{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:19:55Z","timestamp":1750220395976,"version":"3.41.0"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"CHI PLAY","license":[{"start":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T00:00:00Z","timestamp":1633392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2035064"],"award-info":[{"award-number":["2035064"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2021,10,5]]},"abstract":"<jats:p>This article studies the effects and implications of sybils (secondary accounts created by a person in an online platform) through the game World of Tanks from interdisciplinary, mixed-methods perspectives. Considering sybils allows us to access a \"person based'' network, instead of an \"account based\" network, revealing formerly undetected patterns. We move on to behavioral differences between the \"parent\" (the initial account) and \"child\" (those created afterwards) accounts in a sybil relationship. We explore the behavioral patterns of sybils using network, chat, and gameplay data. We find that sybils represent players experimenting with new roles or features without damaging their play record. We find that there are significant behavioral differences between different sybil accounts, and we leverage them to build a machine learning classifier to differentiate sybils. This classifier is able to identify sybil accounts with over 95% accuracy for sybil\/non-sybil, 61% for parent\/child. This study demonstrates the underexplored but rich potential for sybils to improve research and industry practitioners' understandings of user practices and experiences.<\/jats:p>","DOI":"10.1145\/3474704","type":"journal-article","created":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T22:59:48Z","timestamp":1633561188000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["I'll Play on My Other Account"],"prefix":"10.1145","volume":"5","author":[{"given":"Fred","family":"Morstatter","sequence":"first","affiliation":[{"name":"University of Southern California, Marina del Rey, CA, USA"}]},{"given":"Do Own (Donna)","family":"Kim","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]},{"given":"Natalie","family":"Jonckheere","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]},{"given":"Calvin","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]},{"given":"Malika","family":"Seth","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]},{"given":"Dmitri","family":"Williams","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2720187"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/0165551512470051"},{"key":"e_1_2_1_4_1","unstructured":"Sam Chandler. 2016. Why Seal Clubbing Is Good. https:\/\/bit.ly\/seal-clubbing-is-good."},{"volume-title":"Neural information processing systems","author":"Chang Jonathan","key":"e_1_2_1_5_1","unstructured":"Jonathan Chang, Jordan Boyd-Graber, Chong Wang, Sean Gerrish, and David M Blei. 2009. Reading tea leaves: How humans interpret topic models. In Neural information processing systems, Vol. 22. Citeseer, 288--296."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/1202447"},{"volume-title":"Int'l workshop on peer-to-peer systems","author":"Douceur John R","key":"e_1_2_1_7_1","unstructured":"John R Douceur. 2002. The sybil attack. In Int'l workshop on peer-to-peer systems. Springer, 251--260."},{"key":"e_1_2_1_8_1","volume-title":"et almbox","author":"Easley David","year":"2010","unstructured":"David Easley, Jon Kleinberg, et almbox. 2010. Networks, crowds, and markets . Vol. 8. Cambridge university press Cambridge."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.4169\/000298910x523344"},{"key":"e_1_2_1_10_1","volume-title":"Sanjeev R Kulkarni, Kurt Thomas, and Prateek Mittal.","author":"Gao Peng","year":"2018","unstructured":"Peng Gao, Binghui Wang, Neil Zhenqiang Gong, Sanjeev R Kulkarni, Kurt Thomas, and Prateek Mittal. 2018. Sybilfuse: Combining local attributes with global structure to perform robust sybil detection. In IEEE CNS. IEEE, 1--9."},{"key":"e_1_2_1_11_1","volume-title":"mbox","author":"Erving Goffman","year":"1978","unstructured":"Erving Goffman et almbox. 1978. The presentation of self in everyday life .Harmondsworth London."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2014.2316975"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD. 855--864.","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_2_1_14_1","unstructured":"Jessie Cameron Herz. 1997. Joystick nation: how videogames gobbled our money won our hearts and rewired our minds .Abacus."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2015.03.018"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.soscij.2018.12.005"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3410404.3414243"},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Alex Leavitt. 2015. \u201cThis is a Throwaway Account\u201d Temporary Technical Identities and Perceptions of Anonymity in a Massive Online Community. In CSCW. 317--327.","DOI":"10.1145\/2675133.2675175"},{"key":"e_1_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Alex Leavitt Joshua Clark and Dennis Wixon. 2016. Uses of multiple characters in online games and their implications for social network methods. In CSCW. 648--663.","DOI":"10.1145\/2818048.2819980"},{"key":"e_1_2_1_20_1","volume-title":"PecanPy: a fast, efficient, and parallelized Python implementation of node2vec. BioRxiv","author":"Liu Renming","year":"2020","unstructured":"Renming Liu and Arjun Krishnan. 2020. PecanPy: a fast, efficient, and parallelized Python implementation of node2vec. BioRxiv (2020)."},{"key":"e_1_2_1_21_1","volume-title":"I tweet passionately: Twitter users, context collapse, and the imagined audience. New media & society","author":"Marwick Alice E","year":"2011","unstructured":"Alice E Marwick and danah boyd. 2011. I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New media & society , Vol. 13, 1 (2011), 114--133."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.socnet.2018.06.002"},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Bonnie Nardi and Justin Harris. 2006. Strangers and friends: Collaborative play in World of Warcraft. In CSCW. 149--158.","DOI":"10.1109\/LA-WEB.2006.8"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2014.08.023"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.entcom.2017.12.001"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1177\/1555412014537401"},{"key":"e_1_2_1_27_1","volume-title":"creeping, and wall cleaning: Understanding privacy in the age of Facebook. First Monday","author":"Raynes-Goldie Kate","year":"2010","unstructured":"Kate Raynes-Goldie. 2010. Aliases, creeping, and wall cleaning: Understanding privacy in the age of Facebook. First Monday (2010)."},{"key":"e_1_2_1_28_1","volume-title":"Gensim--python framework for vector space modelling","author":"Rehurek Radim","year":"2011","unstructured":"Radim Rehurek and Petr Sojka. 2011. Gensim--python framework for vector space modelling. NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic , Vol. 3, 2 (2011)."},{"key":"e_1_2_1_29_1","unstructured":"Johnny Saldana. 2015. The Coding Manual for Qualitative Researchers .SAGE."},{"key":"e_1_2_1_30_1","volume-title":"Some desirable properties of the Bonferroni correction: is the Bonferroni correction really so bad? American journal of epidemiology","author":"VanderWeele Tyler J","year":"2019","unstructured":"Tyler J VanderWeele and Maya B Mathur. 2019. Some desirable properties of the Bonferroni correction: is the Bonferroni correction really so bad? American journal of epidemiology , Vol. 188, 3 (2019), 617--618."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2019.23226"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8057066"},{"volume-title":"22nd $$USENIX$$ Security Symp. ($$USENIX$$ Security 13). 241--256.","author":"Wang Gang","key":"e_1_2_1_33_1","unstructured":"Gang Wang, Tristan Konolige, Christo Wilson, Xiao Wang, Haitao Zheng, and Ben Y Zhao. 2013. You are how you click: Clickstream analysis for sybil detection. In 22nd $$USENIX$$ Security Symp. ($$USENIX$$ Security 13). 241--256."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1177\/1555412006292616"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1177\/1555412010364983"},{"key":"e_1_2_1_36_1","volume-title":"Qi Li, Yong Cui, and Dawn Song.","author":"Xia Zenghua","year":"2019","unstructured":"Zenghua Xia, Chang Liu, Neil Zhenqiang Gong, Qi Li, Yong Cui, and Dawn Song. 2019. Characterizing and detecting malicious accounts in privacy-centric mobile social networks: A case study. In KDD. 2012--2022."},{"key":"e_1_2_1_37_1","volume-title":"Uncovering social network sybils in the wild. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Yang Zhi","year":"2014","unstructured":"Zhi Yang, Christo Wilson, Xiao Wang, Tingting Gao, Ben Y Zhao, and Yafei Dai. 2014. Uncovering social network sybils in the wild. ACM Transactions on Knowledge Discovery from Data (TKDD) , Vol. 8, 1 (2014), 1--29."},{"volume-title":"Defending against Social Network Sybils with Interaction Graph Embedding. In 2018 IEEE CNS","author":"Yang Zhi","key":"e_1_2_1_38_1","unstructured":"Zhi Yang, Yusi Zhang, and Yafei Dai. 2018. Defending against Social Network Sybils with Interaction Graph Embedding. In 2018 IEEE CNS . IEEE, 1--9."},{"key":"e_1_2_1_39_1","volume-title":"Motivations for play in online games. CyberPsychology & behavior","author":"Yee Nick","year":"2006","unstructured":"Nick Yee. 2006. Motivations for play in online games. CyberPsychology & behavior , Vol. 9, 6 (2006), 772--775."},{"volume-title":"The Proteus paradox: How online games and virtual worlds change us-and how they don't","author":"Yee Nick","key":"e_1_2_1_40_1","unstructured":"Nick Yee. 2014. The Proteus paradox: How online games and virtual worlds change us-and how they don't .Yale University Press."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2008.13"},{"key":"e_1_2_1_42_1","volume-title":"Zheng Yang, Qi Li, Dawn Song, Qian Wang, and Xiao Liang.","author":"Yuan Dong","year":"2019","unstructured":"Dong Yuan, Yuanli Miao, Neil Zhenqiang Gong, Zheng Yang, Qi Li, Dawn Song, Qian Wang, and Xiao Liang. 2019. Detecting fake accounts in online social networks at the time of registrations. In SIGSAC . 1423--1438."},{"key":"e_1_2_1_43_1","volume-title":"Mohammad Ali Abbasi, and Huan Liu","author":"Zafarani Reza","year":"2014","unstructured":"Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu. 2014. Social media mining: an introduction .Cambridge University Press."}],"container-title":["Proceedings of the ACM on Human-Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474704","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474704","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474704","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:43Z","timestamp":1750191523000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474704"}},"subtitle":["The Network and Behavioral Differences of Sybils"],"short-title":[],"issued":{"date-parts":[[2021,10,5]]},"references-count":43,"journal-issue":{"issue":"CHI PLAY","published-print":{"date-parts":[[2021,10,5]]}},"alternative-id":["10.1145\/3474704"],"URL":"https:\/\/doi.org\/10.1145\/3474704","relation":{},"ISSN":["2573-0142"],"issn-type":[{"type":"electronic","value":"2573-0142"}],"subject":[],"published":{"date-parts":[[2021,10,5]]},"assertion":[{"value":"2021-10-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}