Digital humanities | EPFL Graph Search

Digital humanities (DH) is an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities. It includes the systematic use of digital resources in the humanities, as well as the analysis of their application. DH can be defined as new ways of doing scholarship that involve collaborative, transdisciplinary, and computationally engaged research, teaching, and publishing. It brings digital tools and methods to the study of the humanities with the recognition that the printed word is no longer the main medium for knowledge production and distribution. By producing and using new applications and techniques, DH makes new kinds of teaching possible, while at the same time studying and critiquing how these impact cultural heritage and digital culture. DH is also applied in research. Thus, a distinctive feature of DH is its cultivation of a two-way relationship between the humanities and the digita

Online Dating Quantification Practices: A Human-Machine Learning Process

Jessica Carolina Pidoux

The phenomenon of online dating via web and mobile phone applications involves several actors: graphical interfaces, developers, algorithmic systems for user matching, and users. These actors have been studied in parallel by the social sciences and by computer science. While computer science research focuses on the design of more efficient recommendation systems and on the analysis of behavioral patterns, research in the humanities and social sciences is interested in the effects of platforms and their computational practices on the formation of affective relationships.This thesis explores how the mediation relations between the different actors of dating help to commonly establish the human-machine communication. Every actor learns in his-her own way a new conception of dating, mirroring the machineâs algorithmic efficiency. The study of these new dynamics of communication is bound to become key to software and algorithmic studies, for which the human-machine relation-ship has hitherto remained invisible in the study of dating platforms, or incomplete when it comes to analyzing developers' practices or algorithmic systems without taking into account the final uses of the platforms. Using a combination of qualitative and quantitative concepts and methodologies from sociology and data science, this thesis contributes to the comparative analysis of four actors who have a profound impact on the experience of online dating: graphical interfaces, developers, algorithmic matchmaking systems, and users. This is a study of more than 320 variables collected in more than 20 dating applications, both English and French, used in Switzerland and around the world. These variables mediate between the design of profile recommendation systems and their final use for dating. A case study is conducted on the massively-adopted dating application Tinder, which acts as an industry innovator, capable of establishing design conventions (e.g., swiping). This thesis further investigates, on the one hand, the development practices of nine founders and developers working in the industry, and on the other hand, the practices of 40 users of 26 different dating platforms. The results show that the interaction with dating platforms involves a collective learning process of experimentation that allows for communication between humans and machines. This fast-paced communication favors trial-and-error practices: actors either learn to adapt to the machine, or on the contrary to overcome it by accepting the mutual tolerance stemming from the uncertainty of actions. Learning is possible through the supposed social conventions of the actors, some of which are made explicit and quantifiable through graphical interfaces. This thesis contributes to the described research field with a theoretical framework and empirical data which can guide future research on online sociability at the intersection of computer and social sciences, like in the digital humanities.

EPFL2021

Reading Data Together

Dario Rodighiero

Network visualizations are the most complex visualizations possible, but sometimes they are not capable of describing system-complexity. Even if they are the most widely employed visualization techniques, they still have limitations. Indeed a) their relations are not sufficient to analyse complexity and b) networks do not distinguish between qualitative differences of represented entities. Starting from the actual network model, how could one manipulate this visualization to improve complexity comprehension? In this paper, we propose a solution called trajectory. The trajectory has two major points of difference compared to the network: the trajectory a) represents not only distances, but also durations, and b) it displays kinetic entities according to their evolution with time. The discourse is articulated around these four points. Considering that networks are tools widely used by digital humanists, we propose a new language to improve the quality of represented data: a new network based on a vertical timeline. Complexification of the network visualization is not just a new language, but also a tool that would give the field of Digital Humanities the most complex of all possible visualizations.

2016

Online Dating Quantification Practices: A Human-Machine Learning Process

Jessica Carolina Pidoux

EPFL2021