{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:05:06Z","timestamp":1777982706811,"version":"3.51.4"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Hangzhou Key Scientific Research Program","award":["2025SZD1A50"],"award-info":[{"award-number":["2025SZD1A50"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402373"],"award-info":[{"award-number":["62402373"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s41060-026-01060-6","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:31:04Z","timestamp":1772721064000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Emotion-aware multimodal framework with similarity-guided gating for disaster misinformation detection"],"prefix":"10.1007","volume":"22","author":[{"given":"Feifan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jingge","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Haonan","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Chaohao","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"issue":"4","key":"1060_CR1","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s41060-022-00311-6","volume":"13","author":"S Muhammed\u00a0T","year":"2022","unstructured":"Muhammed\u00a0T, S., Mathew, S.K.: The disaster of misinformation: a review of research in social media. Int. J. Data Sci. Anal. 13(4), 271\u2013285 (2022)","journal-title":"Int. J. Data Sci. Anal."},{"issue":"1","key":"1060_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJISSC.303596","volume":"13","author":"J Vasudevan","year":"2022","unstructured":"Vasudevan, J., Alathur, S.: Misinformation in social media during disasters: a case study of the flood in Kerala, India in 2018. Int. J. Inf. Syst. Soc. Change (IJISSC) 13(1), 1\u201315 (2022)","journal-title":"Int. J. Inf. Syst. Soc. Change (IJISSC)"},{"issue":"2","key":"1060_CR3","doi-asserted-by":"publisher","first-page":"262","DOI":"10.3934\/publichealth.2022018","volume":"9","author":"MMF Caceres","year":"2022","unstructured":"Caceres, M.M.F., Sosa, J.P., Lawrence, J.A., Sestacovschi, C., Tidd-Johnson, A., Rasool, M.H.U., Gadamidi, V.K., Ozair, S., Pandav, K., Cuevas-Lou, C., et al.: The impact of misinformation on the Covid-19 pandemic. AIMS Public Health 9(2), 262 (2022)","journal-title":"AIMS Public Health"},{"key":"1060_CR4","doi-asserted-by":"publisher","first-page":"67515","DOI":"10.2196\/67515","volume":"9","author":"JP Stimpson","year":"2025","unstructured":"Stimpson, J.P., Srivastava, A., Tamirisa, K., Kaholokula, J.K., Ortega, A.N.: Crisis communication about the Maui wildfires on TikTok: content analysis of engagement with Maui wildfire-related posts over 1 year. JMIR Form. Res. 9, 67515 (2025)","journal-title":"JMIR Form. Res."},{"key":"1060_CR5","doi-asserted-by":"crossref","unstructured":"Pan, J.Z., Pavlova, S., Li, C., Li, N., Li, Y., Liu, J.: Content based fake news detection using knowledge graphs. In: International Semantic Web Conference, pp. 669\u2013683 (2018). Springer","DOI":"10.1007\/978-3-030-00671-6_39"},{"key":"1060_CR6","doi-asserted-by":"publisher","first-page":"114171","DOI":"10.1016\/j.eswa.2020.114171","volume":"169","author":"A Choudhary","year":"2021","unstructured":"Choudhary, A., Arora, A.: Linguistic feature based learning model for fake news detection and classification. Expert Syst. Appl. 169, 114171 (2021)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"1060_CR7","first-page":"5514220","volume":"2021","author":"WH Bangyal","year":"2021","unstructured":"Bangyal, W.H., Qasim, R., Rehman, N.U., Ahmad, Z., Dar, H., Rukhsar, L., Aman, Z., Ahmad, J.: Detection of fake news text classification on Covid-19 using deep learning approaches. Comput. Math. Methods Med. 2021(1), 5514220 (2021)","journal-title":"Comput. Math. Methods Med."},{"issue":"4","key":"1060_CR8","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s41060-021-00302-z","volume":"13","author":"S Raza","year":"2022","unstructured":"Raza, S., Ding, C.: Fake news detection based on news content and social contexts: a transformer-based approach. Int. J. Data Sci. Anal. 13(4), 335\u2013362 (2022)","journal-title":"Int. J. Data Sci. Anal."},{"key":"1060_CR9","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.patrec.2023.02.026","volume":"168","author":"Q Zhang","year":"2023","unstructured":"Zhang, Q., Guo, Z., Zhu, Y., Vijayakumar, P., Castiglione, A., Gupta, B.B.: A deep learning-based fast fake news detection model for cyber-physical social services. Pattern Recogn. Lett. 168, 31\u201338 (2023)","journal-title":"Pattern Recogn. Lett."},{"issue":"24","key":"1060_CR10","doi-asserted-by":"publisher","first-page":"21503","DOI":"10.1007\/s00521-021-06086-4","volume":"34","author":"B Singh","year":"2022","unstructured":"Singh, B., Sharma, D.K.: Predicting image credibility in fake news over social media using multi-modal approach. Neural Comput. Appl. 34(24), 21503\u201321517 (2022)","journal-title":"Neural Comput. Appl."},{"key":"1060_CR11","doi-asserted-by":"crossref","unstructured":"Qi, P., Cao, J., Yang, T., Guo, J., Li, J.: Exploiting multi-domain visual information for fake news detection. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 518\u2013527 (2019). IEEE","DOI":"10.1109\/ICDM.2019.00062"},{"issue":"1","key":"1060_CR12","first-page":"5","volume":"15","author":"Y Hamid","year":"2023","unstructured":"Hamid, Y., Elyassami, S., Gulzar, Y., Balasaraswathi, V.R., Habuza, T., Wani, S.: An improvised CNN model for fake image detection. Int. J. Inf. Technol. 15(1), 5\u201315 (2023)","journal-title":"Int. J. Inf. Technol."},{"key":"1060_CR13","doi-asserted-by":"crossref","unstructured":"Birunda, S.S., Nagaraj, P., Narayanan, S.K., Sudar, K.M., Muneeswaran, V., Ramana, R.: Fake image detection in twitter using flood fill algorithm and deep neural networks. In: 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 285\u2013290 (2022). IEEE","DOI":"10.1109\/Confluence52989.2022.9734208"},{"key":"1060_CR14","doi-asserted-by":"crossref","unstructured":"Giachanou, A., Zhang, G., Rosso, P.: Multimodal multi-image fake news detection. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 647\u2013654 (2020). IEEE","DOI":"10.1109\/DSAA49011.2020.00091"},{"key":"1060_CR15","doi-asserted-by":"publisher","first-page":"102652","DOI":"10.1016\/j.scs.2020.102652","volume":"66","author":"J Zeng","year":"2021","unstructured":"Zeng, J., Zhang, Y., Ma, X.: Fake news detection for epidemic emergencies via deep correlations between text and images. Sustain. Cities Soc. 66, 102652 (2021)","journal-title":"Sustain. Cities Soc."},{"issue":"2","key":"1060_CR16","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s10844-022-00764-y","volume":"61","author":"SK Uppada","year":"2023","unstructured":"Uppada, S.K., Patel, P., Sivaselvan, B.: An image and text-based multimodal model for detecting fake news in OSN\u2019s. J. Intell. Inf. Syst. 61(2), 367\u2013393 (2023)","journal-title":"J. Intell. Inf. Syst."},{"key":"1060_CR17","doi-asserted-by":"publisher","first-page":"8312","DOI":"10.1109\/TMM.2023.3330296","volume":"26","author":"X Gao","year":"2024","unstructured":"Gao, X., Wang, X., Chen, Z., Zhou, W., Hoi, S.C.: Knowledge enhanced vision and language model for multi-modal fake news detection. IEEE Trans. Multimed. 26, 8312\u20138322 (2024)","journal-title":"IEEE Trans. Multimed."},{"key":"1060_CR18","doi-asserted-by":"publisher","first-page":"102172","DOI":"10.1016\/j.inffus.2023.102172","volume":"104","author":"Z Qu","year":"2024","unstructured":"Qu, Z., Meng, Y., Muhammad, G., Tiwari, P.: QMFND: a quantum multimodal fusion-based fake news detection model for social media. Inf. Fus. 104, 102172 (2024)","journal-title":"Inf. Fus."},{"issue":"5","key":"1060_CR19","doi-asserted-by":"publisher","first-page":"6568","DOI":"10.1109\/TCSS.2024.3415160","volume":"11","author":"Y Bai","year":"2024","unstructured":"Bai, Y., Liu, Y., Li, Y.: Learning frequency-aware cross-modal interaction for multimodal fake news detection. IEEE Trans. Comput. Soc. Syst. 11(5), 6568\u20136579 (2024)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"2","key":"1060_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3381750","volume":"20","author":"B Ghanem","year":"2020","unstructured":"Ghanem, B., Rosso, P., Rangel, F.: An emotional analysis of false information in social media and news articles. ACM Trans. Internet Technol. (TOIT) 20(2), 1\u201318 (2020)","journal-title":"ACM Trans. Internet Technol. (TOIT)"},{"issue":"12","key":"1060_CR21","doi-asserted-by":"publisher","first-page":"6838","DOI":"10.1177\/14614448231153959","volume":"26","author":"J Lee","year":"2024","unstructured":"Lee, J., Hameleers, M., Shin, S.Y.: The emotional effects of multimodal disinformation: how multimodality, issue relevance, and anxiety affect misperceptions about the flu vaccine. New Media Soc. 26(12), 6838\u20136860 (2024)","journal-title":"New Media Soc."},{"key":"1060_CR22","doi-asserted-by":"crossref","unstructured":"Nourani, V., Molajou, A., Najafi, H., Danandeh Mehr, A.. In: Al-Turjman, F. (ed.) Emotional ANN (EANN): A New Generation of Neural Networks for Hydrological Modeling in IoT, pp. 45\u201361. Springer, Cham (2019)","DOI":"10.1007\/978-3-030-04110-6_3"},{"issue":"5","key":"1060_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3395046","volume":"53","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. (CSUR) 53(5), 1\u201340 (2020)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"8","key":"1060_CR24","doi-asserted-by":"publisher","first-page":"11765","DOI":"10.1007\/s11042-020-10183-2","volume":"80","author":"RK Kaliyar","year":"2021","unstructured":"Kaliyar, R.K., Goswami, A., Narang, P.: Fakebert: fake news detection in social media with a Bert-based deep learning approach. Multimed. Tools Appl. 80(8), 11765\u201311788 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"1060_CR25","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, C., Xu, H., Xu, Y., Xu, X., Wang, S.: Cross-modal contrastive learning for multimodal fake news detection. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 5696\u20135704 (2023)","DOI":"10.1145\/3581783.3613850"},{"key":"1060_CR26","doi-asserted-by":"crossref","unstructured":"Alam, F., Sajjad, H., Imran, M., Ofli, F.: Crisisbench: Benchmarking crisis-related social media datasets for humanitarian information processing. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 923\u2013932 (2021)","DOI":"10.1609\/icwsm.v15i1.18115"},{"key":"1060_CR27","doi-asserted-by":"crossref","unstructured":"Wang, B., Wang, F., Wang, J., Yan, H., Zhou, S., Li, C.: EL-FDL: improving image forgery detection and localization via ensemble learning. In: International Conference on Artificial Neural Networks, pp. 248\u2013262 (2024). Springer","DOI":"10.1007\/978-3-031-72335-3_17"},{"key":"1060_CR28","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114 (2019). PMLR"},{"key":"1060_CR29","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763 (2021). PMLR"},{"issue":"3","key":"1060_CR30","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1007\/s11263-018-1140-0","volume":"127","author":"B Zhou","year":"2019","unstructured":"Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., Torralba, A.: Semantic understanding of scenes through the ade20k dataset. Int. J. Comput. Vis. 127(3), 302\u2013321 (2019)","journal-title":"Int. J. Comput. Vis."},{"issue":"6","key":"1060_CR31","doi-asserted-by":"crossref","first-page":"103480","DOI":"10.1016\/j.ipm.2023.103480","volume":"60","author":"M Tufchi","year":"2023","unstructured":"Tufchi, M., Li, Y., Wang, J.: Multimodal coherence and persuasion in fake news: visual\u2013textual alignment in crisis misinformation. Inf. Process. Manag. 60(6), 103480 (2023)","journal-title":"Inf. Process. Manag."},{"key":"1060_CR32","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: a robustly optimized Bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"1060_CR33","doi-asserted-by":"crossref","unstructured":"Abdelnabi, S., Hasan, R., Fritz, M.: Open-domain, content-based, multi-modal fact-checking of out-of-context images via online resources. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14940\u201314949 (2022)","DOI":"10.1109\/CVPR52688.2022.01452"},{"key":"1060_CR34","first-page":"346","volume":"34","author":"M Agarwal","year":"2020","unstructured":"Agarwal, M., Leekha, M., Sawhney, R., Shah, R.R.: Crisis-dias: towards multimodal damage analysis-deployment, challenges and assessment. Proc. AAAI Conf. Artif. Intell. 34, 346\u2013353 (2020)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1060_CR35","doi-asserted-by":"publisher","first-page":"92889","DOI":"10.1109\/ACCESS.2022.3202976","volume":"10","author":"A Khattar","year":"2022","unstructured":"Khattar, A., Quadri, S.: CAMM: cross-attention multimodal classification of disaster-related tweets. IEEE Access 10, 92889\u201392902 (2022)","journal-title":"IEEE Access"},{"key":"1060_CR36","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. 1(2), 3. arXiv preprint arXiv:2204.06125 (2022)"},{"key":"1060_CR37","doi-asserted-by":"crossref","unstructured":"Qi, P., Yan, Z., Hsu, W., Lee, M.L.: Sniffer: multimodal large language model for explainable out-of-context misinformation detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13052\u201313062 (2024)","DOI":"10.1109\/CVPR52733.2024.01240"},{"key":"1060_CR38","first-page":"9694","volume":"34","author":"J Li","year":"2021","unstructured":"Li, J., Selvaraju, R., Gotmare, A., Joty, S., Xiong, C., Hoi, S.C.H.: Align before fuse: vision and language representation learning with momentum distillation. Adv. Neural. Inf. Process. Syst. 34, 9694\u20139705 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1060_CR39","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 427\u2013431 (2017)","DOI":"10.18653\/v1\/E17-2068"},{"key":"1060_CR40","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"1060_CR41","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"1060_CR42","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"1060_CR43","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: 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 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"1060_CR44","unstructured":"Nakamura, K., Levy, S., Wang, W.Y.: Fakeddit: a new multimodal benchmark dataset for fine-grained fake news detection. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 6149\u20136157 (2020)"},{"key":"1060_CR45","unstructured":"Malpani, V.: Crisis4C: Disaster Post Classification Dataset. https:\/\/www.kaggle.com\/datasets\/vaicml\/crisis4c-disaster-post-classification-dataset. Accessed 14 Feb 2025 (2023)"},{"key":"1060_CR46","doi-asserted-by":"crossref","unstructured":"Alam, F., Ofli, F., Imran, M.: Crisismmd: multimodal twitter datasets from natural disasters. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 12 (2018)","DOI":"10.1609\/icwsm.v12i1.14983"},{"key":"1060_CR47","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1060_CR48","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1060_CR49","unstructured":"Kim, W., Son, B., Kim, I.: Vilt: vision-and-language transformer without convolution or region supervision. In: International Conference on Machine Learning, pp. 5583\u20135594 (2021). PMLR"},{"key":"1060_CR50","unstructured":"Li, J., Li, D., Xiong, C., Hoi, S.: Blip: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning, pp. 12888\u201312900 (2022). PMLR"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-026-01060-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-026-01060-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-026-01060-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:31:13Z","timestamp":1772721073000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-026-01060-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,5]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["1060"],"URL":"https:\/\/doi.org\/10.1007\/s41060-026-01060-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-8405470\/v1","asserted-by":"object"}]},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,5]]},"assertion":[{"value":"19 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2026","order":3,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"89"}}