{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T20:43:39Z","timestamp":1768077819679,"version":"3.49.0"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Inf Syst"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10844-025-00944-6","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T02:55:01Z","timestamp":1746672901000},"page":"1391-1422","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Rumor detection for emergency events via few-shot ensembled prompt learning"],"prefix":"10.1007","volume":"63","author":[{"given":"Chen","family":"Su","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junkang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhentao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuwei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng-yang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"issue":"2","key":"944_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.02.016","volume":"57","author":"SA Alkhodair","year":"2020","unstructured":"Alkhodair, S. A., Ding, S. H., Fung, B. C., & Liu, J. (2020). Detecting breaking news rumors of emerging topics in social media. Information Processing & Management, 57(2), 102018. https:\/\/doi.org\/10.1016\/j.ipm.2019.02.016","journal-title":"Information Processing & Management"},{"issue":"01","key":"944_CR2","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1609\/aaai.v34i01.5393","volume":"34","author":"T Bian","year":"2020","unstructured":"Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., & Huang, J. (2020). Rumor detection on social media with bi-directional graph convolutional networks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 549\u2013556. https:\/\/doi.org\/10.1609\/aaai.v34i01.5393","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"944_CR3","doi-asserted-by":"publisher","unstructured":"Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., ... Amodei, D. (2020). Language models are few-shot learners. In Advances in neural information processing systems (pp. 1877\u20131901). https:\/\/doi.org\/10.48550\/arXiv.2005.14165","DOI":"10.48550\/arXiv.2005.14165"},{"issue":"5","key":"944_CR4","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1108\/IntR-05-2012-0095","volume":"23","author":"C Castillo","year":"2013","unstructured":"Castillo, C., Mendoza, M., & Poblete, B. (2013). Predicting information credibility in time-sensitive social media. Internet Research, 23(5), 560\u2013588. https:\/\/doi.org\/10.1108\/IntR-05-2012-0095","journal-title":"Internet Research"},{"key":"944_CR5","doi-asserted-by":"publisher","unstructured":"Chen, D., Chen, X., Wang, X., Wang, W., & Dong, X. (2022). Frd: Few-shot rumor detection for the novel coronavirus pneumonia epidemic. In 2022 IEEE 21st International Conference on Ubiquitous Computing and Communications (IUCC\/CIT\/DSCI\/SmartCNS) (pp. 224\u2013230). https:\/\/doi.org\/10.1109\/IUCC-CIT-DSCI-SmartCNS57392.2022.00044","DOI":"10.1109\/IUCC-CIT-DSCI-SmartCNS57392.2022.00044"},{"issue":"1","key":"944_CR6","doi-asserted-by":"publisher","first-page":"2467539","DOI":"10.1155\/2023\/2467539","volume":"2023","author":"D Chen","year":"2023","unstructured":"Chen, D., Chen, X., Lu, P., Wang, X., & Lan, X. (2023). Cnfrd: A few-shot rumor detection framework via capsule network for covid-19. International Journal of Intelligent Systems, 2023(1), 2467539. https:\/\/doi.org\/10.1155\/2023\/2467539","journal-title":"International Journal of Intelligent Systems"},{"key":"944_CR7","doi-asserted-by":"publisher","unstructured":"Chen, T., Li, X., Yin, H., & Zhang, J. (2018). Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. In Trends and applications in knowledge discovery and data mining (pp. 40\u201352). https:\/\/doi.org\/10.1007\/978-3-030-04503-6_4","DOI":"10.1007\/978-3-030-04503-6_4"},{"key":"944_CR8","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (Long and Short Papers) (pp. 4171\u20134186).https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"944_CR9","doi-asserted-by":"publisher","unstructured":"Ding, N., Chen, Y., Han, X., Xu, G., Xie, P., Zheng, H.-T., Liu, Z., Li, J., & Kim, H.-G. (2021). Prompt-learning for fine-grained entity typing. https:\/\/doi.org\/10.18653\/v1\/2022.findings-emnlp.512","DOI":"10.18653\/v1\/2022.findings-emnlp.512"},{"key":"944_CR10","doi-asserted-by":"publisher","unstructured":"Dong, Y., He, D., Wang, X., Jin, Y., Ge, M., Yang, C., & Jin, D. (2024). Unveiling implicit deceptive patterns in multi-modal fake news via neuro-symbolic reasoning. In Proceedings of the AAAI conference on artificial intelligence (pp. 8354\u20138362). https:\/\/doi.org\/10.1609\/aaai.v38i8.28677","DOI":"10.1609\/aaai.v38i8.28677"},{"key":"944_CR11","doi-asserted-by":"publisher","unstructured":"Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th international conference on machine learning (pp. 1126\u20131135). https:\/\/doi.org\/10.48550\/arXiv.1703.03400","DOI":"10.48550\/arXiv.1703.03400"},{"key":"944_CR12","doi-asserted-by":"publisher","unstructured":"GLM, T. (2024). Chatglm: A family of large language models from glm-130b to glm-4 all tools. https:\/\/doi.org\/10.48550\/arXiv.2406.12793, arXiv:2406.12793","DOI":"10.48550\/arXiv.2406.12793"},{"key":"944_CR13","doi-asserted-by":"publisher","unstructured":"Gu, Y., Han, X., Liu, Z., & Huang, M. (2022). PPT: Pre-trained prompt tuning for few-shot learning. In Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers) (pp. 8410\u20138423). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.576","DOI":"10.18653\/v1\/2022.acl-long.576"},{"key":"944_CR14","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.aiopen.2022.11.003","volume":"3","author":"X Han","year":"2022","unstructured":"Han, X., Zhao, W., Ding, N., Liu, Z., & Sun, M. (2022). Ptr: Prompt tuning with rules for text classification. AI Open, 3, 182\u2013192. https:\/\/doi.org\/10.1016\/j.aiopen.2022.11.003","journal-title":"AI Open"},{"key":"944_CR15","doi-asserted-by":"publisher","unstructured":"He, Z., Li, C., Zhou, F., & Yang, Y. (2021). Rumor detection on social media with event augmentations. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, SIGIR \u201921 (pp. 2020\u20132024). https:\/\/doi.org\/10.1145\/3404835.3463001","DOI":"10.1145\/3404835.3463001"},{"key":"944_CR16","doi-asserted-by":"publisher","unstructured":"Hou, Y., Dong, H., Wang, X., Li, B., & Che, W. (2022). MetaPrompting: Learning to learn better prompts. In Proceedings of the 29th international conference on computational linguistics (pp. 3251\u20133262). https:\/\/doi.org\/10.48550\/arXiv.2209.11486","DOI":"10.48550\/arXiv.2209.11486"},{"key":"944_CR17","doi-asserted-by":"publisher","unstructured":"Hu, S., Ding, N., Wang, H., Liu, Z., Wang, J., Li, J., Wu, W., & Sun, M. (2022a). Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification. In Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers) (pp. 2225\u20132240). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.158","DOI":"10.18653\/v1\/2022.acl-long.158"},{"key":"944_CR18","doi-asserted-by":"publisher","unstructured":"Hu, Z., Liu, H., Li, K., Wang, Y., Liu, Z., & Zhang, X. (2022b). Pekin: Prompt-based external knowledge integration network for rumor detection on social media. In PRICAI 2022: Trends in artificial intelligence (pp. 183\u2013196). https:\/\/doi.org\/10.1007\/978-3-031-20865-2_14","DOI":"10.1007\/978-3-031-20865-2_14"},{"key":"944_CR19","doi-asserted-by":"publisher","unstructured":"Huang, H., Liu, X., Shi, G., Liu, Q. (2023a). Event extraction with dynamic prefix tuning and relevance retrieval. IEEE Transactions on Knowledge and Data Engineering, pp. 1\u201313. https:\/\/doi.org\/10.1109\/TKDE.2023.3266495","DOI":"10.1109\/TKDE.2023.3266495"},{"key":"944_CR20","doi-asserted-by":"publisher","unstructured":"Huang, J., Meng, Y., & Han, J. (2022). Few-shot fine-grained entity typing with automatic label interpretation and instance generation. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, KDD \u201922 (pp. 605\u2013614). https:\/\/doi.org\/10.1145\/3534678.3539443","DOI":"10.1145\/3534678.3539443"},{"key":"944_CR21","doi-asserted-by":"publisher","unstructured":"Huang, Y., & Sun, L. (2023). Harnessing the power of chatgpt in fake news: An in-depth exploration in generation, detection and explanation. https:\/\/doi.org\/10.48550\/arXiv.2310.05046, arXiv:2310.05046","DOI":"10.48550\/arXiv.2310.05046"},{"issue":"3","key":"944_CR22","doi-asserted-by":"publisher","first-page":"103279","DOI":"10.1016\/j.ipm.2023.103279","volume":"60","author":"Y Huang","year":"2023","unstructured":"Huang, Y., Gao, M., Wang, J., Yin, J., Shu, K., Fan, Q., & Wen, J. (2023b). Meta-prompt based learning for low-resource false information detection. Information Processing & Management, 60(3), 103279. https:\/\/doi.org\/10.1016\/j.ipm.2023.103279","journal-title":"Information Processing & Management"},{"key":"944_CR23","doi-asserted-by":"publisher","unstructured":"Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing social media messages in mass emergency: A survey. ACM Computing Surveys, 47(4). https:\/\/doi.org\/10.1145\/2771588","DOI":"10.1145\/2771588"},{"key":"944_CR24","doi-asserted-by":"publisher","unstructured":"Jin, Z., Cao, J., Guo, H., Zhnag, Y., & Luo, J. (2017). Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In Proceedings of the 25th ACM international conference on multimedia, MM \u201917 (pp. 795\u2013816). https:\/\/doi.org\/10.1145\/3123266.3123454","DOI":"10.1145\/3123266.3123454"},{"issue":"1","key":"944_CR25","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10844-022-00769-7","volume":"61","author":"B Jlifi","year":"2023","unstructured":"Jlifi, B., Sakrani, C., & Duvallet, C. (2023). Towards a soft three-level voting model (soft t-lvm) for fake news detection. Journal of Intelligent Information Systems, 61(1), 249\u2013269. https:\/\/doi.org\/10.1007\/s10844-022-00769-7","journal-title":"Journal of Intelligent Information Systems"},{"key":"944_CR26","doi-asserted-by":"publisher","unstructured":"Khan, A., Majumdar, D., & Mondal, B. (2025). A hybrid transformer based model for sarcasm detection from news headlines. Journal of Intelligent Information Systems, pp. 1\u201321. https:\/\/doi.org\/10.1007\/s10844-025-00941-9","DOI":"10.1007\/s10844-025-00941-9"},{"key":"944_CR27","doi-asserted-by":"publisher","unstructured":"Lao, A., Zhang, Q., Shi, C., Cao, L., Yi, K., Hu, L., & Miao, D. (2024). Frequency spectrum is more effective for multimodal representation and fusion: A multimodal spectrum rumor detector. In Proceedings of the AAAI conference on artificial intelligence (pp. 18426\u201318434). https:\/\/doi.org\/10.1609\/aaai.v38i16.29803","DOI":"10.1609\/aaai.v38i16.29803"},{"key":"944_CR28","doi-asserted-by":"publisher","unstructured":"Li, B., Ju, J., Wang, C., & Pan, S. (2023a). How does chatgpt affect fake news detection systems? In Advanced data mining and applications (pp. 565\u2013580). https:\/\/doi.org\/10.1007\/978-3-031-46664-9_38","DOI":"10.1007\/978-3-031-46664-9_38"},{"key":"944_CR29","doi-asserted-by":"publisher","unstructured":"Li, J., Wang, L., He, J., Zhang, Y., &Liu, A. (2023b). Improving rumor detection by class-based adversarial domain adaptation. In Proceedings of the 31st ACM international conference on multimedia (pp. 6634\u20136642). https:\/\/doi.org\/10.1145\/3581783.3612501","DOI":"10.1145\/3581783.3612501"},{"key":"944_CR30","doi-asserted-by":"publisher","unstructured":"Li, L., Cai, G., & Chen, N. (2018). A rumor events detection method based on deep bidirectional gru neural network. In 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) (pp. 755\u2013759). https:\/\/doi.org\/10.1109\/ICIVC.2018.8492819","DOI":"10.1109\/ICIVC.2018.8492819"},{"key":"944_CR31","doi-asserted-by":"publisher","unstructured":"Lin, H., Ma, J., Chen, L., Yang, Z., Cheng, M., & Guang, C. (2022). Detect rumors in microblog posts for low-resource domains via adversarial contrastive learning. In Findings of the Association for Computational Linguistics: NAACL 2022 (pp. 2543\u20132556). https:\/\/doi.org\/10.18653\/v1\/2022.findings-naacl.194","DOI":"10.18653\/v1\/2022.findings-naacl.194"},{"issue":"4","key":"944_CR32","doi-asserted-by":"publisher","first-page":"5213","DOI":"10.48550\/arXiv.2212.01117","volume":"37","author":"H Lin","year":"2023","unstructured":"Lin, H., Yi, P., Ma, J., Jiang, H., Luo, Z., Shi, S., & Liu, R. (2023). Zero-shot rumor detection with propagation structure via prompt learning. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5213\u20135221. https:\/\/doi.org\/10.48550\/arXiv.2212.01117","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"944_CR33","doi-asserted-by":"publisher","unstructured":"Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9). https:\/\/doi.org\/10.1145\/3560815","DOI":"10.1145\/3560815"},{"key":"944_CR34","doi-asserted-by":"publisher","unstructured":"Liu, Y. & Wu, Y. F. B. (2020). Fned: A deep network for fake news early detection on social media. ACM Transactions on Informations Systems, 38(3). https:\/\/doi.org\/10.1145\/3386253","DOI":"10.1145\/3386253"},{"key":"944_CR35","doi-asserted-by":"publisher","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. https:\/\/doi.org\/10.48550\/arXiv.1907.11692","DOI":"10.48550\/arXiv.1907.11692"},{"key":"944_CR36","doi-asserted-by":"publisher","unstructured":"Logan\u00a0IV, R., Balazevic, I., Wallace, E., Petroni, F., Singh, S., & Riedel, S. (2022). Cutting down on prompts and parameters: Simple few-shot learning with language models. In Findings of the Association for Computational Linguistics: ACL 2022 (pp. 2824\u20132835). https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.222","DOI":"10.18653\/v1\/2022.findings-acl.222"},{"key":"944_CR37","doi-asserted-by":"publisher","unstructured":"Hy, L., Fan, C., Song, X., & Fang, W.(2021). A novel few-shot learning based multi-modality fusion model for covid-19 rumor detection from online social media. PeerJ Computer Science, 7, e688. https:\/\/doi.org\/10.7717\/peerj-cs.688","DOI":"10.7717\/peerj-cs.688"},{"key":"944_CR38","doi-asserted-by":"publisher","unstructured":"Lv, B., Jin, L., Zhang, Y., Wang, H., Li, X., & Guo, Z. (2022). Commonsense knowledge-aware prompt tuning for few-shot nota relation classification. Applied Sciences, 12(4). https:\/\/doi.org\/10.3390\/app12042185","DOI":"10.3390\/app12042185"},{"key":"944_CR39","doi-asserted-by":"publisher","unstructured":"Ma, J., Gao, W., Wei, Z., Lu, Y., & Wong, K.-F. (2015). Detect rumors using time series of social context information on microblogging websites. CIKM \u201915 (pp. 1751\u20131754). https:\/\/doi.org\/10.1145\/2806416.2806607","DOI":"10.1145\/2806416.2806607"},{"key":"944_CR40","doi-asserted-by":"publisher","unstructured":"Ma, J., Gao, W., & Wong, K. F. (2018). Rumor detection on Twitter with tree-structured recursive neural networks. In Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers) (pp. 1980\u20131989). https:\/\/doi.org\/10.18653\/v1\/P18-1184","DOI":"10.18653\/v1\/P18-1184"},{"issue":"3","key":"944_CR41","doi-asserted-by":"publisher","first-page":"2657","DOI":"10.1109\/TKDE.2021.3112497","volume":"35","author":"J Ma","year":"2023","unstructured":"Ma, J., Li, J., Gao, W., & Yang, Y. (2023). Improving rumor detection by promoting information campaigns with transformer-based generative adversarial learning. IEEE Transactions on Knowledge and Data Engineering, 35(3), 2657\u20132670. https:\/\/doi.org\/10.1109\/TKDE.2021.3112497","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"944_CR42","doi-asserted-by":"publisher","unstructured":"Ma, Z., Luo, M., Guo, H., Zeng, Z., Hao, Y., & Zhao, X. (2024). Event-radar: Event-driven multi-view learning for multimodal fake news detection. In L. W. Ku, A. Martins, & V. Srikumar (Eds.), Proceedings of the 62nd annual meeting of the association for computational linguistics (Volume 1: Long Papers) (pp. 5809\u20135821). https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.316","DOI":"10.18653\/v1\/2024.acl-long.316"},{"key":"944_CR43","doi-asserted-by":"publisher","unstructured":"Mendoza, M., Poblete, B., & Castillo, C. (2010). Twitter under crisis: Can we trust what we rt? In Proceedings of the First Workshop on Social Media Analytics, SOMA \u201910 (pp. 71\u201379). https:\/\/doi.org\/10.1145\/1964858.1964869","DOI":"10.1145\/1964858.1964869"},{"key":"944_CR44","doi-asserted-by":"publisher","unstructured":"Mukherjee, R., Vishnu, U., Peruri, H. C., Bhattacharya, S., Rudra, K., Goyal, P., & Ganguly, N. (2022). Mtlts: A multi-task framework to obtain trustworthy summaries from crisis-related microblogs. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM \u201922 (pp. 755\u2013763). https:\/\/doi.org\/10.1145\/3488560.3498536","DOI":"10.1145\/3488560.3498536"},{"key":"944_CR45","doi-asserted-by":"publisher","unstructured":"Okanovic, P., Waleffe, R., Mageirakos, V., Nikolakakis, K. E., Karbasi, A., Kalogerias, D., G\u00fcrel, N. M., & Rekatsinas, T. (2023). Repeated random sampling for minimizing the time-to-accuracy of learning. arXiv:2305.18424. https:\/\/doi.org\/10.48550\/arXiv.2305.18424","DOI":"10.48550\/arXiv.2305.18424"},{"key":"944_CR46","doi-asserted-by":"publisher","unstructured":"Pelrine, K., Imouza, A., Thibault, C., Reksoprodjo, M., Gupta, C., Christoph, J., Godbout, J.-F., & Rabbany, R. (2023). Towards reliable misinformation mitigation: Generalization, uncertainty, and gpt-4. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.395, arXiv:2305.14928","DOI":"10.18653\/v1\/2023.emnlp-main.395"},{"key":"944_CR47","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.neucom.2023.02.044","volume":"533","author":"H Ran","year":"2023","unstructured":"Ran, H., Jia, C., & Yu, J. (2023). A metric-learning method for few-shot cross-event rumor detection. Neurocomputing, 533, 72\u201385. https:\/\/doi.org\/10.1016\/j.neucom.2023.02.044","journal-title":"Neurocomputing"},{"issue":"1","key":"944_CR48","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s13278-020-00634-x","volume":"10","author":"S Santhoshkumar","year":"2020","unstructured":"Santhoshkumar, S., & Dhinesh Babu, L. D. (2020). Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks. Social Network Analysis and Mining, 10(1), 20. https:\/\/doi.org\/10.1007\/s13278-020-00634-x","journal-title":"Social Network Analysis and Mining"},{"key":"944_CR49","doi-asserted-by":"publisher","unstructured":"Scala, F., Flesca, S., & Pontieri, L. (2024). Play it straight: An intelligent data pruning technique for green-ai. In International conference on discovery science (pp. 69\u201385). Springer. https:\/\/doi.org\/10.1007\/978-3-031-78977-9_5","DOI":"10.1007\/978-3-031-78977-9_5"},{"key":"944_CR50","doi-asserted-by":"publisher","unstructured":"Schick, T., & Sch\u00fctze, H. (2021). Exploiting cloze-questions for few-shot text classification and natural language inference. In Proceedings of the 16th conference of the european chapter of the association for computational linguistics: Main volume (pp. 255\u2013269). https:\/\/doi.org\/10.18653\/v1\/2021.eacl-main.20","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"944_CR51","doi-asserted-by":"publisher","unstructured":"Seoh, R., Birle, I., Tak, M., Chang, H.-S., Pinette, B., & Hough, A. (2021). Open aspect target sentiment classification with natural language prompts. In Proceedings of the 2021 conference on empirical methods in natural language processing (pp. 6311\u20136322). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.509","DOI":"10.18653\/v1\/2021.emnlp-main.509"},{"key":"944_CR52","doi-asserted-by":"publisher","unstructured":"Shushkevich, E., Alexandrov, M., & Cardiff, J. (2023). Improving multiclass classification of fake news using bert-based models and chatgpt-augmented data. Inventions, 8(5). https:\/\/doi.org\/10.3390\/inventions8050112","DOI":"10.3390\/inventions8050112"},{"key":"944_CR53","doi-asserted-by":"publisher","unstructured":"Su, J., Lu, Y., Pan, S., Murtadha, A., Wen, B., Liu, Y. (2021). Roformer: Enhanced transformer with rotary position embedding. https:\/\/doi.org\/10.1016\/j.neucom.2023.127063","DOI":"10.1016\/j.neucom.2023.127063"},{"key":"944_CR54","doi-asserted-by":"publisher","unstructured":"Sun, M., Zhang, X., Ma, J., Xie, S., Liu, Y., & Yu, P. S. (2023). Inconsistent matters: A knowledge-guided dual-consistency network for multi-modal rumor detection. IEEE Transactions on Knowledge and Data Engineering, pp. 1\u201314. https:\/\/doi.org\/10.1109\/TKDE.2023.3275586","DOI":"10.1109\/TKDE.2023.3275586"},{"key":"944_CR55","doi-asserted-by":"publisher","unstructured":"Sun, S., Liu, H., He, J., & Du, X. (2013). Detecting event rumors on sina weibo automatically. In Web technologies and applications (pp. 120\u2013131). https:\/\/doi.org\/10.1007\/978-3-642-37401-2_14","DOI":"10.1007\/978-3-642-37401-2_14"},{"key":"944_CR56","doi-asserted-by":"publisher","unstructured":"Veyseh, A. P. B., Thai, M. T., Nguyen, T. H., & Dou, D. (2020). Rumor detection in social networks via deep contextual modeling. In Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM \u201919 (pp. 113\u2013120). https:\/\/doi.org\/10.1145\/3341161.3342896","DOI":"10.1145\/3341161.3342896"},{"key":"944_CR57","doi-asserted-by":"publisher","unstructured":"Wang, H., Xu, C., & McAuley, J. (2022). Automatic multi-label prompting: Simple and interpretable few-shot classification. In Proceedings of the 2022 conference of the North American Chapter of the association for computational linguistics: Human language technologies (pp. 5483\u20135492). https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.401","DOI":"10.18653\/v1\/2022.naacl-main.401"},{"key":"944_CR58","doi-asserted-by":"publisher","unstructured":"Wang, W. Y. (2017). \u201cliar, liar pants on fire\u201d: A new benchmark dataset for fake news detection. In Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 2: Short Papers) (pp. 422\u2013426). https:\/\/doi.org\/10.18653\/v1\/P17-2067","DOI":"10.18653\/v1\/P17-2067"},{"key":"944_CR59","doi-asserted-by":"publisher","unstructured":"Wu, L., Li, J., Hu, X., & Liu, H. (2017). Gleaning wisdom from the past: Early detection of emerging rumors in social media. In Proceedings of the 2017 SIAM International Conference on Data Mining (SDM) (pp. 99\u2013107). https:\/\/doi.org\/10.1137\/1.9781611974973.12. https:\/\/epubs.siam.org\/doi\/pdf\/10.1137\/1.9781611974973.12","DOI":"10.1137\/1.9781611974973.12"},{"issue":"2","key":"944_CR60","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1109\/TKDE.2021.3103833","volume":"35","author":"L Wu","year":"2023","unstructured":"Wu, L., Rao, Y., Zhang, C., & Zhao, Y. (2023). Category-controlled encoder-decoder for fake news detection. IEEE Transactions on Knowledge and Data Engineering, 35(2), 1242\u20131257. https:\/\/doi.org\/10.1109\/TKDE.2021.3103833","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"944_CR61","doi-asserted-by":"publisher","unstructured":"Yang, C., Zhang, P., Qiao, W., Gao, H., & Zhao, J. (2023). Rumor detection on social media with crowd intelligence and ChatGPT-assisted networks. In Proceedings of the 2023 conference on empirical methods in natural language processing (pp. 5705\u20135717). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.347","DOI":"10.18653\/v1\/2023.emnlp-main.347"},{"key":"944_CR62","doi-asserted-by":"publisher","unstructured":"Zhang, H., Zhang, X., Huang, H., & Yu, L. (2022). Prompt-based meta-learning for few-shot text classification. In Proceedings of the 2022 conference on empirical methods in natural language processing (pp. 1342\u20131357). https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.87","DOI":"10.18653\/v1\/2022.emnlp-main.87"},{"key":"944_CR63","doi-asserted-by":"publisher","unstructured":"Zhang, L., Zhang, X., Zhou, Z., Huang, F., & Li, C. (2024). Reinforced adaptive knowledge learning for multimodal fake news detection. In Proceedings of the AAAI conference on artificial intelligence (pp. 16777\u201316785). https:\/\/doi.org\/10.1609\/aaai.v38i15.29618","DOI":"10.1609\/aaai.v38i15.29618"},{"key":"944_CR64","doi-asserted-by":"publisher","unstructured":"Zhang, T., & Hu, S. (2023). Unsupervised cross-domain rumor detection from multiple sources based on roberta and multi-cnn. In Proceedings of the 2023 7th International Conference on Deep Learning Technologies, ICDLT \u201923 (pp. 85\u201390). https:\/\/doi.org\/10.1145\/3613330.3613340","DOI":"10.1145\/3613330.3613340"},{"key":"944_CR65","doi-asserted-by":"publisher","unstructured":"Zhao, Z., Resnick, P., & Mei, Q. (2015). Enquiring minds: Early detection of rumors in social media from enquiry posts. In Proceedings of the 24th International Conference on World Wide Web, WWW \u201915 (pp. 1395\u20131405). https:\/\/doi.org\/10.1145\/2736277.2741637","DOI":"10.1145\/2736277.2741637"},{"key":"944_CR66","doi-asserted-by":"publisher","unstructured":"Zubiaga, A., Liakata, M., & Procter, R. (2016). Learning reporting dynamics during breaking news for rumour detection in social media. arXiv:1610.07363. https:\/\/doi.org\/10.48550\/arXiv.2104.09864","DOI":"10.48550\/arXiv.2104.09864"},{"key":"944_CR67","doi-asserted-by":"publisher","unstructured":"Zuo, Y., Zhu, W., & Cai, G. (2022). Continually detection, rapidly react: Unseen rumors detection based on continual prompt-tuning. In Proceedings of the 29th international conference on computational linguistics (pp. 3029\u20133041). https:\/\/doi.org\/10.48550\/arXiv.2203.11720","DOI":"10.48550\/arXiv.2203.11720"}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-025-00944-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-025-00944-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-025-00944-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T03:46:49Z","timestamp":1752810409000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-025-00944-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":67,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["944"],"URL":"https:\/\/doi.org\/10.1007\/s10844-025-00944-6","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"17 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}