{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T13:25:43Z","timestamp":1786281943420,"version":"3.56.0"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819620531","type":"print"},{"value":"9789819620548","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-2054-8_7","type":"book-chapter","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T15:48:30Z","timestamp":1735832910000},"page":"86-100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for\u00a0Fake News Detection"],"prefix":"10.1007","author":[{"given":"Xiaoman","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangrun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taihang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"7_CR1","series-title":"Lecture Notes on Data Engineering and Communications Technologies","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/978-981-16-2937-2_9","volume-title":"Data Management, Analytics and Innovation","author":"S Bhatt","year":"2022","unstructured":"Bhatt, S., Goenka, N., Kalra, S., Sharma, Y.: Fake news detection: experiments and approaches beyond linguistic features. In: Sharma, N., Chakrabarti, A., Balas, V.E., Bruckstein, A.M. (eds.) Data Management, Analytics and Innovation. LNDECT, vol. 71, pp. 113\u2013128. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-2937-2_9"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675\u2013684 (2011)","DOI":"10.1145\/1963405.1963500"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Cross-modal ambiguity learning for multimodal fake news detection. In: Proceedings of the ACM web conference 2022, pp. 2897\u20132905 (2022)","DOI":"10.1145\/3485447.3511968"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Feng, S., Sun, G., Lubis, N., Zhang, C., Ga\u0161i\u0107, M.: Affect recognition in conversations using large language models. arXiv preprint arXiv:2309.12881 (2023)","DOI":"10.18653\/v1\/2024.sigdial-1.23"},{"key":"7_CR5","unstructured":"Fersini, E., Armanini, J., D\u2019Intorni, M., et\u00a0al.: Profiling fake news spreaders: stylometry, personality, emotions and embeddings. In: CLEF (Working Notes) (2020)"},{"issue":"9","key":"7_CR6","first-page":"1117","volume":"72","author":"A Giachanou","year":"2021","unstructured":"Giachanou, A., Rosso, P., Crestani, F.: The impact of emotional signals on credibility assessment. J. Am. Soc. Inf. Sci. 72(9), 1117\u20131132 (2021)","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"7_CR7","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.: PTR: prompt tuning with rules for text classification. AI Open 3, 182\u2013192 (2022)","journal-title":"AI Open"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Hu, B., et al.: Bad actor, good advisor: Exploring the role of large language models in fake news detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 22105\u201322113 (2024)","DOI":"10.1609\/aaai.v38i20.30214"},{"key":"7_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107967","volume":"101","author":"C Iwendi","year":"2022","unstructured":"Iwendi, C., Mohan, S., Ibeke, E., Ahmadian, A., Ciano, T., et al.: Covid-19 fake news sentiment analysis. Comput. Electr. Eng. 101, 107967 (2022)","journal-title":"Comput. Electr. Eng."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Jiang, Y.: Team Qust at Semeval-2023 task 3: a comprehensive study of monolingual and multilingual approaches for detecting online news genre, framing and persuasion techniques. arXiv preprint arXiv:2304.04190 (2023)","DOI":"10.18653\/v1\/2023.semeval-1.40"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Wang, T., Xu, X., Wang, Y., Song, X., Maynard, D.: Cross-modal augmentation for few-shot multimodal fake news detection. arXiv preprint arXiv:2407.12880 (2024)","DOI":"10.1016\/j.engappai.2024.109931"},{"key":"7_CR12","unstructured":"Jiang, Y., Wang, Y.: Large visual-language models are also good classifiers: a study of in-context multimodal fake news detection. arXiv preprint arXiv:2407.12879 (2024)"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Wang, Y., Song, X., Maynard, D.: Comparing topic-aware neural networks for bias detection of news. In: ECAI 2020, pp. 2054\u20132061. IOS Press (2020)","DOI":"10.3233\/FAIA200327"},{"key":"7_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119446","volume":"647","author":"Y Jiang","year":"2023","unstructured":"Jiang, Y., Yu, X., Wang, Y., Xu, X., Song, X., Maynard, D.: Similarity-aware multimodal prompt learning for fake news detection. Inf. Sci. 647, 119446 (2023)","journal-title":"Inf. Sci."},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Lee, N., Bang, Y., Madotto, A., Khabsa, M., Fung, P.: Towards few-shot fact-checking via perplexity. arXiv preprint arXiv:2103.09535 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.158"},{"key":"7_CR16","unstructured":"Li, C., et\u00a0al.: SentiPrompt: sentiment knowledge enhanced prompt-tuning for aspect-based sentiment analysis. arXiv preprint arXiv:2109.08306 (2021)"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Li, J., Xiao, L.: Multi-emotion recognition using multi-emobert and emotion analysis in fake news. In: Proceedings of the 15th ACM Web Science Conference 2023, pp. 128\u2013135 (2023)","DOI":"10.1145\/3578503.3583595"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, T., Yang, K., Thompson, P., Yu, Z., Ananiadou, S.: Emotion detection for misinformation: a review. Inf. Fusion 107, 102300 (2024)","DOI":"10.1016\/j.inffus.2024.102300"},{"issue":"4","key":"7_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103354","volume":"60","author":"AM Luvembe","year":"2023","unstructured":"Luvembe, A.M., Li, W., Li, S., Liu, F., Xu, G.: Dual emotion based fake news detection: a deep attention-weight update approach. Inf. Process. Manag. 60(4), 103354 (2023)","journal-title":"Inf. Process. Manag."},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Ma, R., et al.: Template-free prompt tuning for few-shot NER. arXiv preprint arXiv:2109.13532 (2021)","DOI":"10.18653\/v1\/2022.naacl-main.420"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Ma, Y., Cao, Y., Hong, Y., Sun, A.: Large language model is not a good few-shot information extractor, but a good reranker for hard samples! (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.710"},{"issue":"1","key":"7_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41235-020-00252-3","volume":"5","author":"C Martel","year":"2020","unstructured":"Martel, C., Pennycook, G., Rand, D.G.: Reliance on emotion promotes belief in fake news. Cognit. Res. Principles Implications 5(1), 1\u201320 (2020). https:\/\/doi.org\/10.1186\/s41235-020-00252-3","journal-title":"Cognit. Res. Principles Implications"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Richler, J.: Social pressure to share fake news. vol.\u00a02, pp. 265. Nature Publishing Group US New York (2023)","DOI":"10.1038\/s44159-023-00183-y"},{"key":"7_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2022.107619","volume":"141","author":"A Rijo","year":"2023","unstructured":"Rijo, A., Waldzus, S.: That\u2019s interesting! the role of epistemic emotions and perceived credibility in the relation between prior beliefs and susceptibility to fake-news. Comput. Hum. Behav. 141, 107619 (2023)","journal-title":"Comput. Hum. Behav."},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: Exploiting cloze questions for few shot text classification and natural language inference. arXiv preprint arXiv:2001.07676 (2020)","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"issue":"3","key":"7_CR27","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1089\/big.2020.0062","volume":"8","author":"K Shu","year":"2020","unstructured":"Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data 8(3), 171\u2013188 (2020)","journal-title":"Big Data"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Singhal, S., Shah, R.R., Chakraborty, T., Kumaraguru, P., Satoh, S.: SpotFake: a multi-modal framework for fake news detection. In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 39\u201347. IEEE (2019)","DOI":"10.1109\/BigMM.2019.00-44"},{"key":"7_CR29","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Tuan, M.V., Budo, K., Tuan, C.A., Takeshita, T.: Analysis of unidirectional isolated Y-Y connection three-phase DC-DC converter. In: 2021 IEEE International Future Energy Electronics Conference (IFEEC), pp.\u00a01\u20136 (2021)","DOI":"10.1109\/IFEEC53238.2021.9661684"},{"key":"7_CR31","unstructured":"Wang, T., Xu, X., Wang, Y., Jiang, Y.: Instruction tuning vs. in-context learning: revisiting large language models in few-shot computational social science. arXiv preprint arXiv:2409.14673 (2024)"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"Wu, J., Li, S., Deng, A., Xiong, M., Hooi, B.: Prompt-and-align: prompt-based social alignment for few-shot fake news detection. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 2726\u20132736 (2023)","DOI":"10.1145\/3583780.3615015"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Xu, X., Li, X., Wang, T., Tian, J., Jiang, Y.: Team Qust at Semeval-2024 task 8: a comprehensive study of monolingual and multilingual approaches for detecting AI-generated text. arXiv preprint arXiv:2402.11934 (2024)","DOI":"10.18653\/v1\/2024.semeval-1.71"},{"key":"7_CR34","doi-asserted-by":"crossref","unstructured":"Yang, F., Liu, Y., Yu, X., Yang, M.: Automatic detection of rumor on Sina Weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, pp.\u00a01\u20137 (2012)","DOI":"10.1145\/2350190.2350203"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, B., Yang, H., Zhou, T., Ali\u00a0Babar, M., Liu, X.Y.: Enhancing financial sentiment analysis via retrieval augmented large language models. In: Proceedings of the Fourth ACM International Conference on AI in Finance, pp. 349\u2013356 (2023)","DOI":"10.1145\/3604237.3626866"},{"issue":"2","key":"7_CR36","first-page":"493","volume":"25","author":"L Zhou","year":"2023","unstructured":"Zhou, L., Tao, J., Zhang, D.: Does fake news in different languages tell the same story? An analysis of multi-level thematic and emotional characteristics of news about covid-19. Inf. Syst. Front. 25(2), 493\u2013512 (2023)","journal-title":"Inf. Syst. Front."},{"key":"7_CR37","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1007\/978-3-030-47436-2_27","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Wu, J., Zafarani, R.: $$\\sf SAFE$$: Similarity-aware multi-modal fake news detection. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 354\u2013367. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47436-2_27"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2054-8_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T01:44:13Z","timestamp":1742694253000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2054-8_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819620531","9789819620548"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2054-8_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nara","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 January 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mmm2025.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}