Risk management networks of ethnic minorities in Viet Nam

The utilization of informal social networks is an important risk management strategy of vulnerable households in South-East Asia. To gain insight on this issue, a social network analysis (SNA) was implemented to assess risk management networks of ethnic minority farm households in the northern uplands of Viet Nam. The results from the analysis suggest that kinship relations and the level of wealth play an essential role in enabling basic network services to function. This paper also points out that effective networks require investments to fulfil the requested mutual obligations and that subsequently, social networks among poor farmers are relatively limited. The findings of the analysis show, not surprisingly, that networks cannot completely buffer severe shocks. Consequently, policy measures to reduce the costs of investing in social capital of poor farmers as well as improved access to appropriate social security systems are essential. These findings are applicable to other upland areas of South-East Asia.-

Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Public faces? A critical exploration of the diffusion of face recognition technologies in online social networks

In recent years, we have witnessed a rapid spread of biometric technologies from the security domain to commercial and social media applications. In this article, we critically explore the repercussions of this diffusion of face recognition to everyday contexts with an in-depth analysis of Facebook’s “tag suggestions” tool which first introduced the technology to on-line social networks. We use Nissenbaum’s framework of contextual integrity to show how the informational norms associated with biometrics in security and policing - their contexts of emergence - are grafted on-line social networks onto their context of iteration. Our analysis reveals a process that has inadvertently influenced the way users understand face recognition, precluding critical questioning of its wider use. It provides an important deepening of contextually-driven approaches to privacy by showing the process through which contexts are co-constitutive of informational norms. Citizens are also offered a critical tool for understanding the trajectory of biometrics and reflect on the data practices associated with the use of face recognition in social media and society at large

A heuristic evaluation of the Facebook's advertising tool beacon

Interface usability is critical to the successful adoption of information systems. The aim of this study is to evaluate interface of Facebook's advertising tool Beacon by using privacy heuristics [4]. Beacon represents an interesting case study because of the negative media and user backlash it received. The findings of heuristic evaluation suggest violation of privacy heuristics [4]. Here, analysis identified concerns about user choice and consent, integrity and security of data, and awareness and notice. Beacon was an innovative tool, therefore, its systematic evaluation was needed in order to identify privacy problems, their causes and subsequent consequences. The study provides useful insights to human computer interaction (HCI) designers of online social networks

An analysis on the relation between users' online social networks addiction and users security concerns

Use of online social network platforms has increased over last decades. There are various activities that users can do on those platforms such as, making friends, enjoying time, making business, and education. Given activities make online social network platforms more attractive and users want to spend more time on those platforms. Although there is a massive increment in their use, they are not secure enough to fully protect their users' data and privacy. Some users are not aware of the security settings (i.e. privacy settings) since most users focus on spending time on those platforms which brings online social networks addiction into the consideration. Addiction is defined with time dependency in most of the literature works, however, calling a person as an addicted person depends on various factors. This work provides three main contributions; 1-) It clarifies the definition of addiction with a quantitative model. 2-) It provides an analysis on online social networks addiction; answers the question "whom could be called as an addicted user to those platforms" 3-)It provides an analysis on users' trusts to online social networks platforms