Leveraging Sociological Models for Predictive Analytics

Abstract—There is considerable interest in developing techniques for predicting human behavior, for instance to enable emerging contentious situations to be forecast or the nature of ongoing but “hidden ” activities to be inferred. A promising approach to this problem is to identify and collect appropriate empirical data and then apply machine learning methods to these data to generate the predictions. This paper shows the performance of such learning algorithms often can be improved substantially by leveraging sociological models in their development and implementation. In particular, we demonstrate that sociologically-grounded learning algorithms outperform gold-standard methods in three important and challenging tasks: 1.) inferring the (unobserved) nature of relationships in adversarial social networks, 2.) predicting whether nascent social diffusion events will “go viral”, and 3.) anticipating and defending future actions of opponents in adversarial settings. Significantly, the new algorithms perform well even when there is limited data available for their training and execution. Keywords—predictive analysis, sociological models, social networks, empirical analysis, machine learning. I

Predictive Non-equilibrium Social Science

Non-Equilibrium Social Science (NESS) emphasizes dynamical phenomena, for instance the way political movements emerge or competing organizations interact. This paper argues that predictive analysis is an essential element of NESS, occupying a central role in its scientific inquiry and representing a key activity of practitioners in domains such as economics, public policy, and national security. We begin by clarifying the distinction between models which are useful for prediction and the much more common explanatory models studied in the social sciences. We then investigate a challenging real-world predictive analysis case study, and find evidence that the poor performance of standard prediction methods does not indicate an absence of human predictability but instead reflects (1.) incorrect assumptions concerning the predictive utility of explanatory models, (2.) misunderstanding regarding which features of social dynamics actually possess predictive power, and (3.) practical difficulties exploiting predictive representations.Comment: arXiv admin note: substantial text overlap with arXiv:1212.680

Learning Analytics to Support Experiential Learning

Experiential learning is increasing in prominence within higher education institutions. There is a lack of technology explicitly designed to support experiential learning pedagogies. This paper presents research that brings together learning analytics and learning theory to explore how technology could better support experiential learning programs. The research outcomes will be followed by a reflexive discussion about how insights from the research could impact the practice of experiential learning

Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used machine learning model in various disciplines while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluation on several studies validated the privacy guarantees, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc

Deriving Value from Big Data Analytics in Healthcare: A Value-focused Thinking Approach

With the potential to generate more insights from data than ever before, big data analytics has become highly valuable to many industries, especially healthcare. Big data analytics can make important contributions to many areas, such as enhancements in the quality of patient care and improvements in operational efficiencies. Big data analytics provides opportunities to address concerns such as disease diagnoses and prevention. However, it has posed challenges such as data security and privacy issues. Also, healthcare institutions have concerns about deriving the greatest benefit from their big data analytics endeavors. Therefore, identifying actionable objectives that can help healthcare organizations derive the maximum value from big data analytics is needed. Using the value-focused thinking (VFT) approach, we interviewed individuals associated with data analytics in healthcare to identify actionable objectives that one needs to consider to derive value from big data analytics, which practitioners can use for their own endeavors and provide opportunities for future research

Entrepreneurship, innovation and inequality : exploring territorial dynamics and development

The ongoing advances in technology have brought significant improvements in the processing speed and storage of large volumes of data. Tech-savvy organisations have already started using big data with a goal to improve their decision making, agility and customer-centric approaches. Today, many tourism marketers are hyper-targeting consumers with real-time mobile ad campaigns to drive conversions. They use analytics to identify how exogenous variables, including the broader economy, competitive offerings and even the weather can affect their organisational performance. Similarly, the smaller enterprises are economically gathering and storing data from each and every customer transaction. They use analytics to customise their offerings and improve their customer engagement. Therefore, this chapter builds on the previous theoretical underpinnings on smart tourism. It clarifies how smart, disruptive technologies have led to endless opportunities for tourism and hospitality marketers to gain a competitive advantage. It explains how they are leveraging themselves by utilising contemporary marketing strategies and tactics that are customer-focused. The researcher examines the use of big data, analytics, programmatic advertising and blockchain technologies in the realms of tourism and hospitality.peer-reviewe

A SYSTEMATIC REVIEW OF COMPUTATIONAL METHODS IN AND RESEARCH TAXONOMY OF HOMOPHILY IN INFORMATION SYSTEMS

Homophily is both a principle for social group formation with like-minded people as well as a mechanism for social interactions. Recent years have seen a growing body of management research on homophily particularly on large-scale social media and digital platforms. However, the predominant traditional qualitative and quantitative methods employed face validity issues and/or are not well-suited for big social data. There are scant guidelines for applying computational methods to specific research domains concerning descriptive patterns, explanatory mechanisms, or predictive indicators of homophily. To fill this research gap, this paper offers a structured review of the emerging literature on computational social science approaches to homophily with a particular emphasis on their relevance, appropriateness, and importance to information systems research. We derive a research taxonomy for homophily and offer methodological reflections and recommendations to help inform future research

The use of data-driven technologies for customer-centric marketing

The latest technologies are shifting how businesses capture, analyse and distribute data from the individual users’ online activity. Therefore, this contribution critically reviews the latest developments on big data analytics and programmatic advertising. Moreover, it sheds light on the use of blockchain; as this distributed ledger technology provides secure, verified transactions among marketplace stakeholders. The findings suggest that the service providers are increasingly utilising data-driven technologies including programmatic advertising tools to target and re-target individuals online or on their mobile. However, individuals and organisations are becoming increasingly aware on data protection issues, as they often block marketers from tracking them and serving them ads. In conclusion, this contribution puts forward a theoretical framework that explains how, why, where and when practitioners are capturing, analysing and distributing data. In sum, it implies that the data-driven technologies are facilitating the businesses’ customer-centric marketing.peer-reviewe