Homogeneity in Social Groups of Iraq

Homogeneity in Social Groups of Iraqis Jon Gresham, Farouk Saleh, Shara Majid June 2006 With appreciation to the Royal Institute for Inter-Faith Studies for initiating the Second World Congress for Middle Eastern Studies, this paper summarizes findings on homogeneity in community-level social groups derived from inter-ethnic research conducted during 2005 among Iraqi Arabs and Kurds living in the city of Basra, Iraq, and in the Netherlands. We found that perceptions towards out-groups were not based on religion, ethnicity, class, or location as in traditional individual-focused social networks. Patterns of perception towards out-groups seemed to be rooted in homogeneous social sub-groups with combinations of these factors. This research project used a 192-item survey of two hundred Iraqi business owners and managers in Iraq and in the Netherlands. It measured homogeneity of social group memberships. Survey elements included items drawn from the World Values Surveys (Inglehart), the Social Capital Community Benchmark Survey (Roper Center), and the Social Capital Inventory (Narayan and Cassidy). Homogeneity, relationship segregation, social trust, and community influence in social networks were estimated through indices reflecting components of social relationships in priority in-groups emerging from factor analysis of survey responses. Other indices included civic participation (socialization), perceptions of threat from out-groups, ethnic and religious identity, social trust, personal security, and contribution to community-based resources. Uniformity of responses to certain items about out-groups corresponded to findings by other authors on segregation and membership in social networks (Burt 1997, Buskins 2005, Inglehart 2004, Narayan and Cassidy 2001, Putnam 1995). This work was an expansion on a study on perceptions of threat from out-groups among Iraqis in five locations conducted in 2003 (Gresham 2004). This paper presents the following major sections: I. Introduction II. Purpose III. Background IV. Methodology V. Results VI. Reporting Process VII. Conclusions VIII. Further Work IX. Appendix X. End Notes *Jon Gresham, European Research Centre On Migration & Ethnic Relations, University of Utrecht, Netherlands Farouk Saleh, University of Tilburg, Netherlands Shara Majid, Erasmus University, Netherlands See other reports at: http://www.CivilSocietyIraq.seedwiki.co

POISED: Spotting Twitter Spam Off the Beaten Paths

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses. Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns. In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks

Human resource allocation to multiple projects based on members’ expertise, group heterogeneity and social cohesion

Project managers regularly allocate human resources to construction projects. This critical task is usually executed by fulfilling the minimum project staffing requirements normally based around the quantity and competence of project members. However, research has shown that team performance can increase by up to 10% and 18%, respectively, as a consequence of the group members’ heterogeneity and social cohesion. Also, there is currently no practical quantitative tool which incorporates these aspects to allow project managers to achieve this task efficiently and objectively. A new quantitative model for the effective allocation of human resources to multiple projects, which takes into account group heterogeneity and social cohesion is proposed. This model is easy to build, update and use in real project environments with the use of a spreadsheet and a basic optimization engine (e.g. Excel Solver). A case study is proposed and solved with a Genetic Algorithm to illustrate the model implementation. Finally, a validation example is provided to exemplify how group heterogeneity and social cohesion condition academic achievement in an academic setting

An Applied Study on Educational Use of Facebook as a Web 2.0 Tool: The Sample Lesson of Computer Networks and Communication

The main aim of the research was to examine educational use of Facebook. The Computer Networks and Communication lesson was taken as the sample and the attitudes of the students included in the study group towards Facebook were measured in a semi-experimental setup. The students on Facebook platform were examined for about three months and they continued their education interactively in that virtual environment. After the-three-month-education period, observations for the students were reported and the attitudes of the students towards Facebook were measured by three different measurement tools. As a result, the attitudes of the students towards educational use of Facebook and their views were heterogeneous. When the average values of the group were examined, it was reported that the attitudes towards educational use of Facebook was above a moderate level. Therefore, it might be suggested that social networks in virtual environments provide continuity in life long learning.Comment: 11 page

How the health-seeking behaviour of pregnant women affects neonatal outcomes: findings of system dynamics modelling in Pakistan

Background: Limited studies have explored how health-seeking behaviour during pregnancy through to delivery affect neonatal outcomes. We modelled health-seeking behaviour across urban and rural settings in Pakistan, where poor neonatal outcomes persist with wide disparities. Methods and findings: A system dynamics model was developed and parameterised. Following validation tests, the model was used to determine neonatal mortality for pregnant women considering their decisions to access, refuse and switch antenatal care services in four provider sectors: public, private, traditional and charitable. Four health-seeking scenarios were tested across different pregnancy trimesters. Health-seeking behaviour in different subgroups by geographical locations and social network effect was modelled. The largest reduction in neonatal mortality was achieved with antenatal care provided by skilled providers in public, private or charitable sectors, combined with the use of institutional delivery. Women’s social networks had strong influences on if, when and where to seek care. Interventions by Lady Health Workers had a minimal impact on health-seeking behaviour and neonatal outcomes after trimester 1. Optimal benefits were achieved for urban women when antenatal care was accessed within trimester 2, but for rural women within trimester 1. Antenatal care access delayed to trimester 3 had no protective impact on neonatal mortality. Conclusions: System dynamics modelling enables capturing the complexity of health-seeking behaviours and impact on outcomes, informing intervention design, implementation of targeted policies and uptake of services specific to urban/rural settings considering structural enablers/barriers to access, cultural contexts and strong social network influences

Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks

Information garnered from activity on location-based social networks can be harnessed to characterize urban spaces and organize them into neighborhoods. In this work, we adopt a data-driven approach to the identification and modeling of urban neighborhoods using location-based social networks. We represent geographic points in the city using spatio-temporal information about Foursquare user check-ins and semantic information about places, with the goal of developing features to input into a novel neighborhood detection algorithm. The algorithm first employs a similarity metric that assesses the homogeneity of a geographic area, and then with a simple mechanism of geographic navigation, it detects the boundaries of a city's neighborhoods. The models and algorithms devised are subsequently integrated into a publicly available, map-based tool named Hoodsquare that allows users to explore activities and neighborhoods in cities around the world. Finally, we evaluate Hoodsquare in the context of a recommendation application where user profiles are matched to urban neighborhoods. By comparing with a number of baselines, we demonstrate how Hoodsquare can be used to accurately predict the home neighborhood of Twitter users. We also show that we are able to suggest neighborhoods geographically constrained in size, a desirable property in mobile recommendation scenarios for which geographical precision is key.Comment: ASE/IEEE SocialCom 201

A Computational Model and Convergence Theorem for Rumor Dissemination in Social Networks

The spread of rumors, which are known as unverified statements of uncertain origin, may cause tremendous number of social problems. If it would be possible to identify factors affecting spreading a rumor (such as agents' desires, trust network, etc.), then this could be used to slowdown or stop its spreading. A computational model that includes rumor features and the way a rumor is spread among society's members, based on their desires, is therefore needed. Our research is centering on the relation between the homogeneity of the society and rumor convergence in it and result shows that the homogeneity of the society is a necessary condition for convergence of the spreading rumor.Comment: 29 pages, 7 figure