Local Conventions in Game Play in an Evolving Dual Social Network Framework

People usually perform economic interactions within the social setting of a small group, while they obtain relevant information from a broader source. We capture this feature with a dynamic interaction model based on two separate social networks. Individuals play a coordination game in an interaction network, while updating their strategies using information from a separate influence network through which information is disseminated. In each time period, the interaction and influence networks co-evolve, and the individuals’ strategies are updated through a modified naive learning process. We show that both network structures and players’ strategies always reach a steady state, in which players form fully connected groups and converge to local conventions. We also analyze the influence exerted by a minority group of strongly opinionated players on these outcomes.

Towards automated Android app collusion detection

Android OS supports multiple communication methods between apps. This opens the possibility to carry out threats in a collaborative fashion, c.f. the Soundcomber example from 2011. In this paper we provide a concise definition of collusion and report on a number of automated detection approaches, developed in co-operation with Intel Security.

Trusting Groups in Coalition Formation using Social Distance

In environments where distributed team formation is key, and defections are possible, the use of trust as social capital allows social norms to be defied and compared. An agent can use this information, when invited to join a group or collation, to decide whether or not its utility will be increased by joining. In this work a social network approach is used to define and reason about the relationships contained in the agent community. Previous baseline work is extended with two decision making mechanisms. These are compared by simulating an abstract grid-like environment, and preliminary results are reported.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

CSFinder: A Cold-Start Friend Finder in Large-Scale Social Networks

Recommending users for a new social network user to follow is a topic of interest at present. The existing approaches rely on using various types of information about the new user to determine recommended users who have similar interests to the new user. However, this presents a problem when a new user joins a social network, who is yet to have any interaction on the social network. In this paper we present a particular type of conversational recommendation approach, critiquing-based recommendation, to solve the cold start problem. We present a critiquing-based recommendation system, called CSFinder, to recommend users for a new user to follow. A traditional critiquing-based recommendation system allows a user to critique a feature of a recommended item at a time and gradually leads the user to the target recommendation. However this may require a lengthy recommendation session. CSFinder aims to reduce the session length by taking a case-based reasoning approach. It selects relevant recommendation sessions of past users that match the recommendation session of the current user to shortcut the current recommendation session. It selects relevant recommendation sessions from a case base that contains the successful recommendation sessions of past users. A past recommendation session can be selected if it contains recommended items and critiques that sufficiently overlap with the ones in the current session. Our experimental results show that CSFinder has significantly shorter sessions than the ones of an Incremental Critiquing system, which is a baseline critiquing-based recommendation system.

Recent advances in the analysis of behavioural organization and interpretation as indicators of animal welfare

While the incorporation of mathematical and engineering methods has greatly advanced in other areas of the life sciences, they have been under-utilized in the field of animal welfare. Exceptions are beginning to emerge and share a common motivation to quantify 'hidden' aspects in the structure of the behaviour of an individual, or group of animals. Such analyses have the potential to quantify behavioural markers of pain and stress and quantify abnormal behaviour objectively. This review seeks to explore the scope of such analytical methods as behavioural indicators of welfare. We outline four classes of analyses that can be used to quantify aspects of behavioural organization. The underlying principles, possible applications and limitations are described for: fractal analysis, temporal methods, social network analysis, and agent-based modelling and simulation. We hope to encourage further application of analyses of behavioural organization by highlighting potential applications in the assessment of animal welfare, and increasing awareness of the scope for the development of new mathematical methods in this area.

Mass media and polarisation processes in the bounded confidence model of opinion dynamics

This paper presents a social simulation in which we add an additional layer of mass media communication to the social network 'bounded confidence' model of Deffuant et al (2000). A population of agents on a lattice with continuous opinions and bounded confidence adjust their opinions on the basis of binary social network interactions between neighbours or communication with a fixed opinion. There are two mechanisms for interaction. 'Social interaction' occurs between neighbours on a lattice and 'mass communication' adjusts opinions based on an agent interacting with a fixed opinion. Two new variables are added, polarisation: the degree to which two mass media opinions differ, and broadcast ratio: the number of social interactions for each mass media communication. Four dynamical regimes are observed, fragmented, double extreme convergence, a state of persistent opinion exchange leading to single extreme convergence and a disordered state. Double extreme convergence is found where agents are less willing to change opinion and mass media communications are common or where there is moderate willingness to change opinion and a high frequency of mass media communications. Single extreme convergence is found where there is moderate willingness to change opinion and a lower frequency of mass media communication. A period of persistent opinion exchange precedes single extreme convergence, it is characterized by the formation of two opposing groups of opinion separated by a gradient of opinion exchange. With even very low frequencies of mass media communications this results in a move to central opinions followed by a global drift to one extreme as one of the opposing groups of opinion dominates. A similar pattern of findings is observed for Neumann and Moore neighbourhoods.

Impulsivity as a predictor of newly diagnosed depression

Depression is a severe mental disorder, whose onset has been related to environmental, genetic and personality factors. This study examined the association between impulsivity and incidence of depression. Logistic regression models were related to prospective data from two surveys (2-year time lag) conducted in a large cohort of hospital employees (N = 4,505). Only respondents with no history of depression at baseline were included. Impulsivity was predictive of the onset of depression (OR = 1.95, CI 95% = 1.28-2.97) after adjustment for age, sex and education. This association remained statistically significant after additional adjustment for a variety of baseline characteristics, such as smoking, alcohol consumption, and size of social network. A measure of mental distress (GHQ-12 responses), which may serve as a proxy measure for undiagnosed depression, was also associated with impulsivity. Impulsivity appears to be a distinct personality factor that may contribute to the onset of depressive illness in adults.

The level playing field: the impact of assessment practice on professional development

Context: The effects of assessment practice on students’ learning are unclear, particularly regarding professional development. Corralling in objective structured clinical examinations (OSCEs) is designed to reduce illicit passing of examination information. Candidates completing an examination are kept secluded until the next cohort of examinees has begun. We used the introduction of corralling as a context in which to explore social influences on examination misconduct, with the aims of improving understanding of the hidden effects of assessment, and evaluating the acceptability of corralling from the student perspective.

Methods: A questionnaire was administered to students corralled post-OSCE for the first time. Eleven semi-structured interviews were subsequently conducted. Questionnaire data were analysed for descriptive statistics and thematic analysis of interview transcripts was carried out.

Results: The questionnaire response rate was 95.4% (251/263). Before corralling, 80.9% (203/251) of students were aware of the sharing of information among peers and 78.5% (197/251) agreed that such misconduct was unprofessional. The majority were in favour of corralling (90.8%, 228/251). Four themes emerged from the semi-structured interviews: the student network versus the individual; assessment-driven culture; the deferring of professionalism, and the ‘level playing field’. Students saw interaction within the student network, on a background of assessment-driven culture, as the key driver in examination misconduct. Conforming to the rules of the social network was prioritised over individual agency, although the mismatch between the rules of the network and the dominant professional discourse caused some conflict for individuals. Deferred professionalism (described as the practice of taking on the norms of professional behaviour only when qualified) was a rationalisation used to minimise this conflict. Corralling provided a ‘level playing field’ in which the influences of the network were minimised.

Conclusions: Examination misconduct is thus a complex social construction with implications for individual learners in terms of professional development. Corralling is one mechanism for addressing misconduct that is acceptable to students, but assessment processes have important hidden effects which educators should acknowledge.