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Middleware for social computing: a roadmap

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TLDR
This work identifies three societal grand challenges that are likely to drive future research in social computing and elaborate on how the middleware community can help address them.
Abstract
Social computing broadly refers to supporting social behaviours using computational systems. In the last decade, the advent of Web 2.0 and its social networking services, wikis, blogs, and social bookmarking has revolutionised social computing, creating new online contexts within which people interact socially (social networking). With the pervasiveness of mobile devices and embedded sensors, we stand at the brink of another major revolution, where the boundary between online and offline social behaviours blurs, providing opportunities for (re)defining social conventions and contexts once again. But opportunities come with challenges: can middleware foster the engineering of social software? We identify three societal grand challenges that are likely to drive future research in social computing and elaborate on how the middleware community can help address them.

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Journal ArticleDOI

Architectural implications of smart city business models: an evolutionary perspective

TL;DR: The architectural evolution required to ensure that the rollout and deployment of smart city technologies is smooth through acknowledging and integrating the strengths of both the system architectures proposed is discussed.
Journal ArticleDOI

Delete: The virtue of forgetting in the digital age

TL;DR: Mayer-Schonberger as discussed by the authors is the director of the Information and Informatics Institute at Princeton University, New Jersey, US$24.95 (hardback), ISBN 978•0•691•13861•9
Journal ArticleDOI

S-Aframe: Agent-Based Multilayer Framework With Context-Aware Semantic Service for Vehicular Social Networks

TL;DR: A practical implementation and experimental evaluations of S-Aframe are presented to demonstrate its reliability and efficiency in terms of computation and communication performance on popular mobile devices and a VSN-based smart ride application is developed to demonstrate the functionality and practical usefulness of the framework.
Proceedings ArticleDOI

Torwards context-aware mobile crowdsensing in vehicular social networks

TL;DR: A novel application-oriented service collaboration (ASCM) model is introduced which can automatically match multiple users with multiple mobile crowd sensing tasks in VSNs in an efficient manner and a context information management model is proposed that aims to enable the mobile community sensing applications to autonomously match appropriate service and information with different users (requesters and participants) in crowdsensing.
Proceedings ArticleDOI

Taking Brazil's pulse: tracking growing urban economies from online attention

TL;DR: It is shown that a city's glocality, measured with social media data, effectively signals the city's economic well-being.
References
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Proceedings ArticleDOI

Energy-efficient rate-adaptive GPS-based positioning for smartphones

TL;DR: RAPS is presented, rate-adaptive positioning system for smartphone applications, based on the observation that GPS is generally less accurate in urban areas, so it suffices to turn on GPS only as often as necessary to achieve this accuracy.
Proceedings ArticleDOI

EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

TL;DR: It is shown how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
Journal ArticleDOI

Inferring social ties from geographic coincidences

TL;DR: A framework for quantifying the answers to questions about social ties between people is developed, and this framework is applied to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie.
Proceedings ArticleDOI

Our Twitter Profiles, Our Selves: Predicting Personality with Twitter

TL;DR: It is argued that being able to predict user personality goes well beyond the initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media.
Proceedings ArticleDOI

Anonysense: privacy-aware people-centric sensing

TL;DR: AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model.
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