Over many years we’ve been looking at the emergence of “organized networks” as an alternative concept that could counter the social media platform a priori of gathering (and then exploiting) “weak links.”[1] Organized networks invent new institutional forms whose dynamics, properties, and practices are internal to the operational logic of communication media and digital technologies. Their emergence is prompted, in part, by a wider social fatigue with and increasing distrust of traditional and modern institutions such as the church, political party, firm, and labour union, which maintain hierarchical modes of organization. While not without hierarchical tendencies (founders, technical architectures, centralized infrastructures, personality cults), organized networks tend to gravitate more strongly toward horizontal modes of communication, practice, and planning.
MULTIFILE
This project explores and develops methods for open archiving of socalled "new naturals". A number of tools and templates were created to facilitate collaborative, global - but context-aware and localized - documenting and archiving of "new naturals":
LINK
Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention towards 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 circulation and contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by networked 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 is limited. However, combining automated analyses of images - broken down by their compositional elements - with repurposing platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This paper explores the capacities of platform data - hashtag modularity 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 by networked publics that co-create meaning.
MULTIFILE
