Chapter published in:Corpus Approaches to Social Media
Edited by Sofia Rüdiger and Daria Dayter
[Studies in Corpus Linguistics 98] 2020
► pp. 149–174
Chapter 7Constructing corpora from images and text
An introduction to Visual Constituent Analysis
Visual analysis represents a significant oversight in the corpus literature, and possibly one that may lead to unintended omissions, particularly when analysing social media. In this chapter we introduce Visual Constituent Analysis (VCA), a method of multimodal corpus construction that allows researchers to construct and analyse visual aspects of online media in large-scale corpora. The chapter addresses the shortcomings of a purely textual approach to discourse analysis when dealing with social media texts and offers a solution using computer ‘Vision’-based image annotation (in our case Google Cloud Vision). Finally, we demonstrate how our approach can be used to analyse a sample of 150,000 micro-blog posts from Twitter and show the difference in level of user interaction with combined image/texts over language-only social media texts.
- 2.Opportunities and obstacles – Why ‘visual’?
- 3.Tentative solutions – Constructing ‘constituents’
- 3.2Concatenating outputs
- 4.Analysing T-IRA with VCA
- 4.1Hostile state information operations
- 4.2T-IRA – a general overview
- 5.Quantifying the importance of images
- 5.1Images, likes and retweets
- 5.2Text-image overlap
- 6.T-IRA – A case study
- 6.3.1Image reference (IR)
- 6.3.2Image and text reference (ITR)
- 7.Concluding remarks
Published online: 04 November 2020
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