[1] GUO B, DING Y S, YAO L N, et al. The future of false information detection on social media: new perspectives and trends[J]. ACM computing surveys, 2020, 53(4): 1-36.
[2] ALLCOTT H, GENTZKOW M. Social media and fake news in the 2016 election[J]. Journal of economic perspectives, 2017, 31(2): 211-236.
[3] KWON S, CHA M, JUNG K, et al. Prominent features of rumor propagation in online social media[C]//2013 IEEE 13th international conference on data mining. Dallas: IEEE, 2013: 1103-1108.
[4] MA J, GAO W, WONG K F, et al. Detect rumors in microblog posts using propagation structure via kernel learning[C]// Proceedings of the 55th annual meeting of the Association for Computational Linguistics. Vancouver: Association for Computational Linguistics, 2017: 708-717.
[5] CASTILLO C, MENDOZA M, POBLETE B. Information credibility on twitter[C]//Proceedings of the 20th international conference on World Wide Web. New York: Association for Computing Machinery, 2011: 675-684.
[6] YU F, LIU Q, WU S, et al. A convolutional approach for misinformation identification[C]// Proceedings of the twenty-sixth international joint conference on artificial intelligence. Melbourne: AAAI Press, 2017: 3901-3907.
[7] MA J, GAO W, MITRA P, et al. Detecting rumors from microblogs with recurrent neural networks[C]//Proceedings of the twenty-fifth international joint conference on artificial intelligence. New York: AAAI Press, 2016: 3818-3824.
[8] 凤丽洲, 刘馥榕, 王友卫. 基于图卷积网络和注意力机制的谣言检测方法[J]. 数据分析与知识发现, 2024, 8(4): 125-136. (FENG L Z, LIU F R, WANG Y W. Rumor detection method based on graph convolution network and attention mechanism[J]. Data analysis and knowledge discovery, 2024, 8(4): 125-136.)
[9] LU Y J, LI C T. GCAN: graph-aware co-attention networks for explainable fake news detection on social media[EB/OL]. [2024-9-30]. https://arxiv.org/abs/2004.11648.
[10] WANG Y, MA F, JIN Z, et al. Eann: event adversarial neural networks for multi-modal fake news detection[C]//Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. London: Association for Computing Machinery, 2018: 849-857.
[11] SINGHAL S, SHAH R R, Chakraborty T, et al. Spotfake: a multi-modal framework for fake news detection[C]//2019 IEEE fifth international conference on multimedia big data. Singapore: IEEE, 2019: 39-47.
[12] ZHANG Y, SUI E, YEUNG-LEVY S. Connect, collapse, corrupt: learning cross-modal tasks with uni-modal data[EB/OL]. [2024-9-30]. https://arxiv.org/abs/2401.08567.
[13] KHOSLA P, TETERWAK P, WANG C, et al. Supervised contrastive learning[C]//Proceedings of the 34th international conference on neural information processing systems. New York: Curran Associates, 2020: 18661-18673.
[14] KUMAR V, GLAUDE H, DE LICHY C, et al. A closer look at feature space data augmentation for few-shot intent classification[EB/OL]. [2024-09-30]. https://arxiv.org/abs/1910.04176.
[15] WEI J, ZOU K. Eda: easy data augmentation techniques for boosting performance on text classification tasks[EB/OL]. [2024-09-30]. https://arxiv.org/abs/1901.11196.
[16] FENG S Y, GANGAL V, WEI J, et al. A survey of data augmentation approaches for NLP[EB/OL]. [2024-09-30]. https://arxiv.org/abs/2105.03075.
[17] SU J L. Have it both ways: simbert model for fusion retrieval and generation[EB/OL]. [2024-09-30]. https://github.com/ZhuiyiTechnology/simbert.
[18] SANH V, DEBUT L, CHAUMOND J, et al. DistilBERT: a distilled version of bert: smaller, faster, cheaper and lighter[EB/OL]. [2024-09-30]. https://arxiv.org/abs/1910.01108.
[19] LIU Y, OTT M, GOYAL N, et al. Roberta: a robustly optimized bert pretraining approach[EB/OL]. [2024-09-30]. https://arxiv.org/abs/2406.00367.
[20] HUA J, CUI X, LI X, et al. Multimodal fake news detection through data augmentation-based contrastive learning[J]. Applied soft computing, 2023, 136(1): 110125-110133.
[21] KEYA A J, WADUD M A H, MRIDHA M F, et al. Augfake-BERT: handling imbalance through augmentation of fake news using BERT to enhance the performance of fake news classification[J]. Applied sciences, 2022, 12(17): 8398-8418.
[22] BUCOS M, ȚUCUDEAN G. Text data augmentation techniques for fake news detection in the Romanian language[J]. Applied sciences, 2023, 13(13): 7389-7408.
[23] AMJAD M, SIDOROV G, ZHILA A. Data augmentation using machine translation for fake news detection in the Urdu language[C]//Proceedings of the twelfth language resources and evaluation conference. Marseille: Language Resources Association, 2020: 2537-2542.
[24] SUYANTO S. Synonyms-based augmentation to improve fake news detection using bidirectional LSTM[C]//20208th International conference on information and communication technology. Yogyakarta: IEEE, 2020: 1-5.
[25] JIN Z, CAO J, GUO H, et al. Multimodal fusion with recurrent neural networks for rumor detection on microblogs[C]//Proceedings of the 25th ACM international conference on multimedia. New York: Association for Computing Machinery, 2017: 795-816.
[26] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[EB/OL]. [2024-09-30]. https://arxiv.org/abs/2401.17396.
[27] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: transformers for image recognition at scale[EB/OL]. [2024-09-30]. https://arxiv.org/abs/2010.11929.
[28] HE K, FAN H, WU Y, et al. Momentum contrast for unsupervised visual representation learning[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Seattle: IEEE, 2020: 9729-9738.
[29] CHEN X, HE K. Exploring simple SIAMESE representation learning[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Nashville: IEEE, 2021: 15750-15758.
[30] GRILL J B, STRUB F, ALTCHÉ F, et al. Bootstrap your own latent-a new approach to self-supervised learning[C]// Proceedings of the 34th international conference on neural information processing systems. New York: Curran Associates, 2020: 21271-21284.
[31] HENDRYCKS D, GIMPEL K. Gaussian error linear units(GELUS)[EB/OL]. [2024-09-30]. https://arxiv.org/abs/2406.14854.
[32] HINTON G, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[EB/OL]. [2024-09-30]. https://arxiv.org/abs/1911.12675.
[33] AGARAP F. Deep learning using rectified linear units(RELU)[EB/OL]. [2024-09-30]. https://arxiv.org/abs/1803.08375.
[34] DAI H, LIU Z, LIAO W, et al. AugGPT: leveraging ChatGPT for text data augmentation[EB/OL]. [2024-09-30]. https://arxiv.org/abs/2302.13007.
[35] ACHIAM J, ADLER S, AGARWAL S, et al. GPT-4 technical report[EB/OL]. [2024-09-30]. https://arxiv.org/abs/2303.08774.
[36] DONG L, YANG N, WANG W, et al. Unified language model pre-training for natural language understanding and generation[C]// Proceedings of the 33rd international conference on neural information processing systems. New York: Curran Associates, 2019: 13063-13075.
[37] 王震宇, 朱学芳. 基于多模态Transformer的虚假新闻检测研究[J]. 情报学报, 2023, 42(12): 1477-1486. (WANG Z Y, ZHU X F. Research on fake news detection based on multimodal transformer[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(12): 1477-1486.)
[38] KHATTAR D, GOUD J S, GUPTA M, et al. MVAE: multimodal variational autoencoder for fake news detection[C]//The world wide web conference. New York: Association for Computing Machinery, 2019: 2915-2921.
[39] CHEN Y, LI D, ZHANG P, et al. Cross-modal ambiguity learning for multimodal fake news detection[C]//Proceedings of the ACM Web conference 2022. New York: Association for Computing Machinery, 2022: 2897-2905.
[40] CHEN T, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]//International conference on machine learning. Vienna: PMLR, 2020: 1597-1607.
[41] WANG F, LIU H. Understanding the behavior of contrastive loss[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Nashville: IEEE, 2021: 2495-2504.