We introduce a general framework for analyzing large-scale text-based data, combining the strengths of neural-network language processing and generative statistical modeling. Our methodology generates textual factors by (i) representing texts using vector word embedding, (ii) clustering words...

Society has become increasingly interconnected with networks linking people, the environment, information, and technology. This complexity is a challenge for risk analysis. Traditional risk analysis methods typically underestimate the probability and impact of risks (e.g., terrorist attacks,...

Google occupies a powerful position within the United States economy, a position which many have begun to consider too powerful. Google’s power is derived almost entirely from how it uses the billions of pieces of information it collects on its users—a collection of information known as big...

In this paper, we introduce the concept of a Digital Layer to empirically investigate inter-firm relations at any geographical scale of analysis. The Digital Layer is created from large-scale, structured web scraping of firm websites, their textual content and the hyperlinks among them. Using...

While there is now significant literature in law, politics, economics, and other disciplines that examines tax havens, there is little information on what tax haven intermediaries — so-called offshore service providers — actually do to facilitate offshore evasion, international money...

Given the volumes of data that are now generated by mobile networks due to the almost ubiquitous use of mobile phones by the majority of the population, this data can be considered as big data. Spurred by the exponential growth of mobile connectivity, the attendant large volumes of mobile...

We investigate the data-driven newsvendor problem when one has n observations of p features related to the demand as well as historical demand data. Rather than a two-step process of first estimating a demand distribution then optimizing for the optimal order quantity, we propose solving the...

Recent advancements in data collection have expanded the tools and information available for urban and spatial-based research. This paper presents an overview of spatial big data sources used in urban science and urban economics, with the goal of directing and enriching future research by other...

Computational social science (CSS) is an interdisciplinary field of social science that integrates individual social science disciplines. Its purpose is to advance scientific understanding of social phenomena through the medium of computing, which is used both as a paradigm and a methodological...

Purpose: The objective of the paper is to find trends of research in relic tourism-related topics. Specifically, this paper uncovers all published studies having latent issues with the keywords “relic tourism” from the Web of Science database.Methods: A total of 109 published articles...