Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 14th June 2025, 06:40:09pm WEST

 
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Session Overview
Session
LP-40
Time:
Friday, 18/July/2025:
4:00pm - 5:30pm

Session Chair: Diane Katherine Jakacki, Bucknell University
Location: B207 (TB)

64 places

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Presentations

Patterns of Play: A Computational Approach to Understanding Game Mechanics

Andreas Niekler, Vera Piontkowitz, Sarah Schmidt, Janos Borst-Graetz, Manuel Burghardt

Leipzig University, Germany

This study employs a computational approach to analyze game mechanics using a collection of 296 predefined mechanics. By identifying their occurrences across a large dataset of games, we reveal trends in their popularity, evolution over time, and their relationships to game genres, demonstrating the method's potential for "computational game studies".



Transnational connections and barriers in DH: a UK-Chinese case study

Chen Jing1, Paul Joseph Spence2

1Nanjing University, China; 2King's College London, United Kingdom

In this bi-national study comparing attitudes towards digital humanities in China and the UK, we explore interviewee responses towards a number of questions around DH identity formation, research infrastructures and professional structures. We discuss proposals to foster greater transnational exchange, using China and the UK as a case study.



Uncovering hidden temporal and semantic dataset’s bias in hate speech: A Study of MetaHate's Diachronic and Lexical Variability

Patricia Martin-Rodilla1, Paloma Piot2

1Spanish National Research Council (CSIC), Spain; 2Information Retrieval Lab, University of A Coruña (Spain)

Digital Humanities must critically study datasets to avoid intrinsic bias. This paper analyses bias in 13 datasets from the largest meta-collection of hate speech datasets, discovering hidden bias as a temporal trend of reduced lexical variability and dispersion, and a disproportionate focus on specific social groups or language types.



 
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