Cultural Analytics of Large Datasets from Flickr

Abstract

Deluge became a metaphor to describe the amount of information to which we are subjected, and very often we feel we are drowning while our access to information is rising. Devising mechanisms for exploring massive image sets according to perceptual attributes is still a challenge, even more when dealing with user-generated social media content. Such images tend to be heterogenous, and using metadata-only can be misleading. This paper describes a set of tools designed to analyze large sets of user-created art related images using image features describing color, texture, composition and orientation. The proposed pipeline permits to discriminate Flickr groups in terms of feature vectors and clustering parameters. The algorithms are general enough to be applied to other domains in which the main question is about the variability of the images.

Strategically matching messaging to the platform: The case of “Biolabs” on Instagram, Facebook, and Reddit

“…Imagery increases the effect of disinformation by providing an index of reality (Hameleers et al, 2020). To explore the visual relationships, the collage-based methods developed by Lev Manovich and team for the analysis of cultural images offer crucial leverage (Manovich, 2013; Ushizima, 2012). The implementation of these methods required manually scraping the images located by CrowdTangle and then processing them via a software library written for R designed to replicate and expand the affordances of the earlier plugins for ImageJ (image plot and image montage) (Faltesek et al, 2021; Manovich et al, 2014; Manovich & Giachino, 2012).…”