{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T12:12:45Z","timestamp":1769775165742,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556397","type":"print"},{"value":"9789819556403","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-5640-3_12","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:08:08Z","timestamp":1769720888000},"page":"175-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Dual-Stage Framework Integrating LLM Summarization and Knowledge Graph Embeddings for Health Misinformation Detection"],"prefix":"10.1007","author":[{"given":"Zhiteng","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongxuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiyun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1080\/10410236.2021.2019920","volume":"38","author":"LH Le","year":"2023","unstructured":"Le, L.H., Hoang, P.A., Pham, H.C.: Sharing health information across online platforms: a systematic review. Health Commun. 38, 1550\u20131562 (2023). https:\/\/doi.org\/10.1080\/10410236.2021.2019920","journal-title":"Health Commun."},{"key":"12_CR2","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.7326\/m23-1218","volume":"176","author":"HS Lalani","year":"2023","unstructured":"Lalani, H.S., DiResta, R., Baron, R.J., Scales, D.: Addressing viral medical rumors and false or misleading information. Ann. Intern. Med. 176, 1113\u20131120 (2023). https:\/\/doi.org\/10.7326\/m23-1218","journal-title":"Ann. Intern. Med."},{"key":"12_CR3","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1111\/hir.12320","volume":"38","author":"SB Naeem","year":"2021","unstructured":"Naeem, S.B., Bhatti, R., Khan, A.: An exploration of how fake news is taking over social media and putting public health at risk. Health Info. Libr. J. 38, 143\u2013149 (2021). https:\/\/doi.org\/10.1111\/hir.12320","journal-title":"Health Info. Libr. J."},{"key":"12_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102769","volume":"59","author":"Y Li","year":"2022","unstructured":"Li, Y., Fan, Z., Yuan, X., Zhang, X.: Recognizing fake information through a developed feature scheme: a user study of health misinformation on social media in China. Inf. Process. Manag. 59, 102769 (2022). https:\/\/doi.org\/10.1016\/j.ipm.2021.102769","journal-title":"Inf. Process. Manag."},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"A\u00efmeur, E., Amri, S., Brassard, G.: Fake news, disinformation and misinformation in social media: a review. Soc. Netw. Anal. Min. 13 (2023). https:\/\/doi.org\/10.1007\/s13278-023-01028-5","DOI":"10.1007\/s13278-023-01028-5"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Shu, K., Cui, L., Wang, S., Lee, D., Liu, H.: DEFEND: explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 395\u2013405. ACM, New York, NY, USA (2019)","DOI":"10.1145\/3292500.3330935"},{"key":"12_CR7","doi-asserted-by":"publisher","unstructured":"Sharma, R., Arya, A.: LFWE: linguistic feature based word embedding for Hindi fake news detection. ACM Trans. Asian Low-resour. Lang. Inf. Process. 22, 1\u201324 (2023). https:\/\/doi.org\/10.1145\/3589764","DOI":"10.1145\/3589764"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Li, Y., He, H., Bai, J., Wen, D.: MCFEND: a multi-source benchmark dataset for Chinese fake news detection. In: Proceedings of the ACM Web Conference 2024, pp. 4018\u20134027. ACM, New York, NY, USA (2024)","DOI":"10.1145\/3589334.3645385"},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Yang, S., Shu, K., Wang, S., Gu, R., Wu, F., Liu, H.: Unsupervised fake news detection on social media: a generative approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5644\u20135651 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33015644","DOI":"10.1609\/aaai.v33i01.33015644"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 549\u2013556 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i01.5393","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"12_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106991","volume":"101","author":"MH Goldani","year":"2021","unstructured":"Goldani, M.H., Momtazi, S., Safabakhsh, R.: Detecting fake news with capsule neural networks. Appl. Soft Comput. 101, 106991 (2021). https:\/\/doi.org\/10.1016\/j.asoc.2020.106991","journal-title":"Appl. Soft Comput."},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Dou, Y., Shu, K., Xia, C., Yu, P.S., Sun, L.: User preference-aware fake news detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2051\u20132055. ACM, New York, NY, USA (2021)","DOI":"10.1145\/3404835.3462990"},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Yin, S., Zhu, P., Wu, L., Gao, C., Wang, Z.: GAMC: an unsupervised method for fake news detection using graph autoencoder with masking. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 347\u2013355 (2024). https:\/\/doi.org\/10.1609\/aaai.v38i1.27788","DOI":"10.1609\/aaai.v38i1.27788"},{"key":"12_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3533431","volume":"23","author":"M-Y Chen","year":"2023","unstructured":"Chen, M.-Y., Lai, Y.-W., Lian, J.-W.: Using deep learning models to detect fake news about COVID-19. ACM Trans. Internet Technol. 23, 1\u201323 (2023). https:\/\/doi.org\/10.1145\/3533431","journal-title":"ACM Trans. Internet Technol."},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Ma, J., Chen, C., Hou, C., Yuan, X.: KAPALM: knowledge graph enhanced language models for fake news detection. In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 3999\u20134009. Association for Computational Linguistics, Stroudsburg, PA, USA (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.263"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Dun, Y., Tu, K., Chen, C., Hou, C., Yuan, X.: KAN: knowledge-aware Attention Network for fake news detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 81\u201389 (2021)","DOI":"10.1609\/aaai.v35i1.16080"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Mayank, M., Sharma, S., Sharma, R.: DEAP-FAKED: knowledge graph based approach for fake news detection. In: 2022 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 47\u201351. IEEE (2022)","DOI":"10.1109\/ASONAM55673.2022.10068653"},{"key":"12_CR18","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1109\/tce.2023.3324661","volume":"70","author":"B Xie","year":"2024","unstructured":"Xie, B., Ma, X., Wu, J., Yang, J., Fan, H.: Knowledge graph enhanced heterogeneous graph neural network for fake news detection. IEEE Trans. Consum. Electron. 70, 2826\u20132837 (2024). https:\/\/doi.org\/10.1109\/tce.2023.3324661","journal-title":"IEEE Trans. Consum. Electron."},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Liu, C., Song, J.: Research on cross-domain fake news detection based on multi-space fusion and knowledge graph embedding. In: Proceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy, pp. 113\u2013117. ACM, New York, NY, USA (2024)","DOI":"10.1145\/3672919.3672941"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Upadhyay, R., Pasi, G., Viviani, M.: Leveraging Socio-contextual information in BERT for fake health news detection in social media. In: 3rd International Workshop on Open Challenges in Online Social Networks, pp. 38\u201346. ACM, New York, NY, USA (2023)","DOI":"10.1145\/3599696.3612902"},{"key":"12_CR21","doi-asserted-by":"publisher","unstructured":"Dai, E., Sun, Y., Wang, S.: Ginger cannot cure cancer: battling fake health news with a comprehensive data repository. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 853\u2013862 (2020). https:\/\/doi.org\/10.1609\/icwsm.v14i1.7350","DOI":"10.1609\/icwsm.v14i1.7350"},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Mahara, T., Josephine, V.L.H., Srinivasan, R., Prakash, P., Algarni, A.D., Verma, O.P.: Deep vs. shallow: a comparative study of machine learning and deep learning approaches for fake health news detection. IEEE Access 11, 79330\u201379340 (2023). https:\/\/doi.org\/10.1109\/access.2023.3298441","DOI":"10.1109\/access.2023.3298441"},{"key":"12_CR23","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. ICML. abs\/1606.06357, 2071\u20132080 (20\u201322 June 2016)"},{"key":"12_CR24","unstructured":"Chen, Y.: Convolutional neural network for sentence classification. Master\u2019s thesis, University of Waterloo (2015)"},{"key":"12_CR25","doi-asserted-by":"publisher","unstructured":"Liu, P., Qiu, X., Huang, X.: Recurrent neural network for text classification with multi-task learning (2016). http:\/\/arxiv.org\/abs\/1605.05101, https:\/\/doi.org\/10.48550\/ARXIV.1605.05101","DOI":"10.48550\/ARXIV.1605.05101"},{"key":"12_CR26","doi-asserted-by":"publisher","unstructured":"Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016). https:\/\/doi.org\/10.18653\/v1\/P16-2034","DOI":"10.18653\/v1\/P16-2034"},{"key":"12_CR27","doi-asserted-by":"publisher","unstructured":"Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015). https:\/\/doi.org\/10.1609\/aaai.v29i1.9513","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"12_CR28","unstructured":"Armand, J., Edouard, G., Piotr, B., Tomas, M.: Bag of tricks for efficient text classification (2016). http:\/\/arxiv.org\/abs\/1607.01759"},{"key":"12_CR29","doi-asserted-by":"crossref","unstructured":"Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Barzilay, R., Kan, M.-Y. (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 562\u2013570. Association for Computational Linguistics, Stroudsburg, PA, USA (2017)","DOI":"10.18653\/v1\/P17-1052"},{"key":"12_CR30","unstructured":"Vaswani, A., et al.: Attention is all you need. Neural Inf. Process. Syst. 5998\u20136008 (2017)"},{"key":"12_CR31","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: Burstein, J., Doran, C., and Solorio, T. (eds.), Proceedings of the 2019 Conference of the North, pp. 4171\u20134186. Association for Computational Linguistics, Stroudsburg, PA, USA (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Xu, C., Kechadi, M.-T.: Fuzzy deep hybrid network for fake news detection. In: Proceedings of the 12th International Symposium on Information and Communication Technology, pp. 118\u2013125. ACM, New York, NY, USA (2023)","DOI":"10.1145\/3628797.3628971"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5640-3_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:08:13Z","timestamp":1769720893000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5640-3_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556397","9789819556403"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5640-3_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"28 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb2025.sau.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}