Ten Challenges for Digital Humanities and the Way Forward: Revisited From the Social Context

Ten Challenges for Digital Humanities and the Way Forward: Revisited From the Social Context

Stella Markantonatou (Athena – Research and Innovation Center in Information, Communication and Knowledge Technologies, Greece), Simon Donig (University of Passau, Germany), George Pavlidis (Athena – Research and Innovation Center in Information, Communication and Knowledge Technologies, Greece), Thomas Gees (Institut Digital Enabling, Berner Fachhochschule, Switzerland) and Adamantios Koumpis (Institut Digital Enabling, Berner Fachhochschule, Switzerland)
Copyright: © 2020 |Pages: 9
DOI: 10.4018/978-1-7998-2871-6.ch014

Abstract

In a previous article, the authors came up with a list of what they considered 10 challenges that would define the area of digital humanities at large and their evolution in the next years. However, in the almost two years that have passed since the publication of that paper, they are now able to see the need for relating the challenges for digital humanities with what one may characterize as socially relevant topics by means of outlining 10 challenges where the digital humanities can make a social impact. This chapter does that.
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The Challenges

The difference between the aforementioned original paper and the current one is that while in the first we took a top-down approach by axiomatically defining some guiding principles – which by the way still believe they are all holding and are still valid, we now are able to see and admit that the impact that such an approach has is very limited. So based on this, we now suggest a bottom-up approach that will pay special attention to needs and problem areas where Digital Humanities can be what people call a game-changer. So in the following paragraphs, we list these newly formulated challenges, which we aspire to help spark a dialogue in the academic and the research communities.

Challenge 1: Fake news

There is a wide field for open-ended, high-risk research in the area of fake news. One may, for example, consider the case of a broad multilingual corpus for training and testing Natural Language Processing tools for computing veracity. Such a research action could build on existing research infrastructures and cutting-edge Natural Language Processing components that are already developed and are based on e.g. hybrid knowledge-based and/or deep learning approaches for text analytics and cross lingual aspect-oriented sentiment analysis. Research in this context would demonstrate in real world settings several yet-not-met goals such as:

  • a)

    back testing and historical processing for detection of fake news in the past for a variety of online media – much of which may refer to archived news reports and blogs, social media, etc.;

  • b)

    capabilities to detect emergence of fake news patterns in near real time mode;

  • c)

    based on (a) and (b), to propose an early and weak signals detector for identifying potential fake news spots in online media, blogs, Facebook, Twitter accounts, etc. given historical records of content exchanges and news reporting / content presentation styles.

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