Mobile phone sensing systems : a survey

Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction.

Online social capital : mood, topical and psycholinguistic analysis

Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors' mood, of a large corpus of blog posts, to analyze the aspect of social capital in social media communities. Using data collected from Live Journal, we find that bloggers with lower social capital have fewer positive moods and more negative moods than those with higher social capital. It is also found that people with low social capital have more random mood swings over time than the people with high social capital. Significant differences are found between low and high social capital groups when characterized by a set of latent topics and psycholinguistic features derived from blogposts, suggesting discriminative features, proved to be useful for classification tasks. Good prediction is achieved when classifying among social capital groups using topic and linguistic features, with linguistic features are found to have greater predictive power than latent topics. The significance of our work lies in the importance of online social capital to potential construction of automatic healthcare monitoring systems. We further establish the link between mood and social capital in online communities, suggesting the foundation of new systems to monitor online mental well-being.

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.

Connectivity, online social capital, and mood : a Bayesian nonparametric analysis

Social capital indicative of community interaction and support is intrinsically linked to mental health. Increasing online presence is now the norm. Whilst social capital and its impact on social networks has been examined, its underlying connection to emotional response such as mood, has not been investigated. This paper studies this phenomena, revisiting the concept of “online social capital†in social media communities using measurable aspects of social participation and social support. We establish the link between online capital derived from social media and mood, demonstrating results for different cohorts of social capital and social connectivity. We use novel Bayesian nonparametric factor analysis to extract the shared and individual factors in mood transition across groups of users of different levels of connectivity, quantifying patterns and degree of mood transitions. Using more than 1.6 million users from Live Journal, we show quantitatively that groups with lower social capital have fewer positive moods and more negative moods, than groups with higher social capital. We show similar effects in mood transitions. We establish a framework of how social media can be used as a barometer for mood. The significance lies in the importance of online social capital to mental well-being in overall. In establishing the link between mood and social capital in online communities, this work may suggest the foundation of new systems to monitor online mental well-being.

Geo-social journalism: reorienting the study of small commercial newspapers in a digital environment

This paper begins by problematizing the use of “community” to define and theorize small commercial media outlets that have geography as their primary characteristic—particularly hyper local and small traditional newspapers connected to larger media organizations in digital space. We then extend the concept of “geo-social news” to outline “geo-social journalism” as a specific form of news work currently grouped under the “community media” umbrella. Geo-social is a concept for exploring how small commercial newspapers change as media technologies evolve. It offers a framework for understanding how these news outlets and audiences connect via the notion of “sense of place”. It can also be used as a lens for theorizing their role in social flows and movements and as nodes in the global media network. The practice of “geo-social journalism”, meanwhile, has two dimensions. Firstly, journalists must engage with the land (environment/agriculture/industry), populations, histories and cultures of the places they report news. Secondly, it involves connections and understandings of the shifting constellations of global and national systems, issues and relationships of the digital era. Finally, this paper argues that by its very nature, “geo-social journalism” eschews theoretical universalizing and instead demands fine-grained analyses of the specific dynamic of each “geo-social” publication, its setting and the practices which shape it and it in turn shapes.

A framework for social networked architectural education

Students engage in Social Networks (SN) as a form of interaction with friends and tutors, as news or learning resource, to make their voices heard or to listen to other views and many more. Online SN work in close association with offline SN to form a blended social environment that greatly enables and enhances students' learning. Some Schools of Architecture have struggled or failed to engage in the potential of SN or their respective University's online Learning Management Systems (LMS). Despite efforts to facilitate blended learning environments or to engage students in problem-based learning activities architectural education often fails to tap into the rich resources that online social learning environments offers through their collective and social intelligence of its users. This paper proposes a framework for SN architectural education that provides opportunities for linking the academic LMS with private or professional SN such that it enhances the learning experience and deepens the knowledge of the students. The paper proposes ways of utilising SN supported learning environments in other areas of the curriculum and concludes with directions of how this framework can be employed in professional settings.

The dependent variable in social media use

What is the dependent variable in social media use? From a research perspective, this is a pertinent question to help explain and understand the behaviors that underpin the widespread adoption and use of social media throughout society. From a practical
perspective, the question is relevant for social media technology providers, for businesses that use social media, and community organizations that turn towards social media to reach out to their constituents. We propose the construct 'sense of community' as the dependent variable, which is reflected in four sub-constructs related to the behaviors of social media users. These behaviors are information seeking, hedonic activities, sustaining of strong ties and extending weak ties. Empirical evidence for these constructs comes from a survey of social media use by 18-25 year-olds in Indonesia, a ,country with exceptionally high utilization of social media. We outline practical implications of the  findings and areas for further theoretical development.

Factories of the future : a system of systems engineering perspective

Factories of the Future will be distinguished by intelligent machines, automation, human factors integration and knowledge management. Modelling and simulation is recognised as a key enabling technology essential to economic, social and environmental sustainability of future manufacturing systems. This talk will explore the history, recent achievements and directions in modelling and simulation for 21st century factories and supply chains. A systems science approach is employed, from stakeholder engagement through participative modelling to self-tuning and self-assembling simulations. Our contributions lower the cost of the application of modelling and simulation to manufacturing processes, enabling real time planning, dynamic risk analysis, dashboards and 3D visualisation. This realisation of the virtual factory integrates human factors and decisions into the core technology platform. The implications to future manufacturing enterprises are explored through a series of case studies from aerospace, mining and small and medium manufacturing enterprises.

A multilevel social network perspective on IT adoption

Adoption of technologies has long been a key area of research in the information systems (IS) discipline, and researchers have thus been interested in the attributes, beliefs, intentions, and behaviors of individuals and organisations that could explain information and communication technology (ICT) adoption. The focal unit of adoption has mainly been individuals and organisations, however, research at group or social network level as well as the inter-organizational level have recently gained increased interest from IS researchers. This recent focus supports the view of the world as being the sum of all relations. Various social network theories exist that seek to emphasize different proficiencies of social networks and explain theoretical mechanisms for behavior in social networks. The core idea of these theories is that social networks are valuable, and the relations among actors affect the behavior of individuals, groups, organizations, industries, and societies. IS researchers have also found that social network theory can help explain technology adoption. Some researchers, in addition, acknowledge that most adoption situations involve phenomena occuring at multiple levels, yet most technology adoption research applies a single level of analysis. Multilevel research can address the levels of theory, measurement, and analysis required to fully examining research questions. This paper therfore adapts the Coleman diagram into the Multi-level Framework of Technology. Adoption in order to explain how social network theory, at the individual and social network level, can help explain adoption of ICT. As Coleman (1990) attempts to create a link between the micro and macro level in a holistic manner, his approach is applicable in explaining ICT adoption

Early adoption of mobile devices : a social network perspective

As mobile devices have become the personal information-processing interface of choice, many individuals seem to swiftly follow fashion. Yet, the literature is silent on how early adopters of mobile devices overcome uncertainties related to shifts in technology. Based on purposive sampling, this paper presents detailed insights into why and how five closely related individuals made the decision to adopt the iPhone before it was available through traditional supply chains. Focusing on the role played by social networks, we analyze how adoption threshold, opinion leaders, social contagion, and social learning shaped adoption behaviors and outcomes. The analyses confirm that network structures impacted the early decision to accept the iPhone; they show that when facing uncertainty, adoption decisions emerged as a combined result of individual adoption reflections and major influences from the social network as well as behaviors observed within the network; and, they reveal interesting behaviors that differed from expectations. In conclusion, we discuss implications for both theory and practice.

The social network learning cloud: architectural education for the 21 century

In less than a decade, architectural education has, in some ways, significantly evolved. The advent of computation has not so much triggered the change, but Social Networks (SN) have ignited a novel way of learning, interaction and knowledge construction. SN enable learners to engage with friends, tutors, professionals and peers, form the base for learning resources, allow students to make their voices heard, to listen to other views and much more. They offer a more authentic, inter-professional and integrated problem based, Just-in-Time (JIT), Just-in-Place (JIP) learning. Online SN work in close association with offline SN to form a blended social learning realm-the Social Network Learning Cloud (SNLC)-that greatly enables and enhances students' learning in a far more influential way than any other learning means, resources or methods do. This paper presents a SNLC for architectural education that provides opportunities for linking the academic Learning Management Systems (LMS) with private or professional SN such that it enhances the learning experience and deepens the knowledge of the students. The paper proposes ways of utilising SNLC in other learning and teaching areas of the curriculum and concludes with directions of how SNLC then may be employed in professional settings.

Propagation model of smartphone worms based on semi-Markov process and social relationship graph

Smartphone applications are getting more and more popular and pervasive in our daily life, and are also attractive to malware writers due to their limited computing source and vulnerabilities. At the same time, we possess limited understanding of our opponents in cyberspace. In this paper, we investigate the propagation model of SMS/MMS-based worms through integrating semi-Markov process and social relationship graph. In our modeling, we use semi-Markov process to characterize state transition among mobile nodes, and hire social network theory, a missing element in many previous works, to enhance the proposed mobile malware propagation model. In order to evaluate the proposed models, we have developed a specific software, and collected a large scale real-world data for this purpose. The extensive experiments indicate that the proposed models and algorithms are effective and practical. © 2014 Elsevier Ltd. All rights reserved.

Are the popular users always important for information dissemination in online social networks?

This article verifies the importance of popular users in OSNs. The results are counter-intuitive. First, for dissemination speed, a large amount of users can swiftly distribute information to the masses, but they are not highly-connected users. Second, for dissemination scale, many powerful forwarders in OSNs cannot be identified by the degree measure. Furthermore, to control dissemination, popular users cannot capture most bridges of social communities.

A community based systems diagram of obesity causes

INTRODUCTION: Application of system thinking to the development, implementation and evaluation of childhood obesity prevention efforts represents the cutting edge of community-based prevention. We report on an approach to developing a system oriented community perspective on the causes of obesity. METHODS: Group model building sessions were conducted in a rural Australian community to address increasing childhood obesity. Stakeholders (n = 12) built a community model that progressed from connection circles to causal loop diagrams using scripts from the system dynamics literature. Participants began this work in identifying change over time in causes and effects of childhood obesity within their community. The initial causal loop diagram was then reviewed and elaborated by 50 community leaders over a full day session. RESULTS: The process created a causal loop diagram representing community perceptions of determinants and causes of obesity. The causal loop diagram can be broken down into four separate domains; social influences; fast food and junk food; participation in sport; and general physical activity. DISCUSSION: This causal loop diagram can provide the basis for community led planning of a prevention response that engages with multiple levels of existing settings and systems.