Many systems involve the allocation of rewards for achievements, and these rewards produce a set of incentives that in turn guide behavior. Such effects are visible in many domains from everyday life, and they are increasingly forming a designed aspect of participatory on-line sites through the use of badges and other reward systems. We consider several aspects of the interaction between rewards and incentives in the context of collective effort, including a method for reasoning about on-line user activity in the presence of milestones and badges; and a graph-theoretic framework for analyzing procrastination and other forms of behavior that are inconsistent over time. The talk includes joint work with Ashton Anderson, Dan Huttenlocher, Jure Leskovec, and Sigal Oren
Data brokers have traditionally collected data from businesses, government records, and other publicly available offline sources. While each data source may provide only a few elements about a person's activities, data brokers combine these elements to form a detailed, composite view of the consumer's life. The emergence of social media gives data brokers unprecedented opportunities to enhance their profiles. Data brokers are increasingly interested in combining the information collected from offline sources with information publicly available in social networks to profile not only adults but also children. In this paper, we show how data brokers and other third parties can combine online and offline data sources -- namely, public Facebook profiles and voter registration records -- to create detailed profiles of adults, teens, and children in any target city in the US. We outline and execute an approach that leverages a Facebook user's social ties combined with the city's voter registration records to infer the Facebook users who reside in the city. These inferences enable a data broker to create detailed user profiles, which not only include information publicly available from Facebook but also the user's exact residential address, date and year of birth, and political affiliation. We further show how additional inferences can be made from the combined data. We then discuss how this city attack can be extended to create detailed profiles of minors and children. Finally, we make recommendations to Facebook, municipal authorities, and individuals to decrease the risk of this large-scale privacy breach.
Users today access a multitude of online services---among the most popular of which are online social networks (OSNs)---via both web sites and dedicated mobile applications (apps), using a range of devices (traditional PCs, tablets, and smartphones) that are connected via a variety of networks. The resulting infrastructure makes these services conveniently available anytime and anywhere, enabling them to become an integral part of daily life. As a consequence, users explicitly and implicitly provide a wealth of Personal Information (PI) that reflects several aspects of their life. Service providers monetize this information by selling to third parties (e.g., advertisers). Unfortunately, today, it remains difficult for end users to fully understand the amount and nature of the collected data. Our goal in this paper is to bring visibility into PI collected when accessing online services such as online social networks. This is a major challenge because PI is transferred in a proprietary way by each service. We develop a novel method that can automatically discover various types of PI carried within protocol fields of network traffic; the method includes techniques to filter out potential "containers" that do not actually carry PI and extend the set of containers initially found with additional ones. We evaluate the false positive/negative rates of our proposed method and show examples of interesting findings, including what kind of web sites or apps are more likely to transmit PI and which types of PI are most commonly collected.
Information sharing dynamics of social networks rely on a small set of influencers to effectively reach a large audience. Our recent results and observations demonstrate that the shape and identity of this elite, especially those contributing original content, is difficult to predict. Information acquisition is often cited as an example of a public good. However, this emerging and powerful theory has yet to provably offer qualitative insights on how specialization of users into active and passive participants occurs. This paper bridges, for the first time, the theory of public goods and the analysis of diffusion in social media. We introduce a non-linear model of perishable public goods, leveraging new observations about sharing of media sources. The primary contribution of this work is to show that shelf time, which characterizes the rate at which content get renewed, is a critical factor in audience participation. Our model proves a fundamental dichotomy in information diffusion: While short-lived content has simple and predictable diffusion, long-lived content has complex specialization. This occurs even when all information seekers are ex ante identical and could be a contributing factor to the difficulty of predicting social network participation and evolution.
Diffusion in social networks has been studied extensively in the past few years. Most previous work assumes that the underlying network is a static object that remains unchanged as the diffusion process progresses. However, there are several real-life networks that change dynamically over time. In this paper, we study diffusion on such evolving networks and extend the popular Independent Cascade and Linear Threshold models to account for network evolution. In particular, we introduce two natural variations, a persistent and a transient one, to capture diffusions of different types. We consider the problem of influence maximization where the goal is to select a few influential nodes to initiate a diffusion with maximum spread. We show that, surprisingly, when considering evolving networks the diffusion function is no longer submodular for the transient models, and not even monotone for the transient Independent Cascade model. We also show that, depending on the model, delaying the activation of the initiators may improve diffusion. Our experiments, using three real datasets, demonstrate the effect of network evolution on the diffusion process, and highlight the importance of timing in the selection process.
