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Article published In:
Register Studies
Vol. 4:1 (2022) ► pp.91–131

Multi-Dimensional Exploratory Factor Analysis of TED talks

ORCID logo | The Open University
ORCID logo | University of Queensland
This article conducts Exploratory Factor Analysis (EFA) on a corpus of TED talks (2463 talks, across 427 topic tags) to create a new Multi-Dimensional model. The resultant model contained seven dimensions: i. ‘Spontaneous involved versus edited informational discourse’, ii. ‘Abstract informational versus narrative discourse’, iii. ‘Human-world oriented versus object-oriented discourse’, iv. ‘Subjective perspectives’, v. ‘Persuasive stance’, vi. ‘Expert elaboration’, and vii. ‘Change and inspiration’. When the model was compared to prior research, similarity with MD models based in academic texts was observed. However, some dimensions were found to be indicative of the unique nature of TED talks, such as expert elaboration and change and inspiration. When the EFA model was mapped onto the TED corpus’s subcorpora (defined by topic tags), individual disciplines were characterised in terms of the dimensions and some traditional academic groups were observed.
Keywords: TED talks, multi-dimensional analysis, Exploratory Factor Analysis, register studies, corpus linguistics
Article outline
  • 1.Introduction
  • 2.TED Talk corpus
    • 2.1Compilation
    • 2.2Situational characteristics of TED talks
  • 3.Method
    • 3.1Corpus annotation for MD
    • 3.2Exploratory factor analysis
  • 4.Results
    • 4.1EFA model
    • 4.2Dimension 1 spontaneous involved versus edited informational discourse
    • 4.3Dimension 2 abstract informational versus narrative discourse
    • 4.4Dimension 3 human-world oriented versus object oriented discourse
    • 4.5Dimension 4 subjective perspectives
    • 4.6Dimension 5 persuasive stance
    • 4.7Dimension 6 expert elaboration
    • 4.8Dimension 7 change and inspiration
  • 5.Discussion and conclusion
  • References
Published online: 15 September 2022
DOI logo
https://doi.org/10.1075/rs.21008.win
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