{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T19:35:43Z","timestamp":1762544143698,"version":"3.44.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72074117"],"award-info":[{"award-number":["72074117"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s10489-025-06560-9","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T06:55:22Z","timestamp":1744613722000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MGMP: Multi-granularity semantic relation learning and meta-path structure interaction learning for fake news detection"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6160-1390","authenticated-orcid":false,"given":"Baozhen","family":"Lee","sequence":"first","affiliation":[]},{"given":"Dandan","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Tingting","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"issue":"1","key":"6560_CR1","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: A data mining perspective. SIGKDD Explor Newsl 19(1):22\u201336. https:\/\/doi.org\/10.1145\/3137597.3137600","journal-title":"SIGKDD Explor Newsl"},{"key":"6560_CR2","doi-asserted-by":"publisher","unstructured":"Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web. WWW\u201911, pp 675\u2013684. https:\/\/doi.org\/10.1145\/1963405.1963500","DOI":"10.1145\/1963405.1963500"},{"issue":"5","key":"6560_CR3","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1002\/asi.23216","volume":"66","author":"VL Rubin","year":"2015","unstructured":"Rubin VL, Lukoianova T (2015) Truth and deception at the rhetorical structure level. J Assoc Inf Sci Technol 66(5):905\u2013917. https:\/\/doi.org\/10.1002\/asi.23216","journal-title":"J Assoc Inf Sci Technol"},{"key":"6560_CR4","doi-asserted-by":"publisher","unstructured":"Potthast M, Kiesel J, Reinartz K, Bevendorff J, Stein B (2018) A stylometric inquiry into hyperpartisan and fake news. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 231\u2013240. https:\/\/doi.org\/10.18653\/v1\/P18-1022","DOI":"10.18653\/v1\/P18-1022"},{"key":"6560_CR5","doi-asserted-by":"publisher","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). EMNLP\u201914, pp 1746\u20131751. https:\/\/doi.org\/10.3115\/v1\/D14-1181","DOI":"10.3115\/v1\/D14-1181"},{"key":"6560_CR6","doi-asserted-by":"publisher","unstructured":"Liu P, Qiu X, Huang X (2016) Recurrent neural network for text classification with multi-task learning. In: Proceedings of the 25th international joint conference on artificial intelligence. IJCAI\u201916, pp 2873\u20132879. https:\/\/doi.org\/10.48550\/arXiv.1605.05101","DOI":"10.48550\/arXiv.1605.05101"},{"key":"6560_CR7","doi-asserted-by":"publisher","unstructured":"Hu L, Yang T, Zhang L, Zhong W, Tang D, Shi C, Duan N, Zhou M (2021) Compare to the knowledge: Graph neural fake news detection with external knowledge. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, pp 754\u2013763. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.62","DOI":"10.18653\/v1\/2021.acl-long.62"},{"key":"6560_CR8","doi-asserted-by":"publisher","unstructured":"Koloski B, Stepi\u0161nik\u00a0Perdih T, Robnik-\u0160ikonja M, Pollak S, \u0160krlj B (2022) Knowledge graph informed fake news classification via heterogeneous representation ensembles. Neurocomput 496(C)208\u2013226. https:\/\/doi.org\/10.1016\/j.neucom.2022.01.096","DOI":"10.1016\/j.neucom.2022.01.096"},{"key":"6560_CR9","doi-asserted-by":"publisher","unstructured":"Popat K, Mukherjee S, Str\u00f6tgen J, Weikum G (2018) Credeye: A credibility lens for analyzing and explaining misinformation. In: Companion proceedings of the the web conference 2018. WWW\u201918, pp 155\u2013158. https:\/\/doi.org\/10.1145\/3184558.3186967","DOI":"10.1145\/3184558.3186967"},{"key":"6560_CR10","doi-asserted-by":"publisher","unstructured":"Cheng M, Nazarian S, Bogdan P (2020) Vroc: Variational autoencoder-aided multi-task rumor classifier based on text. In: Proceedings of the web conference 2020. WWW\u201920, pp 2892\u20132898. https:\/\/doi.org\/10.1145\/3366423.3380054","DOI":"10.1145\/3366423.3380054"},{"key":"6560_CR11","doi-asserted-by":"publisher","unstructured":"Xiao L, Zhang Q, Shi C, Wang S, Naseem U, Hu L (2024) Msynfd: Multi-hop syntax aware fake news detection. In: Proceedings of the ACM on web conference 2024. WWW\u201924, pp 4128\u20134137. https:\/\/doi.org\/10.1145\/3589334.3645468","DOI":"10.1145\/3589334.3645468"},{"key":"6560_CR12","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang MW, 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, Vol 1 (Long and Short Papers), pp 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"6560_CR13","doi-asserted-by":"publisher","unstructured":"Zhuang L, Wayne L, Ya S, Jun Z (2021) A robustly optimized BERT pre-training approach with post-training. In: Proceedings of the 20th Chinese national conference on computational linguistics, pp 1218\u20131227. https:\/\/doi.org\/10.1007\/978-3-030-84186-7_31","DOI":"10.1007\/978-3-030-84186-7_31"},{"key":"6560_CR14","doi-asserted-by":"publisher","unstructured":"Zhang X, Cao J, Li X, Sheng Q, Zhong L, Shu K (2021) Mining dual emotion for fake news detection. In: Proceedings of the web conference 2021. WWW\u201921, pp 3465\u20133476. https:\/\/doi.org\/10.1145\/3442381.3450004","DOI":"10.1145\/3442381.3450004"},{"issue":"8","key":"6560_CR15","doi-asserted-by":"publisher","first-page":"11765","DOI":"10.1007\/S11042-020-10183-2","volume":"80","author":"RK Kaliyar","year":"2021","unstructured":"Kaliyar RK, Goswami A, Narang P (2021) Fakebert: Fake news detection in social media with a bert-based deep learning approach. Multim Tools Appl 80(8):11765\u201311788. https:\/\/doi.org\/10.1007\/S11042-020-10183-2","journal-title":"Multim Tools Appl"},{"key":"6560_CR16","doi-asserted-by":"publisher","unstructured":"Mosallanezhad A, Karami M, Shu K, Mancenido MV, Liu H (2022) Domain adaptive fake news detection via reinforcement learning. In: Proceedings of the ACM web conference 2022. WWW\u201922, pp 3632\u20133640. https:\/\/doi.org\/10.1145\/3485447.3512258","DOI":"10.1145\/3485447.3512258"},{"key":"6560_CR17","doi-asserted-by":"publisher","unstructured":"Sheng Q, Cao J, Zhang X, Li R, Wang D, Zhu Y (2022) Zoom out and observe: News environment perception for fake news detection. In: Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 4543\u20134556. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.311","DOI":"10.18653\/v1\/2022.acl-long.311"},{"key":"6560_CR18","doi-asserted-by":"publisher","unstructured":"Hu B, Sheng Q, Cao J, Shi Y, Li Y, Wang D, Qi P (2024) Bad actor, good advisor: exploring the role of large language models in fake news detection. In: Proceedings of the 38th AAAI conference on artificial intelligence and 36th conference on innovative applications of artificial intelligence and 14th symposium on educational advances in artificial intelligence. AAAI\u201924\/IAAI\u201924\/EAAI\u201924. https:\/\/doi.org\/10.1609\/aaai.v38i20.30214","DOI":"10.1609\/aaai.v38i20.30214"},{"key":"6560_CR19","doi-asserted-by":"publisher","unstructured":"Nan Q, Sheng Q, Cao J, Hu B, Wang D, Li J (2024) Let silence speak: Enhancing fake news detection with generated comments from large language models. In: Proceedings of the 33rd ACM international conference on information and knowledge management. CIKM\u201924, pp 1732\u20131742. https:\/\/doi.org\/10.1145\/3627673.3679519","DOI":"10.1145\/3627673.3679519"},{"key":"6560_CR20","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, Rozi\u00e8re B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A, Grave E, Lample G (2023) Llama: Open and efficient foundation language models. ArXiv preprint abs\/2302.13971"},{"key":"6560_CR21","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 34:549\u2013556. https:\/\/doi.org\/10.1609\/aaai.v34i01.5393","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"6560_CR22","unstructured":"Chandra S, Mishra P, Yannakoudakis H, Nimishakavi M, Saeidi M, Shutova E (2020) Graph-based modeling of online communities for fake news detection. arXiv preprint arXiv:2008.06274"},{"key":"6560_CR23","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1609\/aaai.v38i1.27788","volume":"38","author":"S Yin","year":"2024","unstructured":"Yin S, Zhu P, Wu L, Gao C, Wang Z (2024) Gamc: an unsupervised method for fake news detection using graph autoencoder with masking. Proceedings of the AAAI conference on artificial intelligence 38:347\u2013355. https:\/\/doi.org\/10.1609\/aaai.v38i1.27788","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"6560_CR24","doi-asserted-by":"crossref","unstructured":"Jin Y, Wang X, Yang R, Sun Y, Wang W, Liao H, Xie X (2022) Towards fine-grained reasoning for fake news detection. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 5746\u20135754. arXiv2110.15064","DOI":"10.1609\/aaai.v36i5.20517"},{"key":"6560_CR25","doi-asserted-by":"publisher","unstructured":"Su X, Yang J, Wu J, Zhang Y (2023) Mining user-aware multi-relations for fake news detection in large scale online social networks. In: Proceedings of the 16th ACM international conference on web search and data mining. WSDM\u201923, pp 51\u201359. https:\/\/doi.org\/10.1145\/3539597.3570478","DOI":"10.1145\/3539597.3570478"},{"key":"6560_CR26","doi-asserted-by":"publisher","unstructured":"Huang Q, Yu J, Wu J, Wang B (2020) Heterogeneous graph attention networks for early detection of rumors on twitter. In: 2020 international joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207582","DOI":"10.1109\/IJCNN48605.2020.9207582"},{"key":"6560_CR27","doi-asserted-by":"publisher","unstructured":"Cui J, Kim K, Na SH, Shin S (2022) Meta-path-based fake news detection leveraging multi-level social context information. In: Proceedings of the 31st ACM international conference on information & knowledge management. CIKM\u201922, pp 325\u2013334. https:\/\/doi.org\/10.1145\/3511808.3557394","DOI":"10.1145\/3511808.3557394"},{"key":"6560_CR28","doi-asserted-by":"publisher","unstructured":"Yu F, Liu Q, Wu S, Wang L, Tan T (2017) A convolutional approach for misinformation identification. In: Proceedings of the 26th international joint conference on artificial intelligence. IJCAI\u201917, pp 3901\u20133907. https:\/\/doi.org\/10.24963\/ijcai.2017\/545","DOI":"10.24963\/ijcai.2017\/545"},{"key":"6560_CR29","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.neucom.2022.01.096","volume":"496","author":"B Koloski","year":"2022","unstructured":"Koloski B, Stepi\u0161nik Perdih T, Robnik-\u0160ikonja M, Pollak S, \u0160krlj B (2022) Knowledge graph informed fake news classification via heterogeneous representation ensembles. Neurocomputing 496:208\u2013226. https:\/\/doi.org\/10.1016\/j.neucom.2022.01.096","journal-title":"Neurocomputing"},{"key":"6560_CR30","doi-asserted-by":"publisher","unstructured":"Hu B, Sheng Q, Cao J, Zhu Y, Wang D, Wang Z, Jin Z (2023) Learn over past, evolve for future: Forecasting temporal trends for fake news detection. In: Proceedings of the 61st annual meeting of the association for computational linguistics (Volume 5: Industry Track), pp 116\u2013125. https:\/\/doi.org\/10.18653\/v1\/2023.acl-industry.13","DOI":"10.18653\/v1\/2023.acl-industry.13"},{"issue":"7","key":"6560_CR31","doi-asserted-by":"publisher","first-page":"7178","DOI":"10.1109\/TKDE.2022.3185151","volume":"35","author":"Y Zhu","year":"2023","unstructured":"Zhu Y, Sheng Q, Cao J, Nan Q, Shu K, Wu M, Wang J, Zhuang F (2023) Memory-guided multi-view multi-domain fake news detection. IEEE Trans Knowl Data Eng 35(7):7178\u20137191. https:\/\/doi.org\/10.1109\/TKDE.2022.3185151","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6560_CR32","doi-asserted-by":"publisher","unstructured":"Liu F, Zhang X, Liu Q (2023) An emotion-aware approach for fake news detection. IEEE Trans Comput Soc Syst 1\u20139. https:\/\/doi.org\/10.1109\/TCSS.2023.3335269","DOI":"10.1109\/TCSS.2023.3335269"},{"key":"6560_CR33","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.patrec.2024.04.007","volume":"182","author":"V Indu","year":"2024","unstructured":"Indu V, Thampi SM (2024) Misinformation detection in social networks using emotion analysis and user behavior analysis. Patt Recognit Lett 182:60\u201366. https:\/\/doi.org\/10.1016\/j.patrec.2024.04.007","journal-title":"Patt Recognit Lett"},{"key":"6560_CR34","doi-asserted-by":"publisher","unstructured":"Zhang Y, Ma X, Wu J, Yang J, Fan H (2024) Heterogeneous subgraph transformer for fake news detection. In: Proceedings of the ACM web conference 2024. WWW\u201924, pp 1272\u20131282. https:\/\/doi.org\/10.1145\/3589334.3645680","DOI":"10.1145\/3589334.3645680"},{"key":"6560_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109240","volume":"137","author":"J Alghamdi","year":"2024","unstructured":"Alghamdi J, Lin Y, Luo S (2024) Unveiling the hidden patterns: A novel semantic deep learning approach to fake news detection on social media. Eng Appl Artif Intell 137:109240. https:\/\/doi.org\/10.1016\/j.engappai.2024.109240","journal-title":"Eng Appl Artif Intell"},{"key":"6560_CR36","doi-asserted-by":"publisher","unstructured":"Rakib\u00a0Mollah MA, Kabir MMJ, Kabir M, Reza MS (2023) Detection of fake news with roberta based embedding and modified deep neural network architecture. In: 2023 26th international conference on computer and information technology (ICCIT), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICCIT60459.2023.10441206","DOI":"10.1109\/ICCIT60459.2023.10441206"},{"key":"6560_CR37","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.patrec.2024.02.014","volume":"180","author":"Z Zhang","year":"2024","unstructured":"Zhang Z, Lv Q, Jia X, Yun W, Miao G, Mao Z, Wu G (2024) Gbca: Graph convolution network and bert combined with co-attention for fake news detection. Patt Recognit Lett 180:26\u201332. https:\/\/doi.org\/10.1016\/j.patrec.2024.02.014","journal-title":"Patt Recognit Lett"},{"key":"6560_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128263","volume":"602","author":"KM Karao\u011flan","year":"2024","unstructured":"Karao\u011flan KM (2024) Novel approaches for fake news detection based on attention-based deep multiple-instance learning using contextualized neural language models. Neurocomputing 602:128263. https:\/\/doi.org\/10.1016\/j.neucom.2024.128263","journal-title":"Neurocomputing"},{"key":"6560_CR39","doi-asserted-by":"publisher","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations, ICLR, Toulon, France, April 24-26, 2017, Conference track proceedings. https:\/\/doi.org\/10.48550\/arXiv:1609.02907","DOI":"10.48550\/arXiv:1609.02907"},{"key":"6560_CR40","unstructured":"Velikovi P, Cucurull G, Casanova A, Romero A, Li\u00f3 P, Bengio Y (2018) Graph attention networks. Int Conf Learn Represent. arXiv1710.10903"},{"key":"6560_CR41","doi-asserted-by":"publisher","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems. NIPS\u201917, vol 30, pp 1025\u20131035. https:\/\/doi.org\/10.48550\/arXiv:1706.02216","DOI":"10.48550\/arXiv:1706.02216"},{"key":"6560_CR42","doi-asserted-by":"crossref","unstructured":"Han Y, Karunasekera S, Leckie C (2020) Graph neural networks with continual learning for fake news detection from social media. arXiv:2007.03316","DOI":"10.1007\/978-3-030-86340-1_30"},{"key":"6560_CR43","doi-asserted-by":"publisher","unstructured":"Shu K, Wang S, Liu H (2019) Beyond news contents: The role of social context for fake news detection. In: Proceedings of the 12th ACM international conference on web search and data mining. WSDM\u201919, pp 312\u2013320. https:\/\/doi.org\/10.1145\/3289600.3290994","DOI":"10.1145\/3289600.3290994"},{"key":"6560_CR44","doi-asserted-by":"publisher","unstructured":"Zhang C, Song D, Huang C, Swami A, Chawla NV (2019) Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. KDD\u201919, pp 793\u2013803. https:\/\/doi.org\/10.1145\/3292500.3330961","DOI":"10.1145\/3292500.3330961"},{"key":"6560_CR45","doi-asserted-by":"crossref","unstructured":"Li M, Zhang Y, Xu H, Li X, Gao C, Wang Z (2025) Learning complex heterogeneous multimodal fake news via social latent network inference. arXiv preprint arXiv:2501.15508","DOI":"10.1609\/aaai.v39i1.32022"},{"issue":"4","key":"6560_CR46","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1145\/3517214","volume":"65","author":"VH Nguyen","year":"2022","unstructured":"Nguyen VH, Sugiyama K, Nakov P, Kan MY (2022) Fang: leveraging social context for fake news detection using graph representation. Commun. ACM 65(4):124\u2013132. https:\/\/doi.org\/10.1145\/3517214","journal-title":"Commun. ACM"},{"key":"6560_CR47","doi-asserted-by":"publisher","unstructured":"Yuan C, Ma Q, Zhou W, Han J, Hu S (2019) Jointly embedding the local and global relations of heterogeneous graph for rumor detection. 2019 IEEE international conference on data mining (ICDM), pp 796\u2013805. https:\/\/doi.org\/10.1109\/ICDM.2019.00090","DOI":"10.1109\/ICDM.2019.00090"},{"key":"6560_CR48","doi-asserted-by":"publisher","unstructured":"Yang R, Wang X, Jin Y, Li C, Lian J, Xie X (2022) Reinforcement subgraph reasoning for fake news detection. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. KDD\u201922, pp 2253\u20132262. https:\/\/doi.org\/10.1145\/3534678.3539277","DOI":"10.1145\/3534678.3539277"},{"key":"6560_CR49","doi-asserted-by":"publisher","unstructured":"Wu J, Hooi B (2023) Decor: Degree-corrected social graph refinement for fake news detection. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining. KDD\u201923, pp 2582\u20132593. https:\/\/doi.org\/10.1145\/3580305.3599298","DOI":"10.1145\/3580305.3599298"},{"key":"6560_CR50","doi-asserted-by":"crossref","unstructured":"Truica C, Apostol ES, Marogel M, Paschke A (2024) GETAE: graph information enhanced deep neural network ensemble architecture for fake news detection. CoRR arXiv:2412.01825","DOI":"10.1016\/j.eswa.2025.126984"},{"key":"6560_CR51","doi-asserted-by":"publisher","unstructured":"Hu L, Yang T, Zhang L, Zhong W, Tang D, Shi C, Duan N, Zhou M (2021) Compare to the knowledge: Graph neural fake news detection with external knowledge. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Volume 1: Long Papers), pp 754\u2013763. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.62","DOI":"10.18653\/v1\/2021.acl-long.62"},{"key":"6560_CR52","doi-asserted-by":"publisher","unstructured":"Lu YJ, Li CT (2020) GCAN: Graph-aware co-attention networks for explainable fake news detection on social media. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 505\u2013514. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.48","DOI":"10.18653\/v1\/2020.acl-main.48"},{"key":"6560_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119323","volume":"645","author":"H Wang","year":"2023","unstructured":"Wang H, Tang P, Kong H, Jin Y, Wu C, Zhou L (2023) Dhcf: Dual disentangled-view hierarchical contrastive learning for fake news detection on social media. Inf Sci 645:119323. https:\/\/doi.org\/10.1016\/j.ins.2023.119323","journal-title":"Inf Sci"},{"issue":"6","key":"6560_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103822","volume":"61","author":"L Han","year":"2024","unstructured":"Han L, Zhang X, Zhou Z, Liu Y (2024) A multifaceted reasoning network for explainable fake news detection. Inf Process Manag 61(6):103822. https:\/\/doi.org\/10.1016\/j.ipm.2024.103822","journal-title":"Inf Process Manag"},{"key":"6560_CR55","doi-asserted-by":"publisher","unstructured":"Zhu J, Gao C, Yin Z, Li X, Kurths J (2024) Propagation structure-aware graph transformer for robust and interpretable fake news detection. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining. KDD\u201924, pp 4652\u20134663. https:\/\/doi.org\/10.1145\/3637528.3672024","DOI":"10.1145\/3637528.3672024"},{"key":"6560_CR56","doi-asserted-by":"crossref","unstructured":"Gong S, Sinnott RO, Qi J, Paris C (2024) Less is more: Unseen domain fake news detection via causal propagation substructures. CoRR arXiv:2411.09389","DOI":"10.1145\/3701716.3715517"},{"key":"6560_CR57","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.patrec.2023.11.019","volume":"177","author":"K Soga","year":"2024","unstructured":"Soga K, Yoshida S, Muneyasu M (2024) Exploiting stance similarity and graph neural networks for fake news detection. Patt Recognit Lett 177:26\u201332. https:\/\/doi.org\/10.1016\/j.patrec.2023.11.019","journal-title":"Patt Recognit Lett"},{"issue":"2","key":"6560_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00433ED1V01Y201207DMK00","volume":"3","author":"Y Sun","year":"2012","unstructured":"Sun Y, Han J (2012) Mining heterogeneous information networks: principles and methodologies. Synth LectData Min Knowl Discov 3(2):1\u2013159. https:\/\/doi.org\/10.2200\/S00433ED1V01Y201207DMK00","journal-title":"Synth LectData Min Knowl Discov"},{"key":"6560_CR59","doi-asserted-by":"publisher","unstructured":"Fu X, Zhang J, Meng Z, King I (2020) Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the web conference 2020. WWW\u201920, pp 2331\u20132341. https:\/\/doi.org\/10.1145\/3366423.3380297","DOI":"10.1145\/3366423.3380297"},{"key":"6560_CR60","doi-asserted-by":"publisher","unstructured":"Zhou X, Yi Y, Jia G (2021) Path-rotate: Knowledge graph embedding by relational rotation of path in complex space. In: 2021 IEEE\/CIC international conference on communications in China (ICCC), pp 905\u2013910. https:\/\/doi.org\/10.1109\/ICCC52777.2021.9580273","DOI":"10.1109\/ICCC52777.2021.9580273"},{"key":"6560_CR61","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1609\/icwsm.v14i1.7350","volume":"14","author":"E Dai","year":"2020","unstructured":"Dai E, Sun Y, Wang S (2020) Ginger cannot cure cancer: Battling fake health news with a comprehensive data repository. Fourteenth International AAAI conference on web and social media 14:853\u2013862. https:\/\/doi.org\/10.1609\/icwsm.v14i1.7350","journal-title":"Fourteenth International AAAI conference on web and social media"},{"key":"6560_CR62","doi-asserted-by":"publisher","unstructured":"Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (20230) 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. https:\/\/doi.org\/10.1089\/big.2020.0062","DOI":"10.1089\/big.2020.0062"},{"issue":"1","key":"6560_CR63","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: A data mining perspective. SIGKDD Explor Newsl 19(1):22\u201336. https:\/\/doi.org\/10.1145\/3137597.3137600","journal-title":"SIGKDD Explor Newsl"},{"key":"6560_CR64","doi-asserted-by":"publisher","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of the 31st international conference on international conference on machine learning - Vol 32. ICML\u201914, pp 1188\u20131196. https:\/\/doi.org\/10.48550\/arXiv.1405.4053","DOI":"10.48550\/arXiv.1405.4053"},{"key":"6560_CR65","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"6560_CR66","unstructured":"Chung J, G\u00fcl\u00e7ehre \u00c7, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR arXiv:1412.3555"},{"issue":"4","key":"6560_CR67","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427\u2013437. https:\/\/doi.org\/10.1016\/j.ipm.2009.03.002","journal-title":"Inf Process Manag"},{"key":"6560_CR68","doi-asserted-by":"publisher","unstructured":"Huang J, Ling CX (2005) Using auc and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17(3):299\u2013310. https:\/\/doi.org\/10.1109\/TKDE.2005.50","DOI":"10.1109\/TKDE.2005.50"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06560-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06560-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06560-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:38:11Z","timestamp":1758310691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06560-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,14]]},"references-count":68,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6560"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06560-9","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,4,14]]},"assertion":[{"value":"8 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2025","order":2,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}],"article-number":"655"}}