Experimenting with online governance

To solve the problems they face, online communities adopt comprehensive governance methods including committees, boards, juries, and even more complex institutional logics. Helping these kinds of communities succeed will require categorizing best practices and creating toolboxes that fit the needs of specific communities. Beyond such applied uses, there is also a potential for an institutional logic itself to evolve, taking advantage of feedback provided by the fast pace and large ecosystem of online communication. Here, we outline an experimental strategy aiming at guiding and facilitating such an evolution. We first review the advantages of studying collective action using recent technologies for efficiently orchestrating massive online experiments. Research in this vein includes attempts to understand how behavior spreads, how cooperation evolves, and how the wisdom of the crowd can be improved. We then present the potential usefulness of developing virtual-world experiments with governance for improving the utility of social feedback. Such experiments can be used for improving community rating systems and monitoring (dashboard) systems. Finally, we present a framework for constructing large-scale experiments entirely in virtual worlds, aimed at capturing the complexity of governance dynamics, to empirically test outcomes of manipulating institutional logic.Received: 14 November 2020; Accepted: 23 March 2021; Published: 26 April 2021

Measuring Large-Scale Social Networks with High Resolution

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection

Multidimensional human dynamics in mobile phone communications

In today's technology-assisted society, social interactions may be expressed through a variety of techno-communication channels, including online social networks, email and mobile phones (calls, text messages). Consequently, a clear grasp of human behavior through the diverse communication media is considered a key factor in understanding the formation of the today's information society. So far, all previous research on user communication behavior has focused on a sole communication activity. In this paper we move forward another step on this research path by performing a multidimensional study of human sociality as an expression of the use of mobile phones. The paper focuses on user temporal communication behavior in the interplay between the two complementary communication media, text messages and phone calls, that represent the bi-dimensional scenario of analysis. Our study provides a theoretical framework for analyzing multidimensional bursts as the most general burst category, that includes one-dimensional bursts as the simplest case, and offers empirical evidence of their nature by following the combined phone call/text message communication patterns of approximately one million people over three-month period. This quantitative approach enables the design of a generative model rooted in the three most significant features of the multidimensional burst - the number of dimensions, prevalence and interleaving degree - able to reproduce the main media usage attitude. The other findings of the paper include a novel multidimensional burst detection algorithm and an insight analysis of the human media selection process

Core Decomposition in Multilayer Networks: Theory, Algorithms, and Applications

Multilayer networks are a powerful paradigm to model complex systems, where multiple relations occur between the same entities. Despite the keen interest in a variety of tasks, algorithms, and analyses in this type of network, the problem of extracting dense subgraphs has remained largely unexplored so far. In this work we study the problem of core decomposition of a multilayer network. The multilayer context is much challenging as no total order exists among multilayer cores; rather, they form a lattice whose size is exponential in the number of layers. In this setting we devise three algorithms which differ in the way they visit the core lattice and in their pruning techniques. We then move a step forward and study the problem of extracting the inner-most (also known as maximal) cores, i.e., the cores that are not dominated by any other core in terms of their core index in all the layers. Inner-most cores are typically orders of magnitude less than all the cores. Motivated by this, we devise an algorithm that effectively exploits the maximality property and extracts inner-most cores directly, without first computing a complete decomposition. Finally, we showcase the multilayer core-decomposition tool in a variety of scenarios and problems. We start by considering the problem of densest-subgraph extraction in multilayer networks. We introduce a definition of multilayer densest subgraph that trades-off between high density and number of layers in which the high density holds, and exploit multilayer core decomposition to approximate this problem with quality guarantees. As further applications, we show how to utilize multilayer core decomposition to speed-up the extraction of frequent cross-graph quasi-cliques and to generalize the community-search problem to the multilayer setting

Data-Driven Implementation To Filter Fraudulent Medicaid Applications

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

Data-Driven Implementation To Filter Fraudulent Medicaid Applications

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Prediction, evolution and privacy in social and affiliation networks

In the last few years, there has been a growing interest in studying online social and affiliation networks, leading to a new category of inference problems that consider the actor characteristics and their social environments. These problems have a variety of applications, from creating more effective marketing campaigns to designing better personalized services. Predictive statistical models allow learning hidden information automatically in these networks but also bring many privacy concerns. Three of the main challenges that I address in my thesis are understanding 1) how the complex observed and unobserved relationships among actors can help in building better behavior models, and in designing more accurate predictive algorithms, 2) what are the processes that drive the network growth and link formation, and 3) what are the implications of predictive algorithms to the privacy of users who share content online. The majority of previous work in prediction, evolution and privacy in online social networks has concentrated on the single-mode networks which form around user-user links, such as friendship and email communication. However, single-mode networks often co-exist with two-mode affiliation networks in which users are linked to other entities, such as social groups, online content and events. We study the interplay between these two types of networks and show that analyzing these higher-order interactions can reveal dependencies that are difficult to extract from the pair-wise interactions alone. In particular, we present our contributions to the challenging problems of collective classification, link prediction, network evolution, anonymization and preserving privacy in social and affiliation networks. We evaluate our models on real-world data sets from well-known online social networks, such as Flickr, Facebook, Dogster and LiveJournal