{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T16:10:14Z","timestamp":1776960614508,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["2014-3-00077"],"award-info":[{"award-number":["2014-3-00077"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2023R1A2C2006264"],"award-info":[{"award-number":["2023R1A2C2006264"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The earliest detection of rumors across social media is the need to the hour in present global village. User\u2019s are seamlessly connected in an unstructured network leading to rapid flow of information. User\u2019s on the social media with malign intents may share defamatory content to contribute towards the fifth generation media warfare. The ingress of such defamatory content into society can result in panic, uncertainty and demoralization the peoples. Due to the huge amount of content over social platforms, the detection of malicious contents is hard. Earlier research while focuses on content profiling and flow of information, however, the reciprocal perspective of the source and following contents is missing. In this research, a novel Reciprocal Perspective fused Graph Convolutional Neural Network (RPf-GCN) is proposed. The proposed framework incorporates twin GCNs to encode both the bottom-up and top-down perspectives, enhancing the understanding of rumor propagation. Moreover convolutional operation is employed to fuse reciprocal perspective, providing a holistic view of the conversations. To validate the efficacy of the proposed framework, we conducted a series of experiments using real-world datasets, including PHEME and SemEval. Experimentation performed illustrates that the proposed framework outperformed over various baselines in two different evaluation metrics namely Macro F1 (for PHEME 0.736, for SemEval 0.461) and Accuracy (for PHEME 0.748, for SemEval 0.658).<\/jats:p>","DOI":"10.1186\/s40537-023-00866-6","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T15:03:54Z","timestamp":1704812634000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["RPf-GCNs: reciprocal perspective driven fused GCNs for rumor detection on social media"],"prefix":"10.1186","volume":"11","author":[{"given":"Zafran","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeonghwan","family":"Gwak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naima","family":"Iltaf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Witold","family":"Pedrycz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moongu","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"issue":"1","key":"866_CR1","first-page":"1","volume":"47","author":"VL Rubin","year":"2010","unstructured":"Rubin VL. On deception and deception detection: content analysis of computer-mediated stated beliefs. Proc Am Soc Inf Sci Technol. 2010;47(1):1\u201310.","journal-title":"Proc Am Soc Inf Sci Technol"},{"issue":"1","key":"866_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1177\/0392192107073433","volume":"54","author":"N DiFonzo","year":"2007","unstructured":"DiFonzo N, Bordia P. Rumor, gossip and urban legends. Diogenes. 2007;54(1):19\u201335.","journal-title":"Diogenes"},{"key":"866_CR3","unstructured":"Qazvinian V, Rosengren E, Radev D, Mei Q. Rumor has it: identifying misinformation in microblogs. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 2011:pp. 1589\u20131599."},{"key":"866_CR4","doi-asserted-by":"crossref","unstructured":"Ma J, Gao W, Wong KF. Rumor detection on twitter with tree-structured recursive neural networks 2018.","DOI":"10.18653\/v1\/P18-1184"},{"key":"866_CR5","doi-asserted-by":"crossref","unstructured":"Li Q, Zhang Q, Si L. Rumor detection by exploiting user credibility information, attention and multi-task learning. In: Proceedings of the 57th annual meeting of the association for computational linguistics. 2019:pp. 1173\u20131179.","DOI":"10.18653\/v1\/P19-1113"},{"key":"866_CR6","doi-asserted-by":"crossref","unstructured":"Bian T, Xiao X, Xu T, Zhao P, Huang W, Rong Y, Huang J. Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI conference on artificial intelligence. 2020:pp. 549\u2013556.","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"866_CR7","doi-asserted-by":"crossref","unstructured":"Wei L, Hu D, Zhou W, Yue Z, Hu S. Towards propagation uncertainty: Edge-enhanced bayesian graph convolutional networks for rumor detection. 2021. arXiv preprint arXiv:2107.11934.","DOI":"10.18653\/v1\/2021.acl-long.297"},{"key":"866_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123174","volume":"540","author":"FA Ozbay","year":"2020","unstructured":"Ozbay FA, Alatas B. Fake news detection within online social media using supervised artificial intelligence algorithms. Phys A Stat Mech Appl. 2020;540: 123174.","journal-title":"Phys A Stat Mech Appl"},{"key":"866_CR9","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.cogsys.2019.12.005","volume":"61","author":"RK Kaliyar","year":"2020","unstructured":"Kaliyar RK, Goswami A, Narang P, Sinha S. FNDNet-a deep convolutional neural network for fake news detection. Cogn Syst Res. 2020;61:32\u201344.","journal-title":"Cogn Syst Res"},{"key":"866_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113503","volume":"158","author":"PHA Faustini","year":"2020","unstructured":"Faustini PHA, Cov\u00f5es TF. Fake news detection in multiple platforms and languages. Expert Syst Appl. 2020;158: 113503.","journal-title":"Expert Syst Appl"},{"key":"866_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114090","volume":"166","author":"Y Wang","year":"2021","unstructured":"Wang Y, Wang L, Yang Y, Lian T. SemSeq4FD: integrating global semantic relationship and local sequential order to enhance text representation for fake news detection. Expert Syst Appl. 2021;166: 114090.","journal-title":"Expert Syst Appl"},{"issue":"3","key":"866_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386253","volume":"38","author":"Y Liu","year":"2020","unstructured":"Liu Y, Wu Y. FNED: a deep network for fake news early detection on social media. ACM Trans Inf Syst. 2020;38(3):1\u201333.","journal-title":"ACM Trans Inf Syst"},{"key":"866_CR13","doi-asserted-by":"crossref","unstructured":"Ma J, Gao W, Wei Z, Lu Y, Wong KF. Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM international on conference on information and knowledge management. 2015;pp. 1751\u20131754.","DOI":"10.1145\/2806416.2806607"},{"key":"866_CR14","unstructured":"Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong KF, Cha M. Detecting rumors from microblogs with recurrent neural networks 2016."},{"key":"866_CR15","doi-asserted-by":"crossref","unstructured":"Liu Y, Wu YF. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI conference on artificial intelligence 2018.","DOI":"10.1609\/aaai.v32i1.11268"},{"key":"866_CR16","doi-asserted-by":"crossref","unstructured":"Lu YJ, Li CT. Gcan: Graph-aware co-attention networks for explainable fake news detection on social media. arXiv preprint arXiv:2004.11648 2020.","DOI":"10.18653\/v1\/2020.acl-main.48"},{"key":"866_CR17","unstructured":"Benevenuto F, Magno G, Rodrigues T, Almeida V. Detecting spammers on twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS). 2010;6:12."},{"key":"866_CR18","unstructured":"Thomas K, McCoy D, Grier C, Kolcz A, Paxson V. Trafficking fraudulent accounts: the role of the underground market in twitter spam and abuse. in Presented as part of the 22nd fUSENIXg Security Symposium (fUSENIXg Security 13), 2013: pp. 195\u2013210."},{"key":"866_CR19","doi-asserted-by":"crossref","unstructured":"Jiang M, Cui P, Beutel A, Faloutsos C, Yang S. Detecting suspicious following behavior in multimillion-node social networks. in Proceedings of the 23rd International Conference on World Wide Web, 2014: pp. 305\u2013306.","DOI":"10.1145\/2567948.2577306"},{"key":"866_CR20","unstructured":"Rong Y, Huang W, Xu T, Huang J. The truly deep graph convolutional networks for node classification. CoRR abs\/1907.10903 2019. arXiv:1907.10903."},{"key":"866_CR21","doi-asserted-by":"crossref","unstructured":"Kochkina E, Liakata M, Augenstein I. Turing at semeval-2017 task 8: Sequential approach to rumour stance classification with branch-lstm. arXiv preprint arXiv:1704.07221 2017.","DOI":"10.18653\/v1\/S17-2083"},{"key":"866_CR22","doi-asserted-by":"crossref","unstructured":"Wei P, Xu N, Mao W. Modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity. arXiv preprint arXiv:1909.08211 2019.","DOI":"10.18653\/v1\/D19-1485"},{"key":"866_CR23","doi-asserted-by":"crossref","unstructured":"Khoo LMS, Chieu HL, Qian Z, Jiang J. Interpretable rumor detection in micro-blogs by attending to user interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2020:pp. 8783\u20138790.","DOI":"10.1609\/aaai.v34i05.6405"},{"key":"866_CR24","doi-asserted-by":"crossref","unstructured":"Yu J, Jiang J, Khoo LMS, Chieu HL, Xia R. Coupled hierarchical transformer for stance-aware rumor verification in social media conversations 2020.","DOI":"10.18653\/v1\/2020.emnlp-main.108"},{"key":"866_CR25","doi-asserted-by":"crossref","unstructured":"Lin H, Ma J, Cheng M, Yang Z, Chen L, Chen G. Rumor detection on twitter with claim-guided hierarchical graph attention networks. arXiv preprint arXiv:2110.04522 2021.","DOI":"10.18653\/v1\/2021.emnlp-main.786"},{"key":"866_CR26","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yu X, Cui Z, Wu S, Wen Z, Wang L. Every document owns its structure: Inductive text classification via graph neural networks. arXiv preprint arXiv:2004.13826 2020.","DOI":"10.18653\/v1\/2020.acl-main.31"},{"key":"866_CR27","doi-asserted-by":"crossref","unstructured":"Wei Y, Wang X, He X, Nie L, Rui Y, Chua TS. Hierarchical user intent graph network for multimedia recommendation. IEEE Transactions on Multimedia 2021.","DOI":"10.1109\/TMM.2021.3088307"},{"key":"866_CR28","unstructured":"Kipf TN, Welling M. Semi-Supervised Classification with Graph Convolutional Networks. arXiv preprint arXiv:1609.02907, 2018."},{"key":"866_CR29","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y. Graph attention networks stat. 2017;1050:20."},{"key":"866_CR30","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.neunet.2020.08.021","volume":"132","author":"Y Xie","year":"2020","unstructured":"Xie Y, Zhang Y, Gong M, Tang Z, Han C. Mgat: multi-view graph attention networks. Neural Netw. 2020;132:180\u20139.","journal-title":"Neural Netw"},{"key":"866_CR31","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. 2011:pp. 675\u2013684.","DOI":"10.1145\/1963405.1963500"},{"key":"866_CR32","unstructured":"Feng S, Banerjee R, Choi Y. Syntactic stylometry for deception detection. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2012:pp. 171\u2013175."},{"key":"866_CR33","doi-asserted-by":"crossref","unstructured":"Chen Y, Conroy NJ, Rubin VL. Misleading online content: recognizing click bait as \u201c false news\u201d. In: Proceedings of the 2015 ACM on workshop on multimodal deception detection. 2015:pp. 15\u201319.","DOI":"10.1145\/2823465.2823467"},{"key":"866_CR34","doi-asserted-by":"crossref","unstructured":"Kwon S, Cha M, Jung K, Chen W, Wang Y. Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th international conference on data mining. pp. 1103\u20131108. IEEE 2013.","DOI":"10.1109\/ICDM.2013.61"},{"key":"866_CR35","doi-asserted-by":"crossref","unstructured":"Sampson J, Morstatter F, Wu L, Liu H. Leveraging the implicit structure within social media for emergent rumor detection. In: Proceedings of the 25th ACM international on conference on information and knowledge management. 2016: pp.2377\u20132382.","DOI":"10.1145\/2983323.2983697"},{"key":"866_CR36","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. 2012; pp. 1\u20137.","DOI":"10.1145\/2350190.2350203"},{"key":"866_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119083","volume":"642","author":"Z Peng","year":"2023","unstructured":"Peng Z, Zhen H, Yong D, Yeqing Y. Rumor detection on social media through mining the social circles with high homogeneity. Inf Sci. 2023;642: 119083.","journal-title":"Inf Sci"},{"issue":"4","key":"866_CR38","doi-asserted-by":"publisher","first-page":"5213","DOI":"10.1609\/aaai.v37i4.25651","volume":"37","author":"H Lin","year":"2023","unstructured":"Lin H, Yi P, Ma J, Jiang H, Luo Z, Shi S, Liu R. Zero-shot rumor detection with propagation structure via prompt learning. Proc AAAI Conf Artif Intell. 2023;37(4):5213\u201321. https:\/\/doi.org\/10.1609\/aaai.v37i4.25651.","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"866_CR39","doi-asserted-by":"publisher","unstructured":"Li S, Li W, Luvembe AM, Tong W. Graph Contrastive Learning With Feature Augmentation for Rumor Detection. in IEEE Transactions on Computational Social Systems, https:\/\/doi.org\/10.1109\/TCSS.2023.3269303.","DOI":"10.1109\/TCSS.2023.3269303"},{"key":"866_CR40","doi-asserted-by":"crossref","unstructured":"Sun M, Zhang X, Ma J, Xie S, Liu Y, Philip SY. Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection. IEEE Transactions on Knowledge and Data Engineering. 2023.","DOI":"10.1109\/TKDE.2023.3275586"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00866-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-023-00866-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00866-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T15:05:15Z","timestamp":1704812715000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-023-00866-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["866"],"URL":"https:\/\/doi.org\/10.1186\/s40537-023-00866-6","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]},"assertion":[{"value":"19 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"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":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12"}}