| Reference : SOCIAL NETWORK ANALYSIS FOR DIGITAL HUMANITIES |
| Dissertations and theses : Doctoral thesis | |||
| Engineering, computing & technology : Computer science | |||
| Computational Sciences | |||
| http://hdl.handle.net/10993/50638 | |||
| SOCIAL NETWORK ANALYSIS FOR DIGITAL HUMANITIES | |
| English | |
Fiscarelli, Antonio Maria  [University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM) > >] | |
| 17-Jun-2021 | |
| University of Luxembourg, Belval, Luxembourg | |
| Docteur en Informatique | |
| 127 | |
| Bouvry, Pascal | |
| Fickers, Andreas | |
| [en] Social Network Analysis ; Science of Science ; Community Detection | |
| [en] Current trends in academia show that a key factor for tackling complex problems and doing
successful research is interdisciplinarity. With the increasing availability of digital tools and online databases, many disciplines in the humanities and social sciences are seeking to incorporate computational techniques in their research workflow. Digital humanities (DH) is a collaborative and interdisciplinary area of research that bridges computing and the humanities disciplines, bringing digital tools to humanities scholars to use, together with a critical understanding of such tools. Social network analysis is one of such tools. Social network analysis focuses on relationships among social actors and it is an important addition to standard social and behavioral research, which is primarily concerned with attributes of the social units. In this work we present the field of digital humanities and its current challenges, as well as an overview of the most recent trends in historical network research, emphasizing the advantages of using social network analysis in history and the missed opportunities. We then present the field of network analysis, providing a formalization of the concept of social network, models that explain the mechanism governing complex networks and tools such as network metrics, orbit analysis and Exponential Random Graph Model. We tackle the problem of community detection. We propose MemLPA, a new version of the label propagation algorithm, by incorporating a memory element, in order for nodes to consider past states of the network in their decision rule. We present a use case, drawn from the collaboration with a historian colleague, showing how social network analysis can be used to answer research questions in history. In particular, we addressed the gender and ethnic bias problem in computer science research by looking at different collaboration patterns in the temporal co-authorship network. Finally, we present another use case, based on collaboration data collected at the National Electronics and Computer Technology Center (NECTEC) in Thailand. We build a temporal collaboration network where researchers are connected if they worked together on one or more artifacts, focusing on measuring productivity and quality of research and development, while linking these metrics to the structure of the collaboration network. | |
| Luxembourg Centre for Contemporary and Digital History (C2DH) > Doctoral Training Unit (DTU) | |
| Fonds National de la Recherche - FnR | |
| http://hdl.handle.net/10993/50638 |
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