Online gaming franchises such as World of Tanks, Defense of the Ancients, and StarCraft have attracted hundreds of millions of users who, apart from playing the game, also socialize with each other through gaming and viewing gamecasts. As a form of User Generated Content (UGC), gamecasts play an important role in user entertainment and gamer education. They deserve the attention of both industrial partners and the academic communities, corresponding to the large amount of revenue involved and the interesting research problems associated with UGC sites and social networks. Although previous work has put much effort into analyzing general UGC sites such as YouTube, relatively little is known about the gamecast sharing sites. In this work, we provide the first comprehensive study of gamecast sharing sites, including commercial streaming-based sites such as Amazon's Twitch.tv and community-maintained replay-based sites such as WoTreplays. We collect and share a novel dataset on WoTreplays that includes more than 380,000 game replays, shared by more than 60,000 creators with more than 1.9 million gamers. Together with an earlier published dataset on Twitch.tv, we investigate basic characteristics of gamecast sharing sites, and we analyze the activities of their creators and spectators. Among our results, we find that (i) WoTreplays and Twitch.tv are both fast-consumed repositories, with millions of gamecasts being uploaded, viewed, and soon forgotten; (ii) both the gamecasts and the creators exhibit highly skewed popularity, with a significant heavy tail phenomenon; and (iii) the upload and download preferences of creators and spectators are different: while the creators emphasize their individual skills, the spectators appreciate team-wise tactics. Our findings provide important knowledge for infrastructure and service improvement, for example, in the design of proper resource allocation mechanisms that consider future gamecasting and in the tuning of incentive policies that further help player retention.@en

Multiplayer Online Games (MOGs) like Defense of the Ancients and StarCraft II have attracted hundreds of millions of users who communicate, interact, and socialize with each other through gaming. In MOGs, rich social relationships emerge and can be used to improve gaming services such as match recommendation and game population retention, which are important for the user experience and the commercial value of the companies who run these MOGs. In this work, we focus on understanding social relationships in MOGs. We propose a graph model that is able to capture social relationships of a variety of types and strengths. We apply our model to real-world data collected from three MOGs that contain in total over ten years of behavioral history for millions of players and matches. We compare social relationships in MOGs across different game genres and with regular online social networks like Facebook. Taking match recommendation as an example application of our model, we propose SAMRA, a Socially Aware Match Recommendation Algorithm that takes social relationships into account. We show that our model not only improves the precision of traditional link prediction approaches, but also potentially helps players enjoy games to a higher extent.@en

User interactions are indispensable for any online network to thrive, especially for BitTorrent‐like and Web real‐time communication‐based distributed online networks that rely on users' collective contributions instead of the help of central servers. User interactions provide fine‐grained information for many applications, such as security enhancement and cooperation promotion. To date, several schemes for estimating user interaction strength in centralized online networks have been proposed. In contrast, we present design, deployment, and analysis of UISE for user interaction strength estimation in distributed online networks. Among the strong points of UISE is that it captures both direct and indirect user interactions, and that it scales with only partial information dissemination. We apply UISE to devise the first distributed scheme for online time estimation and we implement it into Tribler, a distributed online network for media and social applications like file sharing, streaming, and voting. We demonstrate the accuracy and the scalability of UISE with different information dissemination protocols and user behaviors using simulations, emulations, and a real‐world deployment.@en

Many private BitTorrent communities employ Sharing Ratio Enforcement (SRE) schemes to incentivize users to contribute their upload resources. It has been demonstrated that communities that use SRE are greatly oversupplied, i.e., they have much higher seeder-to-leecher ratios than communities in which SRE is not employed. The first order effect of oversupply under SRE is a positive increase in the average downloading speed. However, users are forced to seed for extremely long times to maintain adequate sharing ratios to be able to start new downloads. In this paper, we propose a fluid model to study the effects of oversupply under SRE, which predicts the average downloading speed, the average seeding time, and the average upload capacity utilization for users in communities that employ SRE. We notice that the phenomenon of oversupply has two undesired negative effects: a) Peers are forced to seed for long times, even though their seeding efforts are often not very productive (in terms of low upload capacity utilization); and b) SRE discriminates against peers with low bandwidth capacities and forces them to seed for longer durations than peers with high capacities. To alleviate these problems, we propose four different strategies for SRE, which have been inspired by ideas in social sciences and economics. We evaluate these strategies through simulations. Our results indicate that these new strategies release users from needlessly long seeding durations, while also being fair towards peers with low capacities and maintaining high system-wide downloading speeds.@en

Many private BitTorrent communities employ Sharing Ratio Enforcement (SRE) schemes to incentivize users to contribute. It has been demonstrated that users in private communities are highly dedicated and that they seed much longer than users in communities where SRE is not employed. While most pre- vious studies focus on showing the positive effect of user dedication in achieving high download speed, in this paper we explore the user behaviors in private communities, we argue the reasons for these behaviors, and we demonstrate both the positive and the negative effects of these behaviors. We show that under SRE, users seed for excessively long times to maintain required sharing ratios, but that their seedings are often not very productive (in terms of low upload speed) and that their long seeding times do not necessarily lead to large upload amounts. We find that as users evolve in the community, some users become more committed, in terms of increasing ratios between their seeding and leeching times. In the mean time, some users game the system by keeping risky and low sharing ratios while leeching more often than seeding. Based on these observations, we analyze strategies that alleviate the negative effects of these user behaviors from both the user’s and the community administrator’s perspective.@en

Enhancing reciprocity has been one of the primary motivations for the design of incentive policies in BitTorrent-like P2P systems. Reciprocity implies that peers need to contribute their bandwidth to other peers if they want to receive bandwidth in return. However, the over-provisioning that characterizes today’s BitTorrent communities and the development of many next-generation P2P systems with real-time constraints (e.g., for live and on-demand streaming) suggest that more effort can be devoted to reducing the inequity (i.e., the difference of service received) among peers, rather than only enhancing reciprocity. Inspired by this observation, in this work we analyze in detail several incentive mechanisms that are used in BitTorrent systems, and explore several strategies that influence the balance between reciprocity and equity. Our study shows that (i) reducing inequity leads to a better overall system performance, and (ii) the behavior of seeders (i.e., peers that hold a complete copy of the file and upload it for free) influences whether reciprocity is enhanced or inequity reduced. @en

Many peer-to-peer communities, including private BitTorrent Communities that serve hundreds of thousands of users, utilize credit-based or sharing ratio enforcement schemes to incentivize their members to contribute. In this paper, we analyze the performance of such communities from both the system-level and the user-level perspectives. We show that both credit-based and sharing ratio enforcement policies can lead to system-wide “crunches” or “crashes” where the system seizes completely due to too little or to too much credit, respectively. We explore the conditions that lead to these system pathologies and present a theoretical model that predicts if a community will eventually crunch or crash. We apply this analysis to design an adaptive credit system that automatically adjusts credit policies to maintain sustainability. Given private communities that are sustainable, it has been demonstrated that they are greatly oversupplied in terms of excessively high seeder-to-leecher ratios. We further analyze the user-level performance by studying the effects of oversupply. We show that although achieving an increase in the average downloading speed, the phenomenon of oversupply has three undesired effects: long seeding times, low upload capacity utilizations, and an unfair playing field for late entrants into swarms. To alleviate these problems, we propose four different strategies, which have been inspired by ideas in social sciences and economics. We evaluate these strategies through simulations and demonstrate their positive effects.@en

YouTube-like User Generated Content (UGC) sites are nowadays entertaining over a billion people. Resource provision is essential for these giant UGC sites as they allow users to request videos from a potentially unlimited selection in an asynchronous fashion. Still, the UGC sites are seeking to create new viewing patterns and social interactions that would engage and attract more users and complicate the already rigorous resource provision problem. In this paper, we seek to combine these two tasks by leveraging social features to provide the reference for resource provision. To this end, we conduct an extensive measurement and analysis of BiliBili, a YouTube-like UGC site with enhanced social features including user following, chat replay, and virtual money donation. Based on datasets that capture the complete view of BiliBili---containing over 2 million videos and over 28 million users---we characterize its video repository and user activities, we demonstrate the positive reinforcement between on-line social behavior and upload behavior, we propose graph models that reveal user relationships and high-level social structures, and we successfully apply our findings to build machine-learnt classifiers to identify videos that will need priority in resource provision.@en