Image networks and practice analysis of larger data corpora. An approach to cluster and recontextualize visual practice in social media

Authors

  • Wolfgang Reißmann FU Berlin, Institute for Media and Communication Studies, Journalism Studies https://orcid.org/0000-0001-9032-5586
  • Miriam Siemon FU Berlin, Institute for Media and Communication Studies, Journalism Studies https://orcid.org/0000-0002-3343-8880
  • Moe Kinoshita FU Berlin, Institute for Media and Communication Studies, Journalism Studies

DOI:

https://doi.org/10.24434/j.scoms.2024.01.3883

Keywords:

image network analysis, multi-methods, large data corpora, visual practice, image clusters, image types

Abstract

This paper reports a methodological exploration combining image network analysis and standardized practice analysis on social media data. Through applying the open source software Memespector to access the Clarifai API, the potential of an easy-at-hand image tagging tool as an instrument to manage larger data corpora is explored. Using the example of the German-speaking Twitter hashtag #systemrelevant, we relate image clusters to the results of standardized practice analysis of posts that contain images. The proposed method is intended for research that attempts to carve out the co-constituting of public discourse in social media by different groups of actors. The approach systematically differentiates the contributions of societal groups such as journalism, civil society, or private individuals, and the embedding of their tweets in selected anchoring practices and further modalities of participation. Altogether, the multistep analytical process offers a possible approach to process larger image corpora, while maintaining a sensitivity for the practice-theoretical demand of (re)contextualizing image use.

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Published

2024-05-08

How to Cite

Reißmann, W., Siemon, M., & Kinoshita, M. (2024). Image networks and practice analysis of larger data corpora. An approach to cluster and recontextualize visual practice in social media. Studies in Communication Sciences, 24(1), 123–139. https://doi.org/10.24434/j.scoms.2024.01.3883

Issue

Section

Thematic Section: Images, clusters and types