{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T20:14:59Z","timestamp":1767212099255},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:00:00Z","timestamp":1668124800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:00:00Z","timestamp":1668124800000},"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":["World Wide Web"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11280-022-01114-2","type":"journal-article","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T13:03:02Z","timestamp":1668171782000},"page":"1793-1809","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["BotFinder: a novel framework for social bots detection in online social networks based on graph embedding and community detection"],"prefix":"10.1007","volume":"26","author":[{"given":"Shudong","family":"Li","sequence":"first","affiliation":[]},{"given":"Chuanyu","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiuming","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Dawei","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Peican","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"1114_CR1","doi-asserted-by":"crossref","unstructured":"Yang F, Liu Y, Yu X, et al.: Automatic detection of rumor on sina weibo[C]\/\/Proceedings of the ACM SIGKDD workshop on mining data semantics. 1\u20137 (2012)","DOI":"10.1145\/2350190.2350203"},{"key":"1114_CR2","doi-asserted-by":"crossref","unstructured":"Bessi A, Ferrara E.: Social bots distort the 2016 US Presidential election online discussion[J]. First monday, 21(11\u20137) (2016)","DOI":"10.5210\/fm.v21i11.7090"},{"issue":"6","key":"1114_CR3","first-page":"17","volume":"4","author":"BC Costa","year":"2013","unstructured":"Costa, B.C., Alberto, B.L.A., Portela, A.M., et al.: Fraud detection in electric power distribution networks using an ann-based knowledge-discovery process[J]. Int. J. Artif. Intell. Appl. 4(6), 17 (2013)","journal-title":"Int. J. Artif. Intell. Appl."},{"issue":"4","key":"1114_CR4","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.elerap.2012.02.005","volume":"11","author":"WH Chang","year":"2012","unstructured":"Chang, W.H., Chang, J.S.: An effective early fraud detection method for online auctions[J]. Electron. Commer. Res. Appl. 11(4), 346\u2013360 (2012)","journal-title":"Electron. Commer. Res. Appl."},{"issue":"6","key":"1114_CR5","first-page":"1035","volume":"4","author":"VR Ganji","year":"2012","unstructured":"Ganji, V.R., Mannem, S.N.P.: Credit card fraud detection using anti-k nearest neighbor algorithm[J]. Int. J. Comput. Sci. Eng. 4(6), 1035\u20131039 (2012)","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"1114_CR6","doi-asserted-by":"crossref","unstructured":"Ferrara, E.: Disinformation and social bot operations in the run up to the 2017 French presidential election[J]. arXiv preprint arXiv:1707.00086, (2017)","DOI":"10.5210\/fm.v22i8.8005"},{"issue":"49","key":"1114_CR7","doi-asserted-by":"publisher","first-page":"12435","DOI":"10.1073\/pnas.1803470115","volume":"115","author":"M Stella","year":"2018","unstructured":"Stella, M., Ferrara, E., De Domenico, M.: Bots increase exposure to negative and inflammatory content in online social systems[J]. Proc. Natl. Acad. Sci. 115(49), 12435\u201312440 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"4","key":"1114_CR8","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1089\/big.2017.0038","volume":"5","author":"D Stukal","year":"2017","unstructured":"Stukal, D., Sanovich, S., Bonneau, R., et al.: Detecting bots on Russian political Twitter[J]. Big Data 5(4), 310\u2013324 (2017)","journal-title":"Big Data"},{"key":"1114_CR9","doi-asserted-by":"crossref","unstructured":"Cai C, Li L, Zengi D. Behavior enhanced deep bot detection in social media[C]\/\/2017 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE,\u00a0128\u2013130 (2017)","DOI":"10.1109\/ISI.2017.8004887"},{"key":"1114_CR10","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.ins.2018.08.019","volume":"467","author":"S Kudugunta","year":"2018","unstructured":"Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection[J]. Inf. Sci. 467, 312\u2013322 (2018)","journal-title":"Inf. Sci."},{"issue":"5","key":"1114_CR11","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/MIS.2016.29","volume":"31","author":"S Cresci","year":"2016","unstructured":"Cresci, S., Di Pietro, R., Petrocchi, M., et al.: DNA-inspired online behavioral modeling and its application to spambot detection[J]. IEEE Intell. Syst. 31(5), 58\u201364 (2016)","journal-title":"IEEE Intell. Syst."},{"issue":"4","key":"1114_CR12","first-page":"561","volume":"15","author":"S Cresci","year":"2017","unstructured":"Cresci, S., Di Pietro, R., Petrocchi, M., et al.: Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling[J]. IEEE Trans. Dependable Secure Comput. 15(4), 561\u2013576 (2017)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"1114_CR13","unstructured":"Chen Z, Subramanian D.: An unsupervised approach to detect spam campaigns that use botnets on twitter[J]. arXiv preprint arXiv:1804.05232, (2018)"},{"issue":"4","key":"1114_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2746403","volume":"10","author":"M Jiang","year":"2016","unstructured":"Jiang, M., Cui, P., Beutel, A., et al.: Catching synchronized behaviors in large networks: A graph mining approach[J]. ACM Trans. Knowl. Discov. Data 10(4), 1\u201327 (2016)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"issue":"3","key":"1114_CR15","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/MWC.001.1900456","volume":"27","author":"S Su","year":"2020","unstructured":"Su, S., Tian, Z., Liang, S., et al.: A reputation management scheme for efficient malicious vehicle identification over 5G networks[J]. IEEE Wirel. Commun. 27(3), 46\u201352 (2020)","journal-title":"IEEE Wirel. Commun."},{"key":"1114_CR16","doi-asserted-by":"crossref","unstructured":"Mazza M, Cresci S, Avvenuti M, et al.: Rtbust: Exploiting temporal patterns for botnet detection on twitter[C]\/\/Proceedings of the 10th ACM Conference on Web Science. 183\u2013192 (2019)","DOI":"10.1145\/3292522.3326015"},{"key":"1114_CR17","first-page":"P1008","volume":"10","author":"L Guillaume","year":"2008","unstructured":"Guillaume, L.: Fast unfolding of communities in large networks[J]. J. Stat. Mech.: Theory Exp. 10, P1008 (2008)","journal-title":"J. Stat. Mech.: Theory Exp."},{"key":"1114_CR18","volume":"401","author":"S Li","year":"2021","unstructured":"Li, S., Jiang, L., Wu, X., et al.: A weighted network community detection algorithm based on deep learning[J]. Appl. Math. Comput. 401, 126012 (2021)","journal-title":"Appl. Math. Comput."},{"key":"1114_CR19","unstructured":"Lerer A, Wu L, Shen J, et al.: Pytorch-biggraph: A large-scale graph embedding system[J]. arXiv preprint arXiv:1903.12287 (2019)"},{"key":"1114_CR20","doi-asserted-by":"crossref","unstructured":"Yu, W., Cheng, W., Aggarwal, C.C., et al.: Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks[C]\/\/Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2672\u20132681 (2018)","DOI":"10.1145\/3219819.3220024"},{"key":"1114_CR21","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks[C]\/\/Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"1114_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2021.101771","volume":"103","author":"P Pham","year":"2022","unstructured":"Pham, P., Nguyen, L.T.T., Vo, B., et al.: Bot2Vec: a general approach of intra-community oriented representation learning for bot detection in different types of social networks[J]. Inf. Syst. 103, 101771 (2022)","journal-title":"Inf. Syst."},{"key":"1114_CR23","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"1114_CR24","doi-asserted-by":"crossref","unstructured":"Aljohani, N.R., Fayoumi, A., Hassan, S.U.: Bot prediction on social networks of Twitter in altmetrics using deep graph convolutional networks[J]. Soft Computing, 1\u201312 (2020)","DOI":"10.1007\/s00500-020-04689-y"},{"key":"1114_CR25","volume":"366","author":"S Li","year":"2020","unstructured":"Li, S., Zhao, D., Wu, X., et al.: Functional immunization of networks based on message passing[J]. Appl. Math. Comput. 366, 124728 (2020)","journal-title":"Appl. Math. Comput."},{"key":"1114_CR26","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.neucom.2015.10.147","volume":"210","author":"Y Nie","year":"2016","unstructured":"Nie, Y., Jia, Y., Li, S., et al.: Identifying users across social networks based on dynamic core interests[J]. Neurocomputing 210, 107\u2013115 (2016)","journal-title":"Neurocomputing"},{"issue":"1","key":"1114_CR27","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TSMC.2016.2608658","volume":"47","author":"C Gao","year":"2016","unstructured":"Gao, C., Liu, J.: Network-based modeling for characterizing human collective behaviors during extreme events[J]. IEEE Trans. Syst. Man Cybernetics: Syst. 47(1), 171\u2013183 (2016)","journal-title":"IEEE Trans. Syst. Man Cybernetics: Syst."},{"issue":"7","key":"1114_CR28","first-page":"1252","volume":"66","author":"P Zhu","year":"2018","unstructured":"Zhu, P., Zhi, Q., Guo, Y., et al.: Analysis of epidemic spreading process in adaptive networks[J]. IEEE Trans. Circuits Syst. II Express Briefs 66(7), 1252\u20131256 (2018)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"issue":"3","key":"1114_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102553","volume":"58","author":"S Su","year":"2021","unstructured":"Su, S., Tian, Z., Li, S., et al.: IoT root union: a decentralized name resolving system for IoT based on blockchain[J]. Inf. Process. Manage. 58(3), 102553 (2021)","journal-title":"Inf. Process. Manage."},{"key":"1114_CR30","first-page":"3146","volume":"30","author":"G Ke","year":"2017","unstructured":"Ke, G., Meng, Q., Finley, T., et al.: Lightgbm: A highly efficient gradient boosting decision tree[J]. Adv. Neural. Inf. Process. Syst. 30, 3146\u20133154 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1114_CR31","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system[C]\/\/Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"1114_CR32","unstructured":"Dorogush, A.V., Ershov, V., Gulin, A.: CatBoost: gradient boosting with categorical features support[J]. arXiv preprint arXiv:1810.11363 (2018)"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-022-01114-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-022-01114-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-022-01114-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:10:24Z","timestamp":1728346224000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-022-01114-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,11]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["1114"],"URL":"https:\/\/doi.org\/10.1007\/s11280-022-01114-2","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"type":"print","value":"1386-145X"},{"type":"electronic","value":"1573-1413"}],"subject":[],"published":{"date-parts":[[2022,11,11]]},"assertion":[{"value":"19 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable<b>.<\/b>","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable<b>.<\/b>","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal ethics"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}