Internet search has been marked by the dominant use of query search, specifically Google, since the mid-1990s. The public release of the AI-based search tool, chatGPT, powered by a recent innovation in deep learning known as large language models (LLMs), marks a paradigm shift in internet search technology. While the essence of both the search technologies, namely, the retrieval of information from the internet, remains the same, there appears to be a marked difference in the manner of their use and perception by both the general public as well as in media. Prior studies have highlighted the importance of assessing perceptions of new technology on users. Examining the impact of this recently-introduced form of search compared to the original query-search can provide valuable insights into users’ perception of search technologies as well as identify underlying attitudes towards AI. This study investigates the distinct discursive patterns characterising user perceptions of these two search paradigms. It uses a collected text corpus of media articles and forum data as its research material, and employs Latent Dirichlet Allocation (LDA) topic modelling to generate a quantitative set of topics. These are then examined qualitatively through the lenses of technological frames and discourse analysis to uncover user perceptions. Findings indicate that user discourse patterns diverge, anticipatory themes differ and there is variation in user concerns as well as media coverage. This research contributes insights into evolving technological perceptions, societal consequences, and the media’s role in shaping user discourse. It also highlights that further investigations into the anthropomorphic aspects of digitalisation and the evolving information landscape may offer promising avenues for future research.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-513285 |
| Date | January 2023 |
| Creators | Rahman, Mansur |
| Publisher | Uppsala universitet, Informationssystem |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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