Accepted author manuscript, 2.46 MB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Using ATLAS.ti for constructing and analysing multimodal social media corpora
AU - Sha, Yuze
AU - Malory, Beth
PY - 2025/2/14
Y1 - 2025/2/14
N2 - Methods to enable comprehensive corpus analyses of multimodal data are imperative to furthering our understanding of social media and digital communication. In this study, we demonstrate how ATLAS.ti version 24.2.0 can be used to construct such corpora and conduct multimodal corpus-assisted discourse studies (MCADS). The focus for such studies may be the exploration of complex patterns of co-occurrence, both intra- and inter-mode, or iterative corpus queries, especially when unexpected patterns move the research focus beyond initial research questions. In such cases, ATLAS.ti’s functionalities facilitate a triangulation of automatic pattern recognition and in-depth manual analysis. It supports a flexible, user-defined approach to the multimodal analysis of short-form social media datasets, overcoming traditional limitations such as analyses being restricted to emojis, or pre-set thematic dimensions in current AI software. In this way, the proposed methodology enables in-depth MCADS, as illustrated in the case study presented in this paper, which analyses the co-occurrences of evaluations and visual representations of social actors.
AB - Methods to enable comprehensive corpus analyses of multimodal data are imperative to furthering our understanding of social media and digital communication. In this study, we demonstrate how ATLAS.ti version 24.2.0 can be used to construct such corpora and conduct multimodal corpus-assisted discourse studies (MCADS). The focus for such studies may be the exploration of complex patterns of co-occurrence, both intra- and inter-mode, or iterative corpus queries, especially when unexpected patterns move the research focus beyond initial research questions. In such cases, ATLAS.ti’s functionalities facilitate a triangulation of automatic pattern recognition and in-depth manual analysis. It supports a flexible, user-defined approach to the multimodal analysis of short-form social media datasets, overcoming traditional limitations such as analyses being restricted to emojis, or pre-set thematic dimensions in current AI software. In this way, the proposed methodology enables in-depth MCADS, as illustrated in the case study presented in this paper, which analyses the co-occurrences of evaluations and visual representations of social actors.
U2 - 10.1515/lingvan-2024-0066
DO - 10.1515/lingvan-2024-0066
M3 - Journal article
JO - Linguistics Vanguard
JF - Linguistics Vanguard
SN - 2199-174X
ER -