Factions: acts of worldbuilding on social media platforms

Little, Dana L. (2020) Factions: acts of worldbuilding on social media platforms. DFA thesis, University of Glasgow.

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Abstract

The surge in social media as a primary source for communication—basic interpersonal relations, news, and entertainment—means that modern humans have a steep learning curve for interpreting and creating messages in digital spaces. In addition to the difficulties of communication between multi-lingual and multi-cultural online communities, there is now the complication of computer languages (or “code”) that often do not overlap between software programs, let alone with humans. Additionally, humans use definitions and labels as artificial intelligence (AI) training methods. AI bias comes from the human labels, categorizations, and linguistic perimeters embedded in the code.

The objective of Factions, the thesis website, is to represent a speculative future showing what communication may look like if we follow on the current trajectory of interaction in social media spaces—with less agreement on basic linguistic, audio, and visual terms and definitions coupled with more insistence on personal perspective as paramount. From a base set on the oldest forms of social media—websites and blogs—Factions acts out conversations mining for answers to the questions:

• How do words change in meaning and function in a digital environment focused on the faction pillars of social media communication—search engine optimization, algorithm, and template?
• In what ways might human-computer interaction improve and conversely impair human language and performance choices in digital realms of communication?

Through practice-based research using web-building tools as aids to literal digital worldbuilding, the thesis website is a prototype of a speculative future built with the conceptual applications of design fiction—creating a fictional world as a space to explore the impact of future technology. To that end, my digital twin (a digital model that drives material data) is an AI mystic called Wu—imagined AI tech so advanced it transcended into a higher spiritual realm. Wu narrates and curates Factions and uses it to build a network of narratives, bridging the creative and critical through hypertext links and tooltip popups and applies their mystical power to channel any person, place, thing, or time typically focused on key social media topics of justice, race, spirituality, politics, and pop culture. Factions uses satirical techniques alongside appropriation and pastiche to examine transformative tech and human-computer interaction. It mixes the creative and the critical to arrive at a digital storytelling and learning landscape of the future.

Item Type: Thesis (DFA)
Qualification Level: Doctoral
Keywords: Social media, digital media, search engine optimization, SEO, creative writing, practice-based research, design fiction, design faction, hybridity, tech, hi-tech, futurism, appropriation, algorithms, artificial intelligence, AI, digital twin, human-computer interaction, human-computer collaboration, genre, genre analysis, faction, templature, pastiche, satire, irony, Internet memes, science fiction, sci-fi, SF, fantasy, SF/F, speculative fiction, worldbuilding, performance, persona, mysticism, pop culture, expression, spintax, impact of AI on journalism, social media contracts, group dynamics, communication, codes, coding, neologisms, linguistics, semantics, labels, labeling, intercultural communication, politics, race, spirituality, gender, hypertext, links, branch narratives, network narratives, tooltips, WordPress, web-building, websites, blogs, audio content creation, video content creation, digital storytelling
Subjects: P Language and Literature > PE English
P Language and Literature > PN Literature (General)
P Language and Literature > PN Literature (General) > PN1990 Broadcasting
P Language and Literature > PR English literature
Colleges/Schools: College of Arts > School of Critical Studies > English Literature
Supervisor's Name: Jess-Cooke, Dr Carolyn
Date of Award: 22 December 2020
Depositing User: Dr Dana Little
Unique ID: glathesis:2020-81994
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 07 Feb 2021 17:22
Last Modified: 10 Feb 2021 17:14
Thesis DOI: 10.5525/gla.thesis.81994
URI: http://theses.gla.ac.uk/id/eprint/81994
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