An Analysis of Social Networking Sites: Privacy Policy and Features

Social Networking Sites (SNSs) are at the heart of many people lives, and the majority of both students and adults who use them to share information, keeping contact with old friends and meeting new acquaintances. However, the increasing number of action on online services also gives a raised to privacy concerns and issues. Therefore, the main purpose of this study is investigate the two SNSs i.e. Facebook and Friendster in terms of privacy policy and features, users‟ preferences and needs as well as producing a guideline for good SNSs from users design perspective. In an attempt to achieve the objectives of this study, however, two different approaches were employed; first literature has reviewed for two SNSs for the comparative analysis, and secondly quantitative approach technique was used. Online questionnaire was designed and published on the web and the respondents were able to access and sent back respectively. The survey was limited only to one hundred respondents within the Universiti Utara Malaysia. Findings from this study reveal that there are significant differences and similarities between Facebook and Friendster privacy policy and features. However, Friendster has hidden users‟ identity information by default to only friends, while Facebook has made it public to everyone. Results from survey in this study indicate that most of the respondents disclose information including personal and private information with public and friends, nevertheless, many respondents prefer to share their personal and private information with friends. Although, majority of respondents are aware of privacy setting changes, while they have notable attitude toward privacy protection as well as trust. This study usher a new era towards knowledge of social networking sites and the result can be use to the body of literature on information system with emphasis on privacy policy setting and features

Predicting Community Evolution in Social Networks

Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page

Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction

Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive sub-websites from the StackExchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive sub-websites. These patterns are mainly: cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactory prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are: (1) the decay process is not governed by only one network measure; it is better described using multiple measures; (2) the expert members of the StackExchange sub-websites were mainly responsible for the activity or inactivity of the StackExchange sub-websites; (3) the Statistics sub-website is going through decay dynamics that may lead to it becoming fully-decayed; and (4) decayed sub-websites were originally less resilient to inactivity decay, unlike the alive sub-websites

An Algorithm for Critical Nodes Problem in Social Networks Based on Owen Value

Discovering critical nodes in social networks has many important applications. For finding out the critical nodes and considering the widespread community structure in social networks, we obtain each node’s marginal contribution by Owen value. And then we can give a method for the solution of the critical node problem. We validate the feasibility and effectiveness of our method on two synthetic datasets and six real datasets. At the same time, the result obtained by using our method to analyze the terrorist network is in line with the actual situation

Studying web 2.0 interactivity: a research framework and two case studies

With more than one third of the world’s population being online, the Internet has increasingly become part of modern living, giving rise to popular literature that often takes a teleological and celebratory perspective, heralding the Internet and Web 2.0 specifically, as an enabler of participation, democracy, and interactivity. However, one should not take these technological affordances of Web 2.0 for granted. This article applies an interaction framework to the analysis of two Web 2.0 websites viewed as spaces where interaction goes beyond the mere consultation and selection of content, i.e., as spaces supporting the (co)creation of content and value. The authors’ approach to interactivity seeks to describe websites in objective, structural terms as spaces of user, document, and website affordances. The framework also makes it possible to talk about the websites in subjective, functional terms, considering them as spaces of perceived inter-action, intra-action and outer-action affordances. Analysis finds that both websites provide numerous user, document, and website affordances that can serve as inter-action or social affordances