This work is a data analysis of titles, comments and images mediated from the Design section of the website dezeen.com. One years’ worth of content from April ’16 to April ‘17 was scraped and re-composed into the Dezeen Bot List, using machine learning, a technique that can teach an algorithmic bot a specific language. The Dezeen Bot has been taught the language typically used by users of the Dezeen comments section. The resulting text might be strange, fragmented and ‘disinformative though similar in style, tone and substance to the original comments. Dezeen Bot List references an ironic twist on the Dezeen Hot List.
In the design section of Dezeen, the contrast between highly curated articles/titles and uncontrolled, blunt and often vicious user’s comments – sporadically appearing here in there on the platform – sparked our interest. We wanted to explore the ‘backend’ of this curated journalistic language, next to the emotive tone of comments through data analysis. This allowed us to reflect on editorial patterns adjacent to commenter behaviour in an archival, quantitative or fact-based manner. Working together with programmer Jayson Haebich, this backend language was collected, deconstructed and rearranged in chronological order and positioned in scrolling lists of nouns, names, verbs and adjectives. Through this chronological arrangement of words, from most used to least used, the dominant semantic ideology of titles and comments unfolds. Words can be highlighted to appear again in the context of either the title or the comment which provides an analytical tool for observation.
www.dezeenbotlist.com
Collaboration with Anastasia Kubrak, Isabel Mager and programmer Jayson Haebich