{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T23:19:35Z","timestamp":1771715975303,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319180311","type":"print"},{"value":"9783319180328","type":"electronic"}],"license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"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":[[2015]]},"DOI":"10.1007\/978-3-319-18032-8_16","type":"book-chapter","created":{"date-parts":[[2015,5,8]],"date-time":"2015-05-08T05:41:54Z","timestamp":1431063714000},"page":"201-214","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["ND-Sync: Detecting Synchronized Fraud Activities"],"prefix":"10.1007","author":[{"given":"Maria","family":"Giatsoglou","sequence":"first","affiliation":[]},{"given":"Despoina","family":"Chatzakou","sequence":"additional","affiliation":[]},{"given":"Neil","family":"Shah","sequence":"additional","affiliation":[]},{"given":"Alex","family":"Beutel","sequence":"additional","affiliation":[]},{"given":"Christos","family":"Faloutsos","sequence":"additional","affiliation":[]},{"given":"Athena","family":"Vakali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,5,9]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Almaatouq, A., et al.: Twitter: who gets caught? observed trends in social micro-blogging spam. In: WebSci, pp. 33\u201341. ACM (2014)","DOI":"10.1145\/2615569.2615688"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Beutel, A., et al.: CopyCatch: stopping group attacks by spotting lockstep behavior in social networks. In: WWW, pp. 119\u2013130. ACM (2013)","DOI":"10.1145\/2488388.2488400"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Breunig, M., et al.: LOF: identifying density-based local outliers. In: Proc. ACM SIGMOD Conf. 2000, pp. 93\u2013104 (2000)","DOI":"10.1145\/342009.335388"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1198\/106186004X12632","volume":"13","author":"G Brys","year":"2004","unstructured":"Brys, G., et al.: A Robust Measure of Skewness. Journal of Computational and Graphical Statistics 13, 996\u20131017 (2004)","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Chan, P. K., et al.:Modeling multiple time series for anomaly detection. In: ICDM, pp. 90\u201397. IEEE Computer Society (2005)","DOI":"10.1109\/ICDM.2005.101"},{"issue":"3","key":"16_CR6","doi-asserted-by":"publisher","first-page":"15:1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., et al.: Anomaly Detection: A Survey. ACM Comput. Surv. 41(3), 15:1\u201315:58 (2009)","journal-title":"ACM Comput. Surv."},{"issue":"6","key":"16_CR7","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1109\/TDSC.2012.75","volume":"9","author":"Z Chu","year":"2012","unstructured":"Chu, Z., et al.: Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? IEEE Trans. Dependable Secur. Comput. 9(6), 811\u2013824 (2012)","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"issue":"1","key":"16_CR8","first-page":"58","volume":"13","author":"D Cook","year":"2014","unstructured":"Cook, D., et al.: Twitter Deception and Influence: Issues of Identity, Slacktivism, and Puppetry. Journal of Information Warfare 13(1), 58\u201371 (2014)","journal-title":"Journal of Information Warfare"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Freitas, C.A., et al.: Reverse Engineering Socialbot Infiltration Strategies in Twitter. ArXiv e-prints (2014)","DOI":"10.1145\/2808797.2809292"},{"key":"16_CR10","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/0375-6742(89)90071-X","volume":"32","author":"RG Garrett","year":"1989","unstructured":"Garrett, R.G.: The Chi-square Plot: a Tool for Multivariate Outlier Recognition. Journal of Geochemical Exploration 32, 319\u2013341 (1989)","journal-title":"Journal of Geochemical Exploration"},{"key":"16_CR11","unstructured":"Ghosh, R., et al.: Entropy-based classification of \u2018Retweeting\u2019 activity on twitter. In: KDD Workshop on Social Network Analysis (SNA-KDD) (2011)"},{"issue":"3","key":"16_CR12","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/s10618-008-0093-2","volume":"16","author":"A Ghoting","year":"2008","unstructured":"Ghoting, A., et al.: Fast mining of distance-based outliers in high-dimensional datasets. Data Mining and Knowledge Discovery 16(3), 349\u2013364 (2008)","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"3","key":"16_CR13","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1109\/36.843012","volume":"38","author":"G Hazel","year":"2000","unstructured":"Hazel, G.: Multivariate Gaussian MRF for Multispectral Scene Segmentation and Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing 38(3), 1199\u20131211 (2000)","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1198\/004017004000000563","volume":"47","author":"M Hubert","year":"2005","unstructured":"Hubert, M., et al.: ROBPCA: A New Approach to Robust Principal Component Analysis. Technometrics 47, 64\u201379 (2005)","journal-title":"Technometrics"},{"issue":"6","key":"16_CR15","doi-asserted-by":"publisher","first-page":"2264","DOI":"10.1016\/j.csda.2008.05.027","volume":"53","author":"M Hubert","year":"2009","unstructured":"Hubert, M., et al.: Robust PCA for Skewed Data and its Outlier Map. Computational Statistics & Data Analysis 53(6), 2264\u20132274 (2009)","journal-title":"Computational Statistics & Data Analysis"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Jiang, M., et al.: CatchSync: catching synchronized behavior in large directed graphs. In: KDD, pp. 941\u2013950. ACM (2014)","DOI":"10.1145\/2623330.2623632"},{"key":"16_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-319-06608-0_11","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"M Jiang","year":"2014","unstructured":"Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Inferring strange behavior from connectivity pattern in social networks. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part I. LNCS, vol. 8443, pp. 126\u2013138. Springer, Heidelberg (2014)"},{"issue":"1","key":"16_CR18","first-page":"21","volume":"22","author":"IT Jolliffe","year":"1973","unstructured":"Jolliffe, I.T.: Discarding Variables in a Principal Component Analysis. II: Real Data. Journal of the Royal Statistical Society. Series C (Applied Statistics) 22(1), 21\u201331 (1973)","journal-title":"Journal of the Royal Statistical Society. Series C (Applied Statistics)"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Noble, C.C., et al.: Graph-based anomaly detection. In: KDD (2003)","DOI":"10.1145\/956750.956831"},{"key":"16_CR20","unstructured":"Papadimitriou, S., et al.: LOCI: Fast outlier detection using the local correlation integral. In: ICDE 2003 (2003)"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Shah, N., et al.: Spotting suspicious link behavior with fBox: an adversarial perspective. In: ICDM (2014)","DOI":"10.1109\/ICDM.2014.36"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Stringhini, G., et al.: Follow the green: growth and dynamics in twitter follower markets. In: IMC, pp. 163\u2013176. ACM (2013)","DOI":"10.1145\/2504730.2504731"},{"issue":"7","key":"16_CR23","doi-asserted-by":"publisher","first-page":"e65774","DOI":"10.1371\/journal.pone.0065774","volume":"8","author":"G Tavares","year":"2013","unstructured":"Tavares, G., et al.: Scaling-Laws of Human Broadcast Communication Enable Distinction between Human, Corporate and Robot Twitter Users. PLoS ONE 8(7), e65774 (2013)","journal-title":"PLoS ONE"},{"key":"16_CR24","unstructured":"Twitter Inc. S-1 Filing, US Securities and Exchange Commission (2013). http:\/\/www.sec.gov\/Archives\/edgar\/data\/1418091\/000119312513390321\/d564001ds1.htm"},{"key":"16_CR25","first-page":"1071","volume":"24","author":"L Xiong","year":"2011","unstructured":"Xiong, L., et al.: Group Anomaly Detection using Flexible Genre Models. Advances in Neural Information Processing Systems 24, 1071\u20131079 (2011)","journal-title":"Advances in Neural Information Processing Systems"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Xiong, L., et al.: Efficient learning on point sets. In: ICDM, pp. 847\u2013856 (2013)","DOI":"10.1109\/ICDM.2013.59"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Yang, C., et al.: Analyzing spammers\u2019 social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: WWW, pp. 71\u201380 (2012)","DOI":"10.1145\/2187836.2187847"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Yu, R., et al.: GLAD: group anomaly detection in social media analysis. In: KDD, pp. 372\u2013381. ACM (2014)","DOI":"10.1145\/2623330.2623719"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-18032-8_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T21:16:02Z","timestamp":1748380562000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-18032-8_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"ISBN":["9783319180311","9783319180328"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-18032-8_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015]]},"assertion":[{"value":"9 May 2015","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}