Jeremy Blackburn

A Simulation Study of a New Green BitTorrent

The use of P2P technologies, such as BitTorrent, to distribute legal content to consumers is actively being explored as a means of reducing both file download times and the energy consumption of data centers. This approach pushes the energy use out of the data centers and into the homes of content consumers (who are also then content distributors). The current BitTorrent protocol requires that clients must be fully powered- on to be participating members in a swarm. In this paper, we show that simple changes to the BitTorrent protocol, including long-lived knowledge of sleeping peers and a new wake-up semantic, can enable clients to sleep when not actively downloading or uploading yet still be responsive swarm members. Using ns-2 we simulate a green BitTorrent swarm. We show that significant energy savings are achievable with only a small performance penalty in increased file download time.

Prometheus: user-controlled P2P social data management for socially-aware applications

Recent Internet applications, such as online social networks and user-generated content sharing, produce an unprecedented amount of social information, which is further augmented by location or collocation data collected from mobile phones. Unfortunately, this wealth of social information is fragmented across many different proprietary applications. Combined, it could provide a more accurate representation of the social world, and it could enable a whole new set of socially-aware applications. We introduce Prometheus, a peer-to-peer service that collects and manages social information from multiple sources and implements a set of social inference functions while enforcing user-defined access control policies. Prometheus is socially-aware: it allows users to select peers that manage their social information based on social trust and exploits naturally-formed social groups for improved performance. We tested our Prometheus prototype on PlanetLab and built a mobile social application to test the performance of its social inference functions under realtime constraints. We showed that the social-based mapping of users onto peers improves the service response time and high service availability is achieved with low overhead.

STFU NOOB!: predicting crowdsourced decisions on toxic behavior in online games

One problem facing players of competitive games is negative, or toxic, behavior. League of Legends, the largest eSport game, uses a crowdsourcing platform called the Tribunal to judge whether a reported toxic player should be punished or not. The Tribunal is a two stage system requiring reports from those players that directly observe toxic behavior, and human experts that review aggregated reports. While this system has successfully dealt with the vague nature of toxic behavior by majority rules based on many votes, it naturally requires tremendous cost, time, and human efforts. In this paper, we propose a supervised learning approach for predicting crowdsourced decisions on toxic behavior with large-scale labeled data collections; over 10 million user reports involved in 1.46 million toxic players and corresponding crowdsourced decisions. Our result shows good performance in detecting overwhelmingly majority cases and predicting crowdsourced decisions on them. We demonstrate good portability of our classifier across regions. Finally, we estimate the practical implications of our approach, potential cost savings and victim protection.

Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games

In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.

Cheating in Online Games: A Social Network Perspective

Online gaming is a multi-billion dollar industry that entertains a large, global population. One unfortunate phenomenon, however, poisons the competition and spoils the fun: cheating. The costs of cheating span from industry-supported expenditures to detect and limit it, to victims’ monetary losses due to cyber crime. This article studies cheaters in the Steam Community, an online social network built on top of the world’s dominant digital game delivery platform. We collected information about more than 12 million gamers connected in a global social network, of which more than 700 thousand have their profiles flagged as cheaters. We also observed timing information of the cheater flags, as well as the dynamics of the cheaters’ social neighborhoods. We discovered that cheaters are well embedded in the social and interaction networks: their network position is largely indistinguishable from that of fair players. Moreover, we noticed that the number of cheaters is not correlated with the geographical, real-world population density, or with the local popularity of the Steam Community. Also, we observed a social penalty involved with being labeled as a cheater: cheaters lose friends immediately after the cheating label is publicly applied. Most importantly, we observed that cheating behavior spreads through a social mechanism: the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future. This allows us to propose ideas for limiting cheating contagion.

Mean Birds: Detecting Aggression and Bullying on Twitter

In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC.

Is the Web HTTP/2 Yet?

Version 2 of the Hypertext Transfer Protocol (HTTP/2) was finalized in May 2015 as RFC 7540. It addresses well-known problems with HTTP/1.1 (e.g., head of line blocking and redundant headers) and introduces new features (e.g., server push and content priority). Though HTTP/2 is designed to be the future of the web, it remains unclear whether the web will—or should—hop on board. To shed light on this question, we built a measurement platform that monitors HTTP/2 adoption and performance across the Alexa top 1 million websites on a daily basis. Our system is live and up-to-date results can be viewed at [1]. In this paper, we report findings from an 11 month measurement campaign (November 2014 – October 2015). As of October 2015, we find 68,000 websites reporting HTTP/2 support, of which about 10,000 actually serve content with it. Unsurprisingly, popular sites are quicker to adopt HTTP/2 and 31 % of the Alexa top 100 already support it. For the most part, websites do not change as they move from HTTP/1.1 to HTTP/2; current web development practices like inlining and domain sharding are still present. Contrary to previous results, we find that these practices make HTTP/2 more resilient to losses and jitter. In all, we find that 80 % of websites supporting HTTP/2 experience a decrease in page load time compared with HTTP/1.1 and the decrease grows in mobile networks.

The Social Hourglass: An Infrastructure for Socially Aware Applications and Services

As the Internets hourglass architecture connects various resources to various applications, an infrastructure that collects information from various social signals can support an ever-evolving set of socially aware applications and services. Among the proposed infrastructures features are social sensors to capture and interpret social signals from user interactions, a personal social information aggregator, and a set of social-inference functions as its API for social applications.

Linguistic Analysis of Toxic Behavior in an Online Video Game

In this paper we explore the linguistic components of toxic behavior by using crowdsourced data from over 590 thousand cases of accused toxic players in a popular match-based competition game, League of Legends. We perform a series of linguistic analyses to gain a deeper understanding of the role communication plays in the expression of toxic behavior. We characterize linguistic behavior of toxic players and compare it with that of typical players in an online competition game. We also find empirical support describing how a player transitions from typical to toxic behavior. Our findings can be helpful to automatically detect and warn players who may become toxic and thus insulate potential victims from toxic playing in advance.

Last call for the buffet: economics of cellular networks

Voice and data traffic growth over the last several years has become a major challenge for cellular operators with a direct impact on revenues, infrastructure investments, and end-user performance. The economics of these operators depend on various incentives used to attract users in the form of unlimited, buffet-like voice/sms/data packages. However, our understanding of the effects of user behavior under these offerings on operator revenues/costs remains poor. Using two years of detailed usage information of ~1 million users across three services, voice, sms and data, combined with payment and cost information, we study how user behavior affects the economics of cellular operators. We discover that around 20% of the users consume more resources than what they pay for and hence are non-profitable. In addition to the individual user behavior, we study how the user interactions in the call graph affect the operators revenues and cost, drawing on tools from social network analysis. We develop a framework that incorporates both the individual and social user behavior for studying how volume caps influence the revenues and the traffic costs. Using this framework we empirically show that volume caps can increase the difference between the revenues and the traffic costs of the studied operator by a factor of 2, while affecting only 16% of the existing user base.