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Cheating in online gaming spreads through observation and victimization

Published online by Cambridge University Press:  25 January 2022

Ji Eun Kim  and
Milena Tsvetkova Open the ORCID record for Milena Tsvetkova [Opens in a new window]
Ji Eun Kim
Affiliation:
Department of Methodology, London School of Economics and Political Science, London WC2A 2AE, UK
Milena Tsvetkova*
Affiliation:
Department of Methodology, London School of Economics and Political Science, London WC2A 2AE, UK
*
*Corresponding author. Email: [email protected]
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Abstract

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Antisocial behavior can be contagious, spreading from individual to individual and rippling through social networks. Moreover, it can spread not only through third-party influence from observation, just like innovations or individual behavior do, but also through direct experience, via “pay-it-forward” retaliation. Here, we distinguish between the effects of observation and victimization for the contagion of antisocial behavior by analyzing large-scale digital trace data. We study the spread of cheating in more than a million matches of an online multiplayer first-person shooter game, in which up to 100 players compete individually or in teams against strangers. We identify event sequences in which a player who observes or is killed by a certain number of cheaters starts cheating and evaluate the extent to which these sequences would appear if we preserve the team and interaction structure but assume alternative gameplay scenarios. The results reveal that social contagion is only likely to exist for those who both observe and experience cheating, suggesting that third-party influence and “pay-it-forward” reciprocity interact positively. In addition, the effect is present only for those who both observe and experience more than once, suggesting that cheating is more likely to spread after repeated or multi-source exposure. Approaching online games as models of social systems, we use the findings to discuss strategies for targeted interventions to stem the spread of cheating and antisocial behavior more generally in online communities, schools, organizations, and sports.

Keywords

social contagionantisocial behaviortemporal networksonline multiplayer games
Type
Research Article
Information
Network Science , Volume 9 , Issue 4 , December 2021 , pp. 425 - 442
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

Action Editor: Ulrik Brandes

References

Alayed, H., Frangoudes, F., & Neuman, C. (2013). Behavioral-based cheating detection in online first person shooters using machine learning techniques. In 2013 IEEE conference on computational intelligence in games (CIG) (pp. 1–8).CrossRefGoogle Scholar
Aral, S., & Nicolaides, C. (2017). Exercise contagion in a global social network. Nature Communications, 8(14753).CrossRefGoogle Scholar
Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. PNAS, 106(51), 21544–21549.CrossRefGoogle ScholarPubMed
Bainbridge, W. S. (2007). The scientific research potential of virtual worlds. Science, 317(5837), 472–476.CrossRefGoogle ScholarPubMed
Bartlett, M. Y., & DeSteno, D. (2006). Gratitude and prosocial behavior. Psychological Science, 17(4), 319–325.CrossRefGoogle ScholarPubMed
Bearman, P. (1997). Generalized exchange. American Journal of Sociology, 102(5), 1383–1415.CrossRefGoogle Scholar
Ben-Ner, A., Putterman, L., Kong, F., & Magan, D. (2004). Reciprocity in a two-part dictator game. Journal of Economic Behavior & Organization, 53(3), 333–352.CrossRefGoogle Scholar
Blackburn, J., Kourtellis, N., Skvoretz, J., Ripeanu, M., & Iamnitchi, A. (2014). Cheating in online games: A social network perspective. ACM Transactions on Internet Technology (TOIT).CrossRefGoogle Scholar
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298.CrossRefGoogle ScholarPubMed
Butts, C. T. (2008). 4. A relational event framework for social action. Sociological Methodology, 38(1), 155–200.CrossRefGoogle Scholar
Carrell, S. E., Malmstrom, F. V., & West, J. E. (2008). Peer effects in academic cheating. The Journal of Human Resources, 43(1), 173–207.CrossRefGoogle Scholar
Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329(5996), 1194–1197.CrossRefGoogle Scholar
Centola, D. (2018). How behavior spreads: The science of complex contagions. Princeton, Oxford: Princeton University Press.Google Scholar
Centola, D., & Macy, M. (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113(3), 702–734.CrossRefGoogle Scholar
Chen, V. H. H., & Ong, J. (2018). The rationalization process of online game cheating behaviors. Information, Communication & Society, 21(2), 273–287.CrossRefGoogle Scholar
Cheng, J., Bernstein, M., Danescu-Niculescu-Mizil, C., & Leskovec, J. (2017). Anyone can become a troll: Causes of trolling behavior in online discussions. In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing (pp. 1217–1230). ACM.CrossRefGoogle Scholar
Christakis, N. A., & Fowler, J. H. (2009). Connected: The surprising power of our social networks and how they shape our lives. New York: Little, Brown and Company.Google Scholar
Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology, 58(6), 1015–1026.CrossRefGoogle Scholar
Consalvo, M. (2009). Cheating: Gaining advantage in videogames. Cambridge, MA: MIT Press.Google Scholar
Croft, D. P., Madden, J. R., Franks, D. W., & James, R. (2011). Hypothesis testing in animal social networks. Trends in Ecology & Evolution, 26(10), 502–507.CrossRefGoogle ScholarPubMed
Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51(3), 629–636.CrossRefGoogle Scholar
Dimant, E. (2019). Contagion of pro- and anti-social behavior among peers and the role of social proximity. Journal of Economic Psychology, 73, 66–88.CrossRefGoogle Scholar
Fagan, J., Wilkinson, D. L., & Davies, G. (2007). Social contagion of violence. In Flannery, D., Vazsonyi, A., & Waldman, I. (Eds.), The cambridge handbook of violent behavior (pp. 688–723). Rochester, NY: Cambridge University Press.CrossRefGoogle Scholar
Falk, A., & Fischbacher, U. (2002). “Crime” in the lab-detecting social interaction. European Economic Review, 46(4–5), 859–869.CrossRefGoogle Scholar
Fowler, J. H., & Christakis, N. A. (2010). Cooperative behavior cascades in human social networks. PNAS, 107(12), 5334–5338.CrossRefGoogle ScholarPubMed
Gino, F., Ayal, S., & Ariely, D. (2009). Contagion and differentiation in unethical behavior: The effect of one bad apple on the barrel. Psychological Science, 20(3), 393–398.CrossRefGoogle Scholar
Golder, S. A., & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40(1), 129–152.CrossRefGoogle Scholar
GonzÁlez-BailÓn, S., Borge-Holthoefer, J., Rivero, A., & Moreno, Y. (2011). The dynamics of protest recruitment through an online network. Scientific Reports, 1(197), 1–7.CrossRefGoogle ScholarPubMed
Holme, P., & SaramÄki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.CrossRefGoogle Scholar
Hoobler, J. M., & Brass, D. J. (2006). Abusive supervision and family undermining as displaced aggression. Journal of Applied Psychology, 91(5), 1125–1133.CrossRefGoogle ScholarPubMed
Jordan, J. J., Rand, D. G., Arbesman, S., Fowler, J. H., & Christakis, N. A. (2013). Contagion of cooperation in static and fluid social networks. Plos One, 8(6), e66199.CrossRefGoogle ScholarPubMed
Games, Kakao. (2020). Rules of conduct. Retrieved from https://pubg.game.daum.net/pubg/policy/service/index.daum (Accessed May 27, 2020).Google Scholar
Keizer, K., Lindenberg, S., & Steg, L. (2008). The spreading of disorder. Science, 322(5908), 1681–1685.CrossRefGoogle ScholarPubMed
Kim, J. E. (2020). PUBG gameplay logs (Version 5), figshare. doi: 10.6084/m9.figshare.13077215.v5CrossRefGoogle Scholar
Kizilcec, R. F., Bakshy, E., Eckles, D., & Burke, M. (2018). Social influence and reciprocity in online gift giving. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1–11). CHI’18. Montreal QC, Canada: Association for Computing Machinery.Google Scholar
Kovanen, L., Karsai, M., Kaski, K., KertÉsz, J., & SaramÄki, J. (2011). Temporal motifs in time-dependent networks. Journal of Statistical Mechanics: Theory and Experiment, 11, 11005.CrossRefGoogle Scholar
Kovanen, L., Kaski, K., KertÉsz, J., & SaramÄki, J. (2013). Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. PNAS, 110(45), 18070–18075.CrossRefGoogle ScholarPubMed
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. PNAS, 111(24), 8788–8790.CrossRefGoogle ScholarPubMed
Kwon, K. H., & Gruzd, A. (2017). Is offensive commenting contagious online? Examining public vs interpersonal swearing in response to Donald Trump’s YouTube campaign videos. Internet Research, 27(4), 991–1010.CrossRefGoogle Scholar
Loftin, C. (1986). Assaultive violence as a contagious social process. Bulletin of the New York Academy of Medicine, 62(5), 550–555.Google ScholarPubMed
Lusher, D., Koskinen, J., & Robins, G. (2013). Exponential random graph models for social networks: Theory, methods, and applications. New York, NY: Cambridge University Press.Google Scholar
Malinowski, B. (1920). 51. Kula; the circulating exchange of valuables in the archipelagoes of Eastern New Guinea. Man, 20, 97–105. ArticleType: research-article/Full publication date: July, 1920/Copyright © 1920 Royal Anthropological Institute of Great Britain and Ireland.Google Scholar
McCabe, D. L., Trevino, L. K., & Butterfield, K. D. (2001). Cheating in academic institutions: A decade of research. Ethics & Behavior, 11(3), 219–232.CrossRefGoogle Scholar
Meegan, D. V. (2010). Zero-sum bias: Perceived competition despite unlimited resources. Frontiers in Psychology, 1, 1–7.CrossRefGoogle ScholarPubMed
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298(5594), 824–827.CrossRefGoogle ScholarPubMed
Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment. Science, 341(6146), 647–651.CrossRefGoogle ScholarPubMed
Nishi, A., Shirado, H., Rand, D. G., & Christakis, N. A. (2015). Inequality and visibility of wealth in experimental social networks. Nature, 526(7573), 426–429.CrossRefGoogle ScholarPubMed
Norbutas, L., & Corten, R. (2018). Sustainability of generalized exchange in the sharing economy: The case of the “freecycling” Facebook groups. International Journal of the Commons, 12(1), 111–133.CrossRefGoogle Scholar
Nowak, M. A., & Roch, S. (2007). Upstream reciprocity and the evolution of gratitude. Proceedings of the Royal Society B, 274(1610), 605–610.Google Scholar
Nowak, M. A., & Sigmund, K. (2005). Evolution of indirect reciprocity. Nature, 437(7063), 1291–1298.CrossRefGoogle ScholarPubMed
Paranjape, A., Benson, A. R., & Leskovec, J. (2017). Motifs in temporal networks. In Proceedings of the tenth ACM international conference on web search and data mining (pp. 601–610). ACM.CrossRefGoogle Scholar
Patihis, L., Frenda, S. J., LePort, A. K. R., Petersen, N., Nichols, R. M., Stark, C. E. L., McGaugh, J. L., & Loftus, E. F. (2013). False memories in highly superior autobiographical memory individuals. PNAS, 110(52), 20947–20952.Google ScholarPubMed
Rettinger, D. A., & Kramer, Y. (2009). Situational and personal causes of student cheating. Research in Higher Education, 50(3), 293–313.Google Scholar
Ribeiro, P., & Silva, F. (2014). Discovering colored network motifs. In Contucci, P., Menezes, R., Omicini, A., & Poncela-Casasnovas, J. (Eds.), Complex networks V (pp. 107–118). Studies in Computational Intelligence. New York, NY: Springer International Publishing.Google Scholar
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Simon and Schuster.Google Scholar
Sender, R., Fuchs, S., & Milo, R. (2016). Revised estimates for the number of human and bacteria cells in the body. Plos Biology, 14(8), e1002533.CrossRefGoogle ScholarPubMed
Shaffer, J. P. (1995). Multiple hypothesis testing. Annual Review of Psychology, 46, 561–584.CrossRefGoogle Scholar
Shalizi, C. R., & Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociological Methods & Research, 40(2), 211–239.Google ScholarPubMed
Simpson, B., Harrell, A., Melamed, D., Heiserman, N., & Negraia, D. V. (2018). The roots of reciprocity: Gratitude and reputation in generalized exchange systems. The American Sociological Review, 83(1), 88–110.CrossRefGoogle Scholar
Sinclair, B. (2012). The social citizen: Peer networks and political behavior. Illustrated ed. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60.CrossRefGoogle Scholar
Stadtfeld, C., & Block, P. (2017). Interactions, actors, and time: Dynamic network actor models for relational events. Sociological Science, 4, 318–352.CrossRefGoogle Scholar
Stadtfeld, C., Hollway, J., & Block, P. (2017). Dynamic network actor models: Investigating coordination ties through time. Sociological Methodology, 47(1), 1–40.Google Scholar
Stanca, L. (2009). Measuring indirect reciprocity: Whose back do we scratch? Journal of Economic Psychology, 30(2), 190–202.CrossRefGoogle Scholar
Sutherland, E. H., & Luckenbill, D. (1992). Principles of criminology. Dix Hills, NY: General Hall.Google Scholar
Szell, M., Lambiotte, R., & Thurner, S. (2010). Multirelational organization of large-scale social networks in an online world. Proceedings of the National Academy of Sciences, 107(31), 13636–13641.CrossRefGoogle Scholar
Tsvetkova, M., & Macy, M. (2015). The social contagion of antisocial behavior. Sociological Science, 2, 36–49.CrossRefGoogle Scholar
Tsvetkova, M., & Macy, M. W. (2014). The social contagion of generosity. Plos One, 9(2), e87275.CrossRefGoogle ScholarPubMed
Tsvetkova, M., GarcÍa-Gavilanes, R., & Yasseri, T. (2016). Dynamics of disagreement: Large-scale temporal network analysis reveals negative interactions in online collaboration. Scientific Reports, 6, 36333.CrossRefGoogle ScholarPubMed
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.CrossRefGoogle ScholarPubMed
Wedekind, C., & Milinski, M. (2000). Cooperation through image scoring in humans. Science, 288(5467), 850–852.CrossRefGoogle ScholarPubMed
Widom, C. S. (1989). Does violence beget violence? a critical examination of the literature. Psychological Bulletin, 106(1), 3–28.CrossRefGoogle ScholarPubMed
Wilson, J. Q., & Kelling, G. L. (1982). Broken windows. Atlantic Monthly, 249(3), 29–38.Google Scholar
Woo, J., Kang, S. W., Kim, H. K., & Park, J. (2018). Contagion of cheating behaviors in online social networks. IEEE Access, 6, 29098–29108.CrossRefGoogle Scholar
Wu, Y., & Chen, V. H. H. (2013). A social-cognitive approach to online game cheating. Computers in Human Behavior, 29(6), 2557–2567.CrossRefGoogle Scholar
Yee, N. (2006). Motivations for play in online games. Cyberpsychology & Behavior, 9(6), 772–775.CrossRefGoogle ScholarPubMed
Zuo, X., Gandy, C., Skvoretz, J., & Iamnitchi, A. (2016). Bad apples spoil the fun: Quantifying cheating in online gaming. In Tenth international AAAI conference on web and social media.Google Scholar
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