290 research outputs found

    SOCIAL NETWORK INFLUENCE ON RIDESHARING, DISASTER COMMUNICATIONS, AND COMMUNITY INTERACTIONS

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    The complex topology of real networks allows network agents to change their functional behavior. Conceptual and methodological developments in network analysis have furthered our understanding of the effects of interpersonal environment on normative social influence and social engagement. Social influence occurs when network agents change behavior being influenced by others in the social network and this takes place in a multitude of varying disciplines. The overarching goal of this thesis is to provide a holistic understanding and develop novel techniques to explore how individuals are socially influenced, both on-line and off-line, while making shared-trips, communicating risk during extreme weather, and interacting in respective communities. The notion of influence is captured by quantifying the network effects on such decision-making and characterizing how information is exchanged between network agents. The methodologies and findings presented in this thesis will benefit different stakeholders and practitioners to determine and implement targeted policies for various user groups in regular, special, and extreme events based on their social network characteristics, properties, activities, and interactions

    User Feedback in Controllable and Explainable Social Recommender Systems: a Linguistic Analysis. In: Proceedings of Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

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    Controllable and explainable intelligent user interfaces have been used to provide transparent recommendations. Many researchers have explored interfaces that support user control and provide explanations of the recommendation process and models. To extend the works to real-world decision-making scenarios, we need to understand further the users' mental models of the enhanced system components. In this paper, we make a step in this direction by investigating a free form feedback left by users of social recommender systems to specify the reasons of selecting prompted social recommendations. With a user study involving 50 subjects (N=50), we present the linguistic changes in using controllable and explainable interfaces for a social information-seeking task. Based on our findings, we discuss design implications for controllable and explainable recommender systems

    Sybil attacks against mobile users: friends and foes to the rescue

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    Collaborative applications for co-located mobile users can be severely disrupted by a sybil attack to the point of being unusable. Existing decentralized defences have largely been designed for peer-to-peer networks but not for mobile networks. That is why we propose a new decentralized defence for portable devices and call it MobID. The idea is that a device manages two small networks in which it stores information about the devices it meets: its network of friends contains honest devices, and its network of foes contains suspicious devices. By reasoning on these two networks, the device is then able to determine whether an unknown individual is carrying out a sybil attack or not. We evaluate the extent to which MobID reduces the number of interactions with sybil attackers and consequently enables collaborative applications.We do so using real mobility and social network data. We also assess computational and communication costs of MobID on mobile phones

    Linking information and people in a social system for academic conferences

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    This paper investigates the feasibility of maintaining a social information system to support attendees at an academic conference. The main challenge of this work was to create an infrastructure where users’ social activities, such as bookmarking, tagging, and social linking could be used to enhance user navigation and maximize the users’ ability to locate two important types of information in conference settings: presentations to attend and attendees to meet. We developed Conference Navigator 3, a social conference support system that integrates a conference schedule planner with a social linking service. We examined its potential and functions in the context of a medium-scale academic conference. In this paper, we present the design of the system’s socially enabled features and report the results of a conference-based study. Our study demonstrates the feasibility of social information systems for supporting academic conferences. Despite the low number of potential users and the short timeframe in which conferences took place, the usage of the system was high enough to provide sufficient data for social mechanisms. The study shows that most critical social features were highly appreciated and used, and provides direction for further research

    Mate Choice Patterns in Social and Non-Social Decision-Making Domains

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    Thesis (Ph.D.) - Indiana University, Department of Psychological and Brain Sciences and the Cognitive Science Program, 2019Humans are a fundamentally social species, and an individual may have social ties of many flavors. One social domain, mate or romantic partner choice, has been thoroughly examined, but others remain relatively understudied. How do our choice patterns vary between different social domains? In this work, I argue that although choice constraints vary between social domains (e.g. a collaborator, spouse, friend, mentor, or dodgeball teammate), the fundamental patterns of choice are ultimately similar. In this dissertation, I present studies of three different choice areas. First, I compare the search for non-social resources such as food (i.e. Optimal Foraging Theory; OFT) with that for a romantic partner to produce a theory-driven framework for mate choice as a foraging problem. Mate foragers demonstrated sensitivity to search costs as predicted by OFT, where those searching longest for their first marriage (but not cohabitations) had a decreased risk of relationship dissolution. However, periods of relationships and search also covaried in ways unexpected by OFT. Next, I tested for the presence of two common patterns in romantic partner choice: positive assortment (e.g. homophily) and the stated-revealed preference gap (inconsistency between one’s stated preferences and the actual traits of a chosen partner). I demonstrated these patterns in two social domains: academic collaborator choice and companion animal choice. I tested whether homophily was the best predictor of academic collaborations forming. I held three academic speed-networking events, a modified form of speed-dating. Pairs were assigned experimentally based on the similarity of academics’ current research and complementarity of desired vs current knowledge. These manipulations did not significantly impact collaboration rates; rather, believing a partner’s research was similar was predictive of collaboration, suggesting homophily has a nuanced role in collaboration formation. I then examined dog choice in animal shelters. Comparing the traits of a newly adopted dog to the stated preferences of their adopter, adopters perceived their dog to fulfill their stated preferences at above-chance rates. These adopter-dog pairs also exhibited weak positive assortment of personality. I summarize the implications of exapting choice mechanisms which are appropriate for one adaptive domain to novel social domains with different choice constraints

    Effects of Gatekeeping on the Diffusion of Information

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    This study proposes a theoretical model of information diffusion using the conceptual framework of Gatekeeping Theory (Shoemaker & Vos, 2009). Diffusion is a process by which elements are distributed through a social system (Rogers, 2003; Kadushin, 2012). This model builds on previous diffusion research and incorporates constructs of authority and vivid information, novel to the domain. To test the fit of the model, Twitter data derived using data mining techniques are utilized. Specifically, messages posted to Twitter relating to the 2013 Consumer Electronics (CES) conference are mined. Essentially, this study focuses on the diffusion of technology information through a popular social medium, Twitter. From these messages, the network was be visualized and diffusion paths were determined using network analysis. A test of the model was conducted to determine fit using structural equation modeling
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