Erez Shmueli

Persuasion is at the core of norm creation, emergence of collective action, and solutions to 'tragedy of the commons' problems. In this paper, we show that the directionality of friendship ties affect the extent to which individuals can influence the behavior of each other. Moreover, we find that people are typically poor at perceiving the directionality of their friendship ties and that this can significantly limit their ability to engage in cooperative arrangements. This could lead to failures in establishing compatible norms, acting together, finding compromise solutions, and persuading others to act. We then suggest strategies to overcome this limitation by using two topological characteristics of the perceived friendship network. The findings of this paper have significant consequences for designing interventions that seek to harness social influence for collective action.

Spam in Online Social Networks (OSNs) is a sys-temic problem that imposes a threat to these services in terms of undermining their value to advertisers and potential investors , as well as negatively affecting users' engagement. As spammers continuously keep creating newer accounts and evasive techniques upon being caught, a deeper understanding of their spamming strategies is vital to the design of future social media defense mechanisms. In this work, we present a unique analysis of spam accounts in OSNs viewed through the lens of their behavioral characteristics. Our analysis includes over 100 million messages collected from Twitter over the course of one month. We show that there exist two behaviorally distinct categories of spammers and that they employ different spamming strategies. Then, we illustrate how users in these two categories demonstrate different individual properties as well as social interaction patterns. Finally, we analyze the detectability of spam accounts with respect to three categories of features, namely, content attributes, social interactions, and profile properties.

Active honeytokens are fake digital data objects planted among real data objects and used in an attempt to detect data misuse by insiders. In this paper we are interested in understanding how users (e.g., employees) behave when interacting with honeytokens, specifically addressing the following questions: (1) Can users distinguish genuine data objects from honeytokens? and (2) How does the user's behavior and tendency to misuse data change when he/she is aware of the use of honeytokens? First, we present an automated and generic method for generating the honeytokens that are used in the subsequent behavioral studies. The results of the first study indicate that it is possible to automatically generate honeytokens that are difficult for users to distinguish from real tokens. The results of the second study unexpectedly show that users did not behave differently when informed in advance that honeytokens were planted in the database and that these honeytokens would be monitored to detect illegitimate behavior. These results can inform security system designers about the type of environmental variables that affect people's data misuse behavior and how to generate honeytokens that evade detection.

In this paper, we exploit different facets of the Friends and Family study to deal with two personality-related tasks of paramount importance for the user modeling and ubiquitous computing fields. 2 Bruno Lepri et al. First, we propose and validate an approach for automatic classification of personality traits based on the ego-networks' structural characteristics. Our classification results show that i) mobile phones-based behavioral data can be superior to survey ones for the purposes of personality classification from structural network properties and ii) particular feature set/network type combinations promise to perform better with given personality traits. Then, we investigate the mediating role played by personality in the context of inducing behavioral change, specifically increasing daily physical activity using social strategies (social comparison and peer pressure). Our results confirm the role played by Extraversion and Neuroticism. Extroverts exposed to a social comparison strategy are positively associated with an increase in physical activity level, while they tend to decrease physical activity level if they are exposed to a peer pressure intervention strategy. Regarding Neuroticism dimension, neurotic people tend to increase their physical daily activity level if they are exposed to a social comparison strategy. Our findings may have implications in designing personality-based behavioral change strategies and suggest to incorporate users' personality models in the implementation of persuasive systems.

Optimizing the use of available resources is one of the key challenges in activities that consist of interactions with a large number of " target individuals " , with the ultimate goal of "winning" as many of them as possible, such as in marketing, service provision, political campaigns, or homeland security. Typically, the cost of interactions is monotonically increasing such that a method for maximizing the performance of these campaigns is required. In this paper we propose a mathematical model to compute an optimized campaign by automatically determining the number of interacting units and their type, and how they should be allocated to different geographical regions in order to maximize the campaign's performance. We validate our proposed model using real world mobility data.

Recommender systems have become extremely common in recent years, and are utilized in a variety of domains such as movies, music , news, products, restaurants, etc. While a typical recommender system bases its recommendations solely on users' preference data collected by the system itself, the quality of recommendations can signiicantly be improved if several recommender systems (or vendors) share their data. However, such data sharing poses signiicant privacy and security challenges, both to the vendors and the users. In this paper we propose secure protocols for distributed item-based Collaborative Filtering. Our protocols allow to compute both the predicted ratings of items and their predicted rankings, without compromising privacy nor predictions' accuracy. Unlike previous solutions in which the secure protocols are executed solely by the vendors, our protocols assume the existence of a mediator that performs intermediate computations on encrypted data supplied by the vendors. Such a mediated seeing is advantageous over the non-mediated one since it enables each vendor to communicate solely with the mediator. is yields reduced communication costs and it allows each vendor to issue recommendations to its clients without being dependent on the availability and willingness of the other vendors to collaborate.

One highly studied aspect of social networks is the identification of influential nodes that can spread ideas in a highly efficient way. The vast majority of works in this field have investigated the problem of identifying a set of nodes, that if "seeded" simultaneously, would maximize the information spread in the network. Yet, the timing aspect, namely, finding not only which nodes should be seeded but also when to seed them, has not been sufficiently addressed. In this work, we revisit the problem of network seeding and demonstrate by simulations how an approach takes takes into account the timing aspect, can improve the rates of spread by over 23% compared to existing seeding methods. Such an approach has a wide range of applications, especially in cases where the network topology is easily accessible.