on natural language processing for fake news detection.
Qi, 2019, Exploiting multi-domain visual information for fake news detection
Qian, 2018, Neural user response generator: Fake news detection with collective user intelligence, 3834
Rubin, 2015, Deception detection for news: three types of fakes, 83:1
Ruchansky, 2017, Csi: A hybrid deep model for fake news detection, 797
Sejeong, 2013, Prominent features of rumor propagation in online social media, 1103
Sharma, 2019, Combating fake news: a survey on identification and mitigation techniques, ACM Transactions on Intelligent Systems and Technology, 10, 21:1, 10.1145/3305260
Shu, 2019, defend: explainable fake news detection, 395
Shu, 2017, Fake news detection on social media: a data mining perspective, ACM SIGKDD Explorations Newsletter, 19, 2236, 10.1145/3137597.3137600
Shu, 2018, Understanding user profiles on social media for fake news detection, 430
Simonyan, 2015, Very deep convolutional networks for large-scale image recognition
Singhal, 2020, Spotfake+: A multimodal framework for fake news detection via transfer learning (student abstract), 13915
Singhal, 2019, Spotfake: A multi-modal framework for fake news detection, 39
Slaney, 2008, Locality-sensitive hashing for finding nearest neighbors, IEEE Signal processing magazine, 25, 128, 10.1109/MSP.2007.914237
Su, 2019, Ensembles of recurrent networks for classifying the relationship of fake news titles, 893896
Tsai, 2019, Multimodal transformer for unaligned multimodal language sequences, 6558
Vaswani, 2017, Attention is all you need, 5998
Vinyals, 2015, Show and tell: a neural image caption generator, 3156
Vo, 2018, The rise of guardians: Fact-checking url recommendation to combat fake news, 275
Vo, 2019, Learning from fact-checkers: analysis and generation of fact-checking language, 335
Vosoughi, 2018, The spread of true and false news online, Science, 359, 1146, 10.1126/science.aap9559
Wang, 2018, Dkn: Deep knowledge-aware network for news recommendation, 1835
Wang, 2018, Eann: event adversarial neural networks for multi-modal fake news detection, 849
Yang, 2012, Automatic detection of rumor on sina weibo, 13:1
Yang, 1998, A study of retrospective and on-line event detection, 28
Yang, 2019, Xlnet: Generalized autoregressive pretraining for language understanding, 5753
Yu, 2017, A convolutional approach for misinformation identification, 3901
Zhang, 2019, Multi-modal knowledge-aware event memory network for social media rumor detection, 1942
Zhang, 2020, An overview of online fake news: Characterization, detection, and discussion, Information Processing & Management, 57, 102025, 10.1016/j.ipm.2019.03.004
Zhao, 2019, An image-text consistency driven multimodal sentiment analysis approach for social media, Information Processing & Management, 56, 102097, 10.1016/j.ipm.2019.102097
Zhou, 2019, Early rumour detection, 1614
Zhou, 2019, Fake news: fundamental theories, detection strategies and challenges, 836