Mining, Shaping, Visualizing, and Interpreting Instagram Hipertextual Networks of Freight Train Graffiti Communalities in North America Using Machine Learning Custom Models and Graphology
Abstract
The practice of benching in graffiti has evolved over time, transitioning from a gathering point for graffiti writers in New York City subway stations, where they admired and valued the artwork on passenger vehicles, to becoming an integral part of graffiti on freight trains in North America. Nowadays, interventions decorating rolling stock that circulates transnationally are documented and shared in benching communities. Although the dynamics and geographical reach have shifted from hyperlocal to international through online platforms, the underlying principle remains the same: benching serves as a meeting place where writers appreciate each other’s work and gain recognition.
This methodological-practical study explores the possibilities of analyzing communalities among graffiti writers on freight trains through their online publications. Communalities can be derived from data such as the types of documented graffiti, the number of likes, the quantity of comments, the communal glossary used in hypertextual tags, and the volume of posts published inside those hashtags.
This text revolves around the exposure of three hypertextual conversations with different mining scales and analyzing scopes. It showcases the hashtags of a graffiti writer in freight trains (#kosm), a communal meeting point hashtag (#freightgraffiti), and a geographically focused hashtag (#portlandbench). By selecting the seed node in the mining iterator, different types of symbolic exchanges, participants, and content within Instagram metadata and those generated through training and inference of machine learning models can be analyzed.
While the interpretation of these three examples is central, the text also presents the encoded computational techniques for data extraction, construction, and visualization of user-generated conversations on Instagram. Parameters such as depth, the number of mined posts, and the concept of seed node in data mining are discussed. The text addresses the limitations and capabilities of the machine learning models used, including object detection in images and categorization of hypertextual tags in posts. Additionally, it highlights data cleansing and parameters such as gravity, scale-ratio, and centrality measures used for real-time visualization achieved through Graphology.