Accelerating Information Diffusion in Social Networks Under the Susceptible-Infected-Susceptible Epidemic Model

Standard Susceptible-Infected-Susceptible (SIS) epidemic models assume that a message spreads from the infected to the susceptible nodes due to only susceptible-infected epidemic contact. We modify the standard SIS epidemic model to include direct recruitment of susceptible individuals to the infected class at a constant rate (independent of epidemic contacts), to accelerate information spreading in a social network. Such recruitment can be carried out by placing advertisements in the media. We provide a closed form analytical solution for system evolution in the proposed model and use it to study campaigning in two different scenarios. In the first, the net cost function is a linear combination of the reward due to extent of information diffusion and the cost due to application of control. In the second, the campaign budget is fixed. Results reveal the effectiveness of the proposed system in accelerating and improving the extent of information diffusion. Our work is useful for devising effective strategies for product marketing and political/social-awareness/crowd-funding campaigns that target individuals in a social network.

Cost effective campaigning in social networks

Campaigners are increasingly using online social networking platforms for promoting products, ideas and information. A popular method of promoting a product or even an idea is incentivizing individuals to evangelize the idea vigorously by providing them with referral rewards in the form of discounts, cash backs, or social recognition. Due to budget constraints on scarce resources such as money and manpower, it may not be possible to provide incentives for the entire population, and hence incentives need to be allocated judiciously to appropriate individuals for ensuring the highest possible outreach size. We aim to do the same by formulating and solving an optimization problem using percolation theory. In particular, we compute the set of individuals that are provided incentives for minimizing the expected cost while ensuring a given outreach size. We also solve the problem of computing the set of individuals to be incentivized for maximizing the outreach size for given cost budget. The optimization problem turns out to be non trivial; it involves quantities that need to be computed by numerically solving a fixed point equation. Our primary contribution is, that for a fairly general cost structure, we show that the optimization problems can be solved by solving a simple linear program. We believe that our approach of using percolation theory to formulate an optimization problem is the first of its kind. (C) 2016 Elsevier B.V. All rights reserved.

Developing an effective electronic social care record for care in the home

Cooper, J. & Urquhart, C. (2005). Developing an effective electronic social care record for care in the home. In J. Bryant (Ed.), Current Perspectives in Healthcare Computing conference, Harrogate 21-23 March 2005 (CD-ROM). Swindon: BCS HIC Sponsorship: AHRC

Computing regular equivalence: practical and theoretical issues

Social network analysts have tried to capture the idea of social role explicitly by proposing a framework that precisely gives conditions under which group actors are playing equivalent roles. They term these methods positional analysis techniques. The most general definition is regular equivalence which captures the idea that equivalent actors are related in a similar way to equivalent alters. Regular equivalence gives rise to a whole class of partitions on a network. Given a network we have two different computational problems. The first is how to find a particular regular equivalence. An algorithm exists to find the largest regular partition but there are not efficient algorithms to test whether there is a regular k-partition. That is a partition in k groups that is regular. In addition, when dealing with real data, it is unlikely that any regular partitions exist. To overcome this problem relaxations of regular equivalence have been proposed along with optimisation techniques to find nearly regular partitions. In this paper we review the algorithms that have developed to find particular regular equivalences and look at some of the recent theoretical results which give an insight into the complexity of finding regular partitions.

An investigation of assessment policy and practices in terms of broad social and educational purposes

Based upon relevant literature, this study investigated the assessment policy and practices for the BSc (Hons) Computing Science programme at the University of Greenwich (UOG), contextualising these in terms of broad social and educational purposes. It discusses Assessment, and then proceeds to give a critical evaluation of the assessment policy and practices at the UOG. Although this is one case study, because any of the features of the programme are generic to other programmes and institutions, it is of wider value and has further implications. The study was concluded in the summer of 2002. It concludes that overall, the programme's assessment policy and practices are well considered in terms of broad social and educational purposes, although it identifies and outlines several possible improvements, as well as raising some major issues still to be addressed which go beyond assessment practices.

An analysis of masculine and feminine writings in postings within a series of health and social care online undergraduate courses

This study aims to extend understanding of pedagogic dialogue by analysing the effect that the gender of the student has on interaction within a selection of ordinary health and social care on line courses.

Social networks and cooperative learning in a MOOC

MOOCs are changing the educational landscape and gaining a lot of attention in scientific literature. However, the pedagogical design of these proposals has been called into question. It is precisely MOOCs’ social aspect, i.e. the interaction between course participants and the support for learning processes that has become one of the main topics of interest. This article presents the results of a research project carried out at the University of the Basque Country, which focused in cooperative learning and the intensive use of social networks in a MOOC. Significant data was compiled through Likert-type surveys, revealing that the use of both external and internal social networks in a massive open online course is a factor that is evaluated positively by students. We argue that the use of social networks as a learning strategy in a MOOC has an influence on academic performance and on the students' success rate. Furthermore, the participants’ age also has a bearing on the social networks they use, and we have found that the younger members tend to work with external networks such as Twitter or personal blogs, whereas the older students are more inclined to use forums from the Chamilo or Ning platforms.

Does the Child Health Computing System adequately identify children with cerebral palsy?

Background This paper assesses the usefulness of the Child Health Computing System as a source of information about children with cerebral palsy.

Methods A comparative survey of information held on the Child Health Computing System (CHCS) and the Northern Ireland Cerebral Palsy Register (NICPR) in one Health and Social Services Board in Northern Ireland was carried out. The sample comprised children with cerebral palsy aged 5–9 years.

Results Of the 135 cases recorded on the NICPR, 47 per cent were not found on the CHCS; the majority of these children had no computer record of any medical diagnosis. Of the 82 cases recorded on the CHCS, 10(12 per cent) were not found on the NICPR; five of these cases (6 per cent) were found on follow–up not to have CP.

Conclusions Unless improvements are made in case ascertainment, case validation and recording activities, the evidence suggests that the CHCS will not be able to provide the same quality of information for needs assessment and surveillance of very low birthweight infants in relation to cerebral palsy as is provided by a specialist case register.

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.

The Belfast Storytelling Database: A spontaneous social interaction database with laughter focused annotation

To support the endeavor of creating intelligent interfaces between computers and humans the use of training materials based on realistic human-human interactions has been recognized as a crucial task. One of the effects of the creation of these databases is an increased realization of the importance of often overlooked social signals and behaviours in organizing and orchestrating our interactions. Laughter is one of these key social signals; its importance in maintaining the smooth flow of human interaction has only recently become apparent in the embodied conversational agent domain. In turn, these realizations require training data that focus on these key social signals. This paper presents a database that is well annotated and theoretically constructed with respect to understanding laughter as it is used within human social interaction. Its construction, motivation, annotation and availability are presented in detail in this paper.

SocialLDA:Scalable Topic Modeling in Social Networks

Thesis (Master's)--University of Washington, 2012

Adaptive Probabilistic Topic Models for Social Networks

Thesis (Master's)--University of Washington, 2012