Towards implicit contextual integrity

Many real incidents demonstrate that users of Online Social Networks need mechanisms that help them manage their interactions by increasing the awareness of the different contexts that coexist in Online Social Networks and preventing users from exchanging inappropriate information in those contexts or disseminating sensitive information from some contexts to others. Contextual integrity is a privacy theory that expresses the appropriateness of information sharing based on the contexts in which this information is to be shared. Computational models of Contextual Integrity assume the existence of well-defined contexts, in which individuals enact pre-defined roles and information sharing is governed by an explicit set of norms. However, contexts in Online Social Networks are known to be implicit, unknown a priori and ever changing; users’ relationships are constantly evolving; and the norms for information sharing are implicit. This makes current Contextual Integrity models not suitable for Online Social Networks. This position paper highlights the limitations of current research to tackle the problem of exchanging inappropriate information and undesired dissemination of information and outlines the desiderata for a new vision that we call Implicit Contextual Integrity

On the phase transition curve in a directed exponential random graph model

We consider a family of directed exponential random graph models parametrized by edges and outward stars. Much of the important statistical content of such models is given by the normalization constant of the models, and in particular, an appropriately scaled limit of the normalization, which is called the free energy. We derive precise asymptotics for the normalization constant for finite graphs. We use this to derive a formula for the free energy. The limit is analytic everywhere except along a curve corresponding to a first order phase transition. We examine unusual behavior of the model along the phase transition curve.Comment: 31 pages, 2 figure

Dynamics of Trust Reciprocation in Heterogenous MMOG Networks

Understanding the dynamics of reciprocation is of great interest in sociology and computational social science. The recent growth of Massively Multi-player Online Games (MMOGs) has provided unprecedented access to large-scale data which enables us to study such complex human behavior in a more systematic manner. In this paper, we consider three different networks in the EverQuest2 game: chat, trade, and trust. The chat network has the highest level of reciprocation (33%) because there are essentially no barriers to it. The trade network has a lower rate of reciprocation (27%) because it has the obvious barrier of requiring more goods or money for exchange; morever, there is no clear benefit to returning a trade link except in terms of social connections. The trust network has the lowest reciprocation (14%) because this equates to sharing certain within-game assets such as weapons, and so there is a high barrier for such connections because they require faith in the players that are granted such high access. In general, we observe that reciprocation rate is inversely related to the barrier level in these networks. We also note that reciprocation has connections across the heterogeneous networks. Our experiments indicate that players make use of the medium-barrier reciprocations to strengthen a relationship. We hypothesize that lower-barrier interactions are an important component to predicting higher-barrier ones. We verify our hypothesis using predictive models for trust reciprocations using features from trade interactions. Using the number of trades (both before and after the initial trust link) boosts our ability to predict if the trust will be reciprocated up to 11% with respect to the AUC

CONSUMERS’ ENDORSEMENT EFFECTS ON MARKETER AND USER-GENERATED CONTENT IN A SOCIAL MEDIA BRAND COMMUNITY

The effects of marketer-generated content (MGC) and user-generated content (UGC) on inducing consumers’ responses have been widely studied as stand-alone main effects. Extending these research, this paper studies the interaction effects of consumers’ endorsements on MGC and UGC posts in a social media brand community (SMBC) of a popular Asian fashion retailer. We examined if passive and active consumers’ endorsements have enhancement effects on MGC/UGC and if they are also effective in inducing consumers’ expenditure by themselves. Passive endorsement refers to “likes” on social network sites (SNS), while active endorsement refers to the more involved act of “commenting” on a post. We found evidence that active endorsements positively moderate the effects of MGC in inducing consumers’ expenditure. However, passive endorsements negatively moderated MGC, making it less effective in inducing expenditure. Interestingly, the results were reversed for UGC whereby passive endorsements positively moderated UGC, while active endorsements negatively moderated UGC in inducing expenditure. Meanwhile, active endorsements through social-tagging on brand fans were found to be very effective, with recipients of social-tags spending $6 more than non-recipients in a particular week. Additional robustness checks on selection bias were conducted, and results remain qualitatively similar

On the Interplay between Social and Topical Structure

People's interests and people's social relationships are intuitively connected, but understanding their interplay and whether they can help predict each other has remained an open question. We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks - who connects with whom - and the set structure of topical affiliations - who is interested in what. In studying this interface, we identify key relationships whereby each of these structures can be understood in terms of the other. The context for our analysis is Twitter, a complex social network of both follower relationships and communication relationships. On Twitter, "hashtags" are used to label conversation topics, and we examine hashtag usage alongside these social structures. We find that the hashtags that users adopt can predict their social relationships, and also that the social relationships between the initial adopters of a hashtag can predict the future popularity of that hashtag. By studying weighted social relationships, we observe that while strong reciprocated ties are the easiest to predict from hashtag structure, they are also much less useful than weak directed ties for predicting hashtag popularity. Importantly, we show that computationally simple structural determinants can provide remarkable performance in both tasks. While our analyses focus on Twitter, we view our findings as broadly applicable to topical affiliations and social relationships in a host of diverse contexts, including the movies people watch, the brands people like, or the locations people frequent.Comment: 11 page