The Network of Online Stolen Data Markets: How Vendor Flows Connect Digital Marketplaces

In the face of market uncertainty, illicit actors on the darkweb mitigate risk by displacing their operations across digital marketplaces. In this study, we reconstruct market networks created by vendor displacement to examine how digital marketplaces are connected on the darkweb and identify the properties that drive vendor flows before and after a law enforcement disruption. Findings show that vendors’ movement across digital marketplaces creates a highly connected ecosystem; nearly all markets are directly or indirectly connected. These network characteristics remain stable following a law enforcement operation; prior vendor flows predict vendor movement before and after the interdiction. The findings inform work on collective patterns in offender decision-making and extend discussions of displacement into digital spaces

Research Methods for the Digital Humanities

In holistic Digital Humanities studies of information infrastructure, we cannot rely solely on the selection of any given techniques from various disciplines. In addition to selecting our research methods pragmatically, for their relative efficacy at answering a part of a research question, we must also attend to the way in which those methods complement or contradict one another. In my study on West African network backbone infrastructure, I use the tools of different humanities, social-sciences, and computer science disciplines depending not only on the type of information that they help glean, but also on how they can build upon one another as I move through the phases of the study. Just as the architecture of information infrastructure includes discrete “layers” of machines, processes, human activity, and concepts, so too does the study of that architecture allow for multiple layers of abstraction and assumption, each a useful part of a unified, interdisciplinary approach

Python Scrapers for Scraping Cryptomarkets on Tor

Cryptomarkets are commercial websites on the web that operate via darknet, a portion of the Internet that limits the ability to trace users’ identity. Cryptomarkets have facilitated illicit product trading and transformed the methods used for illicit product transactions. The survellience and understanding of cryptomarkets is critical for law enforcement and public health. In this paper, we design and implement Python scrapers for scraping cryptomarkets. The design of the scraper system is described with details and the source code of the scrapers is shared with the public

Project management in social data science : integrating lessons from research practice and software engineering

Online platforms, transaction processing systems, mobile sensors and other novel sources of data have shaped many areas of social research. The emerging discipline of social data science is subject to questions of epistemology, politics, ethics and responsibility, while the practice of doing social data science raises significant project management issues that include logistics, team communication, software system integration and stakeholder engagement. Keeping track of such a multitude of individual concerns while maintaining an overview of a social data science project as a whole is not trivial. This calls for provision of appropriate guidance for holistic project management. The project management issues in social data science are strikingly similar to those arising in software engineering. In this thesis, I adapt a particular software engineering project management tool – the SEMAT Essence model (Jacobson et al., 2013) – to the needs of social data science. This model offers a holistic management approach by addressing key project aspects, including the often overlooked yet crucially important ones such as maintaining stakeholder engagement and establishing the ways of working. The SEMAT Essence is a progress tracking model and does not assume any specific work process, which is valuable given the great diversity of social data science projects. To achieve this goal, I study the practice of doing social data science through participant observation of social data science projects and by providing ethnographic accounts for those. Using the ethnographic findings and the basic content and structure of the SEMAT model, I develop the Social Science Scorecard Deck – an agile project management tool for social data science. To assess the Scorecard Deck, I use the tool in management of a social data science project and then subject the tool to external validation by interviewing experts in social data science