{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T12:47:50Z","timestamp":1780922870561,"version":"3.54.1"},"reference-count":69,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003448","name":"General Secretariat for Research and Technology","doi-asserted-by":"publisher","award":["T2EDK-5055943"],"award-info":[{"award-number":["T2EDK-5055943"]}],"id":[{"id":"10.13039\/501100003448","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003448","name":"General Secretariat for Research and Technology","doi-asserted-by":"publisher","award":["T1EDK-03470"],"award-info":[{"award-number":["T1EDK-03470"]}],"id":[{"id":"10.13039\/501100003448","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The combat against fake news and disinformation is an ongoing, multi-faceted task for researchers in social media and social networks domains, which comprises not only the detection of false facts in published content but also the detection of accountability mechanisms that keep a record of the trustfulness of sources that generate news and, lately, of the networks that deliberately distribute fake information. In the direction of detecting and handling organized disinformation networks, major social media and social networking sites are currently developing strategies and mechanisms to block such attempts. The role of machine learning techniques, especially neural networks, is crucial in this task. The current work focuses on the popular and promising graph representation techniques and performs a survey of the works that employ Graph Convolutional Networks (GCNs) to the task of detecting fake news, fake accounts and rumors that spread in social networks. It also highlights the available benchmark datasets employed in current research for validating the performance of the proposed methods. This work is a comprehensive survey of the use of GCNs in the combat against fake news and aims to be an ideal starting point for future researchers in the field.<\/jats:p>","DOI":"10.3390\/fi14030070","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T07:22:35Z","timestamp":1645687355000},"page":"70","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Survey on the Use of Graph Convolutional Networks for Combating Fake News"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0876-8167","authenticated-orcid":false,"given":"Iraklis","family":"Varlamis","sequence":"first","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, 17671 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5316-6704","authenticated-orcid":false,"given":"Dimitrios","family":"Michail","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, 17671 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Foteini","family":"Glykou","sequence":"additional","affiliation":[{"name":"Palo Services Ltd., 10562 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Tsantilas","sequence":"additional","affiliation":[{"name":"Palo Services Ltd., 10562 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1257\/jep.31.2.211","article-title":"Social media and fake news in the 2016 election","volume":"31","author":"Allcott","year":"2017","journal-title":"J. Econ. Perspect."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1177\/0002764219869402","article-title":"Mapping recent development in scholarship on fake news and misinformation, 2008 to 2017: Disciplinary contribution, topics, and impact","volume":"65","author":"Ha","year":"2021","journal-title":"Am. Behav. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3961\/jpmph.20.094","article-title":"Impact of rumors and misinformation on COVID-19 in social media","volume":"53","author":"Tasnim","year":"2020","journal-title":"J. Prev. Med. Public Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1080\/10584609.2019.1661888","article-title":"Political astroturfing on twitter: How to coordinate a disinformation campaign","volume":"37","author":"Keller","year":"2020","journal-title":"Political Commun."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Benkler, Y., Faris, R., and Roberts, H. (2018). Network Propaganda: Manipulation, Disinformation, and Radicalization in American Politics, Oxford University Press.","DOI":"10.1093\/oso\/9780190923624.001.0001"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1126\/science.aap9559","article-title":"The spread of true and false news online","volume":"359","author":"Vosoughi","year":"2018","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, S., Tong, H., Xu, J., and Maciejewski, R. (2018, January 18\u201320). Graph convolutional networks: Algorithms, applications and open challenges. Proceedings of the International Conference on Computational Social Networks, Shanghai, China.","DOI":"10.1007\/978-3-030-04648-4_7"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: A comprehensive review","volume":"6","author":"Zhang","year":"2019","journal-title":"Comput. Soc. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake News Detection on Social Media: A Data Mining Perspective","volume":"19","author":"Shu","year":"2017","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cardoso Durier da Silva, F., Vieira, R., and Garcia, A.C. (2019, January 8\u201311). Can machines learn to detect fake news? A survey focused on social media. Proceedings of the 52nd Hawaii International Conference on System Sciences, Maui, HI, USA.","DOI":"10.24251\/HICSS.2019.332"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3395046","article-title":"A survey of fake news: Fundamental theories, detection methods, and opportunities","volume":"53","author":"Zhou","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1108\/IJICC-04-2021-0069","article-title":"A systematic survey on deep learning and machine learning approaches of fake news detection in the pre-and post-COVID-19 pandemic","volume":"14","author":"Varma","year":"2021","journal-title":"Int. J. Intell. Comput. Cybern."},{"key":"ref_13","unstructured":"Hindman, M., and Barash, V. (2018). Disinformation, and Influence Campaigns on Twitter, Knight Foundation: George Washington University."},{"key":"ref_14","unstructured":"Freelon, D., and Lokot, T. (Misinformation Review, 2020). Russian Twitter Disinformation Campaigns Reach across the American Political Spectrum, Misinformation Review."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Recuero, R., Soares, F.B., and Gruzd, A. (2020, January 8\u201311). Hyperpartisanship, disinformation and political conversations on Twitter: The Brazilian presidential election of 2018. Proceedings of the International AAAI Conference on Web and Social Media, Atlanta, GA, USA.","DOI":"10.1609\/icwsm.v14i1.7324"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101769","DOI":"10.1016\/j.is.2021.101769","article-title":"Detecting inorganic financial campaigns on Twitter","volume":"103","author":"Tardelli","year":"2021","journal-title":"Inform. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tardelli, S., Avvenuti, M., Tesconi, M., and Cresci, S. (2020, January 19\u201324). Characterizing social bots spreading financial disinformation. Proceedings of the International Conference on Human-Computer Interaction, Copenhagen, Denmark.","DOI":"10.1007\/978-3-030-49570-1_26"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"113230","DOI":"10.1109\/ACCESS.2020.3003370","article-title":"Charting the landscape of online cryptocurrency manipulation","volume":"8","author":"Nizzoli","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3313184","article-title":"Cashtag piggybacking: Uncovering spam and bot activity in stock microblogs on Twitter","volume":"13","author":"Cresci","year":"2019","journal-title":"ACM Trans. Web (TWEB)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102250","DOI":"10.1016\/j.ipm.2020.102250","article-title":"Detection of bots in social media: A systematic review","volume":"57","author":"Orabi","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Freitas, C., Benevenuto, F., Ghosh, S., and Veloso, A. (2015, January 25\u201328). Reverse engineering socialbot infiltration strategies in twitter. Proceedings of the 2015 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Paris, France.","DOI":"10.1145\/2808797.2809292"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., and Skiena, S. (2014, January 24\u201327). Deepwalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2623330.2623732"},{"key":"ref_24","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_25","unstructured":"Yao, L., Mao, C., and Luo, Y. (February, January 27). Graph convolutional networks for text classification. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., and Lin, D. (2018, January 2\u20137). Spatial temporal graph convolutional networks for skeleton-based action recognition. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, Y., Qian, S., Hu, J., Fang, Q., and Xu, C. (2020, January 8\u201311). Fake news detection via knowledge-driven multimodal graph convolutional networks. Proceedings of the 2020 International Conference on Multimedia Retrieval, Dublin, Ireland.","DOI":"10.1145\/3372278.3390713"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, G., Ding, Y., Qi, S., Wang, X., and Liao, Q. (2019, January 9\u201314). Multi-depth graph convolutional networks for fake news detection. Proceedings of the CCF International Conference on nAtural Language Processing and Chinese Computing, Dunhuang, China.","DOI":"10.1007\/978-3-030-32233-5_54"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3451215","article-title":"Knowledge-aware multi-modal adaptive graph convolutional networks for fake news detection","volume":"17","author":"Qian","year":"2021","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kang, Z., Cao, Y., Shang, Y., Liang, T., Tang, H., and Tong, L. (2021). Fake News Detection with Heterogenous Deep Graph Convolutional Network, Springer. PAKDD (1).","DOI":"10.1007\/978-3-030-75762-5_33"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Feng, S., Wan, H., Wang, N., and Luo, M. (2021). BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks. arXiv.","DOI":"10.1145\/3487351.3488336"},{"key":"ref_32","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"11109","DOI":"10.1007\/s00500-020-04689-y","article-title":"Bot prediction on social networks of Twitter in altmetrics using deep graph convolutional networks","volume":"24","author":"Aljohani","year":"2020","journal-title":"Soft Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Guo, Q., Xie, H., Li, Y., Ma, W., and Zhang, C. (2022). Social Bots Detection via Fusing BERT and Graph Convolutional Networks. Symmetry, 14.","DOI":"10.3390\/sym14010030"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lian, D., Xu, Y., Wu, L., and Chen, E. (2020, January 7\u201312). Graph convolutional networks with Markov random field reasoning for social spammer detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5455"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sharma, S., and Sharma, R. (2021, January 18\u201322). Identifying possible rumor spreaders on twitter: A weak supervised learning approach. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9534185"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dong, M., Zheng, B., Quoc Viet Hung, N., Su, H., and Li, G. (2019, January 3\u20137). Multiple rumor source detection with graph convolutional networks. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, New York, NY, USA.","DOI":"10.1145\/3357384.3357994"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., and Huang, J. (2020, January 7\u201312). Rumor detection on social media with bi-directional graph convolutional networks. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"ref_39","unstructured":"Li, C., and Goldwasser, D. (August, January 28). Encoding social information with graph convolutional networks for political perspective detection in news media. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Choi, J., Ko, T., Choi, Y., Byun, H., and Kim, C.k. (2021). Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0256039"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4774","DOI":"10.1007\/s10489-020-02036-0","article-title":"Detection of rumor conversations in Twitter using graph convolutional networks","volume":"51","author":"Lotfi","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Wu, Y.F.B. (2018, January 2\u20137). Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11268"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zubiaga, A., Liakata, M., and Procter, R. (2017, January 13\u201315). Exploiting context for rumour detection in social media. Proceedings of the International Conference on Social Informatics, Oxford, UK.","DOI":"10.1007\/978-3-319-67217-5_8"},{"key":"ref_44","unstructured":"Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B.J., Wong, K.F., and Cha, M. (2016, January 9\u201315). Detecting rumors from microblogs with recurrent neural networks. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, W.Y. (2017). \u201cliar, liar pants on fire\u201d: A new benchmark dataset for fake news detection. arXiv.","DOI":"10.18653\/v1\/P17-2067"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Khattar, D., Goud, J.S., Gupta, M., and Varma, V. (2019, January 13\u201317). Mvae: Multimodal variational autoencoder for fake news detection. Proceedings of the World Wide Web Conference, New York, NY, USA.","DOI":"10.1145\/3308558.3313552"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., and Wong, K.F. (2017). Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning, Association for Computational Linguistics.","DOI":"10.18653\/v1\/P17-1066"},{"key":"ref_48","unstructured":"Nakamura, K., Levy, S., and Wang, W.Y. (2019). r\/fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Feng, S., Wan, H., Wang, N., Li, J., and Luo, M. (2021, January 1\u20135). TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Gold Coast, QLD, Australia.","DOI":"10.1145\/3459637.3482019"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mazza, M., Cresci, S., Avvenuti, M., Quattrociocchi, W., and Tesconi, M. (2019, January 27\u201330). Rtbust: Exploiting temporal patterns for botnet detection on twitter. Proceedings of the 10th ACM Conference on Web Science, New York, NY, USA.","DOI":"10.1145\/3292522.3326015"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1002\/hbe2.115","article-title":"Arming the public with artificial intelligence to counter social bots","volume":"1","author":"Yang","year":"2019","journal-title":"Hum. Behav. Emerg. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., and Crowcroft, J. (August, January 31). Of bots and humans (on twitter). Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, New York, NY, USA.","DOI":"10.1145\/3110025.3110090"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Cresci, S., Lillo, F., Regoli, D., Tardelli, S., and Tesconi, M. (2018, January 25\u201328). $FAKE: Evidence of spam and bot activity in stock microblogs on Twitter. Proceedings of the Twelfth International AAAI Conference on Web and Social Media, Stanford, CA, USA.","DOI":"10.1609\/icwsm.v12i1.15073"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yang, K.C., Varol, O., Hui, P.M., and Menczer, F. (2020, January 7\u201312). Scalable and generalizable social bot detection through data selection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5460"},{"key":"ref_55","unstructured":"Lee, K., Eoff, B.D., and Caverlee, J. (2011, January 17\u201321). Seven months with the devils: A long-term study of content polluters on twitter. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yang, C., Harkreader, R., Zhang, J., Shin, S., and Gu, G. (2012, January 16\u201320). Analyzing spammers\u2019 social networks for fun and profit: A case study of cyber criminal ecosystem on twitter. Proceedings of the 21st International Conference on World Wide Web, New York, NY, USA.","DOI":"10.1145\/2187836.2187847"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1016\/j.joi.2018.08.002","article-title":"Investigating the quality of interactions and public engagement around scientific papers on Twitter","volume":"12","author":"Didegah","year":"2018","journal-title":"J. Informetr."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s11192-017-2512-x","article-title":"Measuring social media activity of scientific literature: An exhaustive comparison of scopus and novel altmetrics big data","volume":"113","author":"Hassan","year":"2017","journal-title":"Scientometrics"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T., and Leiserson, C. (2020, January 7\u201312). Evolvegcn: Evolving graph convolutional networks for dynamic graphs. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Si, C., Chen, W., Wang, W., Wang, L., and Tan, T. (2019, January 16\u201320). An attention enhanced graph convolutional lstm network for skeleton-based action recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00132"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-gcn: A temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., and Wu, X.M. (2018, January 2\u20137). Deeper insights into graph convolutional networks for semi-supervised learning. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Giachanou, A., Zhang, G., and Rosso, P. (2020, January 8\u201311). Multimodal fake news detection with textual, visual and semantic information. Proceedings of the International Conference on Text, Speech, and Dialogue, Olomouc, Czech Republic.","DOI":"10.1007\/978-3-030-58323-1_3"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Khrulkov, V., Mirvakhabova, L., Ustinova, E., Oseledets, I., and Lempitsky, V. (2020, January 13\u201319). Hyperbolic image embeddings. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00645"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hamdi, T., Slimi, H., Bounhas, I., and Slimani, Y. (2020, January 9\u201312). A hybrid approach for fake news detection in twitter based on user features and graph embedding. Proceedings of the International Conference on Distributed Computing and Internet Technology, Bhubaneswar, India.","DOI":"10.1007\/978-3-030-36987-3_17"},{"key":"ref_66","unstructured":"Wang, Z., and Ji, S. (2020). Second-order pooling for graph neural networks. IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_67","unstructured":"Yuan, H., and Ji, S. (2020, January 26\u201330). Structpool: Structured graph pooling via conditional random fields. Proceedings of the 8th International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_68","unstructured":"Zhang, Z., Bu, J., Ester, M., Zhang, J., Yao, C., Yu, Z., and Wang, C. (2019). Hierarchical graph pooling with structure learning. arXiv."},{"key":"ref_69","unstructured":"Shchur, O., Mumme, M., Bojchevski, A., and G\u00fcnnemann, S. (2018). Pitfalls of graph neural network evaluation. arXiv."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/3\/70\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:26:09Z","timestamp":1760135169000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/3\/70"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,24]]},"references-count":69,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["fi14030070"],"URL":"https:\/\/doi.org\/10.3390\/fi14030070","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,24]]}}}