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Unsupervised Machine Learning Approach to Analyzing Social Behaviors in an Online Multi-Player Game

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Social dynamics are based on human needs for trust, support, resource sharing, irrespective of whether they operate in real life or in a virtual setting. Massively multiplayer online role-playing games (MMORPGS) serve as enablers of leisurely social activity and are important tools for social interactions. Past research has shown that socially dense gaming environments like MMORPGs can be used to study important social phenomena, which may operate in real life, too. We describe the process of social exploration to entail the following components 1) finding the balance between personal and social time; 2) making a choice between a large number of weak ties or few strong social ties; 3) finding a social group. In general, these are the major determinants of an individual’s social life. This research looks into the phenomenon of social exploration in an activity-based online social environment. We study this process through the lens of the following research questions, 1) What are the different social behavior types? 2) Is there a change in a player’s social behavior over time? 3) Are certain social behaviors more stable than others? 4) Can longitudinal research of player behavior help shed light on the social dynamics and processes in the network? We use an unsupervised machine learning approach to come up with 4 different social behavior types - Lone Wolf, Pack Wolf of Small Pack, Pack Wolf of a Large Pack and Social Butterfly. The types represent the degree of socialization of players in the game. Our research reveals that social behaviors change with time. While lone wolf and pack wolf of small pack are more stable social behaviors, pack wolf of large pack and social butterflies are more transient. We also observe that players progressively move from large groups with weak social ties to settle in small groups with stronger ties.

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Unsupervised Machine Learning Approach to Analyzing Social Behaviors in an Online Multi-Player Game

  1. 1. Unsupervised Machine Learning Approach to Analyzing Social Behaviors in an Online Multi- Player Game An Empirical Study of Social Behaviors and Social Exploration in Multiplayer Online Game. Arpita Chandra Dual Degree Thesis Defense Center for Exact Humanities IIIT – Hyderabad Committee Members Dr. Ponnurangam Kumaraguru (Advisor) Dr. Nimmi Rangaswamy Dr. Radhika Krishnan 1
  2. 2. Outline BACKGROUND AND SETUP ANALYSIS › EverQuest II (MMORPG) › Construction of group instances › Defining feature set & importance of intuitive clusters › Clustering (k-means) RESEARCH MOTIVATION SOCIAL BEHAVIORS DATASET AND METHODOLOGY ANALYSIS 1 2 3 4 › Studying social behaviors › Games as social networks › Importance from a gaming perspective › Defining 4 types of social behaviors › Characteristics for each › Social settling in – from large-weak tied groups to smaller-strong tied ones. › Relationship between social behaviors and churn behavior (attrition). 2
  3. 3. What is Social Exploration? 3
  4. 4. Social Exploration What is the split between social time and alone time? Extent of socialization How many people do they want to socialize with? Breadth of socialization What kind of ties do they want with their neighbors - weak or strong? Depth of socialization How much social activity? Lone time vs Social time How many people to socialize with? Small Group vs Large Group What is the quality of social ties?? Several weak ties vs few strong ties 4
  5. 5. Social Exploration in Literature o Choosing work-group members, during the exploration phase, points of contact are many for information seeking, learning. o Social exploration to find an expert in social expert – “An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps improving the effectiveness of an expert finding system.” o E.W Morisson studied patterns of relationships, learning, integration into a social group of new comers in a work place and when an individual needs to change their social group 5
  6. 6. Research Questions o What are different kinds of social behaviors? o Do players change social behavior overtime? o Are patterns of behavior change indicative of any social phenomenon at play? 6
  7. 7. Gaming Industry estimated to be $116 billion in 2017. $135 billion in 2018. Almost 11% increase. Gaming networks are activity based environments. Provide active socialization. 55.2 million online social gamers only in the United States of America as of 2016. Games as social networks 7 Source - https://www.wepc.com/news/video-game-statistics/#online-gaming
  8. 8. Conventional gaming systems are also going social. Xbox live, PlayStation plus Encouraging socialization in games 8
  9. 9. What roles do games play in real life? Power and future of gaming: her research reveals how gamers have become expert problem solvers and collaborators We can use games design to socially positive ends, be it in our own lives, our communities or our businesses. Video games allow us to create social bonds and connect with others at a level some of us can’t reach in real life. 9
  10. 10. Massively Multi-player Online Role Playing Games MMORPGS – Testbeds for social research 10
  11. 11. Games as warehouses of social data Huge player base ~ more than a billion users worldwide Provide rich social data Online games are a good indicator of human behavior in the real world. 11
  12. 12. EVERQUEST II o Originally developed by Sony Online Entertainment, Nov 2004. o As of Jan, 2005, over 300,000 active users. o Had a subscription based model during it’s initial years. o Subscription packs available for 1,3,6,12 months. 12
  13. 13. Player Activity Logs Took player logs for 4 consecutive weeks for the analysis. o 4 weeks is the natural cycle for a subscription-based model in Everquest II. o Analyze social behavior transformation on a weekly basis for 4 weeks. 13
  14. 14. RQ1: What are different types of social behaviors? 14
  15. 15. Methodology Identify group instances from player logs STEP 1 Feature selection STEP 2 Clustering STEP 3 Parse through the player logs sequentially and identify entries with same log_date, group_size, server_name, location_id Make group instances Calculating different in- game metrics for socialization, enagagement Extracting feature list Clustering using k-means with k from the previous step and feature list from step 2 Determining the number of naturally occurring clusters (k) using the elbow method 15
  16. 16. Group Instances A group instance consisted of entries that logged the same values of server name, log time, location id, and group size 16
  17. 17. Engagement And Socialization Metrics o No. of Group Sessions: This feature denotes the total no. of sessions participated in by player in groups. o No of Lone sessions: Sessions played alone. o Total no. of sessions : Sum of group sessions and lone sessions. o No. of neighbors: Unique number of players played with . Total observed history (T) - 4 weeks. Unit of analysis (∆t) – 1 week What comprises a game session?: Series of in- game activity separated by no more than 30 minutes 17
  18. 18. Noise Reduction o We only take engaged social players who have total no. of sessions > threshold value, τ for each week, defined as: τ = μ(log (total no. of sessions)) – σ (log(total no. of sessions) 𝜏 = 𝜇(𝑙𝑜𝑔(Total no. of sessions)) − 𝜎(𝑙𝑜𝑔 𝑡𝑜𝑡𝑎𝑙 𝑛𝑜. 𝑜𝑓 𝑔𝑟𝑜𝑢𝑝 𝑠𝑒𝑠𝑠𝑖𝑜𝑛𝑠 ) 𝜇 is mean and σ is standard deviation Week 14 18
  19. 19. Importance of intuitive clusters o Computationally, clustering reduces the dimensionality of the dataset. o A number of studies in literature stress upon the importance of producing clusters that are easy to interpret and are intuitive within the framework of the dataset. o Rather than using traditional methods for reducing feature dimensionality like PCA, an interpretable and intuitive feature set was recognized to reflect the social connectedness of players 19
  20. 20. Defining Feature Set o No of neighbors: Unique players a players plays with. o Fraction of group sessions: Ratio of group sessions to total number of sessions o Average Tie Strength: 𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐺𝑟𝑜𝑢𝑝 𝑆𝑒𝑠𝑠𝑖𝑜𝑛𝑠 𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑠 20
  21. 21. Clustering o Used k means to cluster around the features. o Used the elbow method or minimum within cluster sum of squared errors to determine number of clusters (k). 21
  22. 22. Centroid outputs 22
  23. 23. Types of Social Behaviors Based on centroids from k-means 23
  24. 24. Social Behavior Types o Lone Wolf (LW) o Pack Wolf of Small Pack (PWS) o Pack Wolf of Large Pack (PWL) o Social Butterfly (SB) 24
  25. 25. Social Behavior Types Lone Wolf (LW) Pack Wolf of Small Pack (PWS) Pack Wolf of Large Pack (PWL) Social Butterfly (SB) • Spends more time playing alone. • Interacts with a very small number of players. (mean = 6.82) • Has a high tie strength with their neighbors. (2.63) • Interacts with small number of players (mean = 26.35) • Tie strength is lower as compared to lone wolves. (mean = 0.87) • Interacts with a large number of players. (mean = 58.86) • Plays in groups and spends less time playing alone • Tie strength is lower than first two. (mean = 0.41) • Interacts with a very high number of players. (mean = 115.04) • Tie strength is low. (mean = 0.29) • Has a lot of weak connections. 25
  26. 26. Social Behavior Types o Lone Wolf (LW) o Pack Wolf of Small Pack (PWS) o Pack Wolf of Large Pack (PWL) o Social Butterfly (SB) No. of neighbors increases 26
  27. 27. Social Behavior Types o Lone Wolf (LW) o Pack Wolf of Small Pack (PWS) o Pack Wolf of Large Pack (PWL) o Social Butterfly (SB) Tie strength decreases 27
  28. 28. 28
  29. 29. RQ2: Do players change social behavior overtime? 29
  30. 30. Behavior Change 1862 players analyzed Some behaviors are more resilient to change. Change in behaviors from week 14 to week 17 LW PWS PWL SB LW 74.90% 18.70% 5.00% 1.30% PWS 43.70% 37.70% 15.70% 2.90% PWL 19.20% 37.30% 32.90% 10.50% SB 14.00% 29.00% 39.00% 18.00% Week 14 labels Week 17 labels 30
  31. 31. Social Behavior Types o Lone Wolf (LW) o Pack Wolf of Small Pack (PWS) o Pack Wolf of Large Pack (PWL) o Social Butterfly (SB) Propensity to change behavior increases. 31
  32. 32. RQ3: Can longitudinal research of in-game social behaviors shed light on the social dynamics that exist in the network ? Bpaths: What do transition of social behaviors look like? 32
  33. 33. o A behavior path (Bpath) for a player X may look something like this for a period of 4 weeks. e.g BpathX = (PWS, PWS, LW, LW) o Each entry corresponds to behavior in the respective week. Behavior Pathways Bpath 33
  34. 34. Week 14 Week 15 Week 16 Week 17 Common Bpaths SB PWL PWL PWL 12% PWL PWL PWS 7% SB PWL PWL 7% SB SB SB 7% PWL PWS PWS PWS 9% PWL PWL PWS 8% PWL PWL PWL 8% PWL PWS PWS 8% PWS LW LW LW 13% PWS PWS LW 10% PWS PWS PWS 9% PWS LW LW 7% LW LW LW LW 42% PWS LW LW 10% LW PWS LW 10% PWS PWS LW 17% 34
  35. 35. “Social Settling In” What do pathways of behavior change indicate? 35
  36. 36. Towards a smaller group and stronger ties Lone Wolf Pack Wolf of Small Pack Pack Wolf of Large Pack Social Butterfly o 75% remained the same. o Stable behavior o 56.5% exhibited behaviors remained same or became less social. o 49% starting out as PWL became less social but not LW o 22% either remained PWL or shifted SB. o Like high socialization. o 82% of the players who started out as SB became less social. 36
  37. 37. Benefits of high socialization o Degree of socialization is large o Points of contact are many o Learn skills and strategies from different players and groups o Social Exploration o Information Diffusion Also in line with existing literature on social exploration; most people start off by interacting with many people for information gain, learning and familiarity. 37
  38. 38. Social Behavior and Churn How are social behaviors linked with churning behavior or attrition? 38
  39. 39. What is Churn? o Customer churn is when an existing customer, user, player, subscriber leaves the network or stops ends the relationship with a company. 39
  40. 40. Churn Behavior Week 14 to 15 o We see that lone wolves have the highest churn or attrition rate amongst all. o The same trends are seen for all the weeks of the observed history. o This supports the common notion of “player engages player” 40
  41. 41. Churn Behavior Week 15 to 16 41
  42. 42. Churn Behavior Week 16 to 17 42
  43. 43. Churn Behavior – Low Socialization o Less social responsibility towards other players to return to the game. o The social influence of other players or groups is also limited. o Strong group cohesiveness; high tie strength. Even the slight undercurrents that bring about a change in the group structure or dynamics can have a big impact. 43
  44. 44. Churn Behavior - High socialization o This could mean that these players are in the early stages of the social exploration process; actively looking for a group to settle down with. o High number of neighbors; many options to play game sessions with even if a few churn. 44
  45. 45. Related Publications o Chandra, Arpita, Zoheb Borbora, Ponnurangam Kumaraguru, and Jaideep Srivastava. "Finding Your Social Space: Empirical Study of Social Exploration in Multiplayer Online Games." (2019). o Borbora, Zoheb, Arpita Chandra, Ponnurangam Kumaraguru, and Jaideep Srivastava. "On Churn and Social Contagion." networks 26 (2019): 29. 45
  46. 46. Summary, Limitations and Future Work 46
  47. 47. Summary o Examined social behaviors in an MMORPG - Everquest II. o Came up with behavior types based on the characteristics of k-means centroids. o Examined behaviors longitudinally & uncovering social phenomena. o The methodology can be easily replicated for other games and activity based networks. o Trends in behavior change, suggest a process of ’social settling in or social exploration’ operating in the network. o The analysis also reveals that there is a link between social behaviors and churn behavior. As such, this supports the common notion that "player engages player” and that socialization is an important factor for retaining players 47
  48. 48. Limitations o A larger sample size would have been instrumental in making better assessments on behavior evolution and statistically significant most traversed Bpaths. o Furthermore, the analysis was done on one dataset. However, this research could be easily reproduced to study social interactions on similar platforms that facilitate social interactions. 48
  49. 49. Future Work o Finding out the optimal time duration that the process of social settling in takes. o Investigate whether different social personality types take different amounts of time to settle into a social group. 49
  50. 50. Acknowledgements o I would like to express my heartfelt gratitude to Prof P.K for being an amazing ‘Guru’ and mentor in this endeavor. Sir, here’s to you! o Also, a sincere word of thanks to Dr. Jaideep Srivastava and Dr. Zoheb Borbora for their continued support and being wonderful people to work with. I interned with Dr. Srivastava in my sophomore year. o Last but not the least, I would also like to remember Prof. Navjyoti and wish he was here to see me defend my thesis. 50
  51. 51. References 1. Chandra, Arpita, et al. "Finding Your Social Space: Empirical Study of Social Exploration in Multiplayer Online Games." (2019). 2. Hinds, Pamela J., et al. "Choosing work group members: Balancing similarity, competence, and familiarity." Organizational behavior and human decision processes 81.2 (2000): 226-251. 3. Bozzon, Alessandro, et al. "Choosing the right crowd: expert finding in social networks." Proceedings of the 16th International Conference on Extending Database Technology. ACM, 2013. 4. Morrison, Elizabeth Wolfe. "Newcomers' relationships: The role of social network ties during socialization." Academy of management Journal 45.6 (2002): 1149-1160. 5. Williams, Dmitri, et al. "The virtual worlds exploratorium: Using large-scale data and computational techniques for communication research." Communication Methods and Measures 5.2 (2011): 163-180. - ”In a partnership with a corporation that hosts an MMO, a 20-person team of scholars is engaged in the study of behavior within a game and also game activities that parallel those in “real life” (e.g., economic activity, social networking, group processes). ” 6. Romero, Margarida, Mireia Usart, and Michela Ott. "Can serious games contribute to developing and sustaining 21st century skills?." Games and Culture 10.2 (2015): 148-177. 7. Shim, Kyong Jin, and Jaideep Srivastava. "Behavioral profiles of character types in EverQuest II." Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games. IEEE, 2010. 51
  52. 52. References 8. Bauckhage, Christian, Anders Drachen, and Rafet Sifa. "Clustering game behavior data." IEEE Transactions on Computational Intelligence and AI in Games 7.3 (2014): 266-278. - The goal of user behavior analysis in game business intelligence is an interpretable representation of the data at hand and the patterns residing in the data, as the representation basically has to assist a human in analyzing huge amounts of game data 9. MLADrachen, Anders, et al. "Guns, swords and data: Clustering of player behavior in computer games in the wild." 2012 IEEE conference on Computational Intelligence and Games (CIG). IEEE, 2012. – “Interpretability which is important in a practical development context, where the results of a clustering analysis should be as easy as possible to interpret” 10. Borbora, Zoheb, Arpita Chandra, Ponnurangam Kumaraguru, and Jaideep Srivastava. "On Churn and Social Contagion." networks 26 (2019): 29. 11. https://www.wepc.com/news/video-game-statistics/#online-gaming 12. Farhangi, Sanaz. "Reality is broken to be rebuilt: how a gamer’s mindset can show science educators new ways of contribution to science and world?." (2012): 1037-1044. 13. Kawale, Jaya, Aditya Pal, and Jaideep Srivastava. "Churn prediction in MMORPGs: A social influence based approach." 2009 International Conference on Computational Science and Engineering. Vol. 4. IEEE, 2009. 52
  53. 53. Thank You 53

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