Digital Tools for Mythologizing the Past: Social Media and Neural Networks
The article is devoted to the study of the processes of mythologising the past in the digital age, where social media and neural networks are becoming key tools for the formation of collective memory. The authors analyse how digital technologies transform the ways of constructing historical narratives, blurring the boundaries between scientific knowledge and philistine notions of the past. Particular attention is paid to the role of socio-epistemic arenas where professional historians, history buffs, masses of Internet users and digital technologies interact as equals, creating new forms of historical knowledge. The article discusses examples of mythologising the past in social media such as Wikipedia, Internet memes, computer games and fanfics, and explores the influence of neural networks (GPT4, Midjourney, Gemini) on historical content creation. The authors emphasise that neural networks, capable of generating both reliable information and new myths, become active participants in the myth-making process. The article also touches upon the transformation of collective memory in the digital age, where media memory is becoming a new form of preservation and transmission of historical information. The authors conclude that digital technologies, on the one hand, open up new opportunities for the study and popularisation of history, and on the other hand, pose challenges to society in preserving historical accuracy in the context of mass myth-making. It concludes by emphasising the need to find a balance between the use of digital technologies and maintaining academic rigour in historical research.
Authors
- Vladimir A. Syusyukin
- Denis S. Artamonov (ORCID: https://orcid.org/0000-0001-8689-1948)
Institutions
- Saratov State University (RU)
- Lipetsk State Technical University (RU)
Publication Details
- Published
- 2025-04-09
- DOI
- https://doi.org/10.1109/comsds65569.2025.10971334
- Primary Topic
- Digital Humanities and Scholarship
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Russian Science Foundation