Reliable online social network data collection

Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.Postprin

Social Alignment Contagion in Online Social Networks

Researchers have already observed social contagion effects in both in-person and online interactions. However, such studies have primarily focused on users’ beliefs, mental states, and interests. In this article, we expand the state of the art by exploring the impact of social contagion on social alignment, i.e., whether the decision to socially align oneself with the general opinion of the users on the social network is contagious to one’s connections on the network or not. The novelty of our work in this article includes: 1) unlike earlier work, this article is among the first to explore the contagiousness of the concept of social alignment on social networks; 2) our work adopts an instrumental variable approach to determine reliable causal relations between observed social contagion effects on the social network; and 3) our work expands beyond the mere presence of contagion in social alignment and also explores the role of population heterogeneity on social alignment contagion. Based on the systematic collection and analysis of data from two large social network platforms, namely, Twitter and Foursquare, we find that a user’s decision to socially align or distance from social topics and sentiments influences the social alignment decisions of their connections on the social network. We further find that such social alignment decisions are significantly impacted by population heterogeneity

Event Estimation Accuracy of Social Sensing with Facebook for Social Internet of Vehicles

© 2014 IEEE. Social Internet of Vehicles (SIoV) is a new paradigm that enables social relationships among vehicles via the Internet. People in the vehicles using online social networks (OSNs) can be an integral part of SIoV that enables the collection of data for sensing a physical phenomenon, i.e., social sensing. In this paper, we study the main social sensing mechanism in Facebook, comment thread network (CTN), which is based on the interactions of users through user walls in Facebook for SIoV. After seeing their commuters' contents about an event, users either add comments or like these posts, and Facebook CTN emerges as a social sensing medium in estimation of an event through social consensus. For the first time, this paper investigates the social sensing capability of Facebook CTN, i.e., the accuracy of collective observations for SIoV. The accuracy depends on the user characteristics and the features of the OSN, since perceptions of the users and how they use Facebook may manipulate their observation signals. We analyze the reliability of Facebook CTN for varying user behaviors, user relationships, Facebook features, and network size. The results indicate that the polarized weighting of the observations and the use of less reliable post types in CTN deteriorate the accuracy of the estimate signal, i.e., social consensus. Furthermore, the selection of users is likely to be an important factor in social sensing

Digital Forensic Automation Model For Online Social Networks

Presently, law enforcement agencies and legal practitioners frequently utilize social networks to quickly access the information related to the participants of any illicit incident. However, the forensic process is technically intricate due to heterogeneous and unstructured online social networks and legally challenging. Hence, creating intellectual challenges and enormous workloads for the investigators. Therefore, it is critical to developing automated and reliable solutions to assist investigators. Though automation is not an entirely technical issue in digital forensics. Legal requirements always demand an explainable theory for the conclusions generated by automated methods. This work introduces an automation model; that addresses the automation issues from collection to evidence analysis in online social network forensics. This study first describes a formal knowledge model to explain the forensic process for the social network. This knowledge model is formulated to explain the results obtained by an automated analysis. Second, it explained a forensic investigation model that specifically addresses the issue of automated investigations on online social networks. This model suggested an investigation process to carry out a semi-automated forensic investigation on online social networks. The third component of this approach is a hybrid ontology model that involves multiple ontologies to manage the unstructured data into an organized collection. Finally, this work proposed a set of analysis operators that are on domain correlations. These operators can be embedded in software tools

Locational wireless and social media-based surveillance

The number of smartphones and tablets as well as the volume of traffic generated by these devices has been growing constantly over the past decade and this growth is predicted to continue at an increasing rate over the next five years. Numerous native features built into contemporary smart devices enable highly accurate digital fingerprinting techniques. Furthermore, software developers have been taking advantage of locational capabilities of these devices by building applications and social media services that enable convenient sharing of information tied to geographical locations. Mass online sharing resulted in a large volume of locational and personal data being publicly available for extraction. A number of researchers have used this opportunity to design and build tools for a variety of uses – both respectable and nefarious. Furthermore, due to the peculiarities of the IEEE 802.11 specification, wireless-enabled smart devices disclose a number of attributes, which can be observed via passive monitoring. These attributes coupled with the information that can be extracted using social media APIs present an opportunity for research into locational surveillance, device fingerprinting and device user identification techniques. This paper presents an in-progress research study and details the findings to date

Topology comparison of Twitter diffusion networks effectively reveals misleading information

In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analyses, have produced deeper knowledge about this phenomenon, showing that misleading information spreads faster, deeper and more broadly than factual information on social media, where echo chambers, algorithmic and human biases play an important role in diffusion networks. Following these directions, we explore the possibility of classifying news articles circulating on social media based exclusively on a topological analysis of their diffusion networks. To this aim we collected a large dataset of diffusion networks on Twitter pertaining to news articles published on two distinct classes of sources, namely outlets that convey mainstream, reliable and objective information and those that fabricate and disseminate various kinds of misleading articles, including false news intended to harm, satire intended to make people laugh, click-bait news that may be entirely factual or rumors that are unproven. We carried out an extensive comparison of these networks using several alignment-free approaches including basic network properties, centrality measures distributions, and network distances. We accordingly evaluated to what extent these techniques allow to discriminate between the networks associated to the aforementioned news domains. Our results highlight that the communities of users spreading mainstream news, compared to those sharing misleading news, tend to shape diffusion networks with subtle yet systematic differences which might be effectively employed to identify misleading and harmful information.Comment: A revised new version is available on Scientific Report

Measuring internet activity: a (selective) review of methods and metrics

Two Decades after the birth of the World Wide Web, more than two billion people around the world are Internet users. The digital landscape is littered with hints that the affordances of digital communications are being leveraged to transform life in profound and important ways. The reach and influence of digitally mediated activity grow by the day and touch upon all aspects of life, from health, education, and commerce to religion and governance. This trend demands that we seek answers to the biggest questions about how digitally mediated communication changes society and the role of different policies in helping or hindering the beneficial aspects of these changes. Yet despite the profusion of data the digital age has brought upon us—we now have access to a flood of information about the movements, relationships, purchasing decisions, interests, and intimate thoughts of people around the world—the distance between the great questions of the digital age and our understanding of the impact of digital communications on society remains large. A number of ongoing policy questions have emerged that beg for better empirical data and analyses upon which to base wider and more insightful perspectives on the mechanics of social, economic, and political life online. This paper seeks to describe the conceptual and practical impediments to measuring and understanding digital activity and highlights a sample of the many efforts to fill the gap between our incomplete understanding of digital life and the formidable policy questions related to developing a vibrant and healthy Internet that serves the public interest and contributes to human wellbeing. Our primary focus is on efforts to measure Internet activity, as we believe obtaining robust, accurate data is a necessary and valuable first step that will lead us closer to answering the vitally important questions of the digital realm. Even this step is challenging: the Internet is difficult to measure and monitor, and there is no simple aggregate measure of Internet activity—no GDP, no HDI. In the following section we present a framework for assessing efforts to document digital activity. The next three sections offer a summary and description of many of the ongoing projects that document digital activity, with two final sections devoted to discussion and conclusions

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Searching for superspreaders of information in real-world social media

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure