Machine Vision and Social Media Images: Why Hashtags Matter
By Marloes Geboers, Chad Thomas Van De Wiele. 2020
27 citations recorded by the source corpus.
Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention toward the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by online publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images remains limited. Combining automated analyses of images with platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This article explores the capacities of hashtags and retweet counts to complement the automated assessment of social media images, doing justice to both the visual elements of an image and the contextual elements encoded through the hashtag practices of networked publics.
Subfields: Cognitive Neuroscience, Law, Urban Studies, Natural Language Processing, Multimodal Learning
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Citations in the corpus
- Diagnosing Gender Bias in Image Recognition Systems (2020, background): “…in commercial enterprise (e.g., Greenfield 2018; HG Insights 2020) and, increasingly, for social research (e.g., Di Ronco and Allen-Robertson 2020; Garimella and Eckles 2020; Geboers and Van de Wiele 2020; Gelman, Mattson, and Simpson 2018; Webb Williams, Casas, and Wilkerson 2020; Xi et al. 2019).”