Lev Manovich’s problem
To track and visualize global digital cultures is an end which Lev Manovich endeavors to accomplish in his essay “How to Follow Global Digital Cultures, or Cultural Analytics for Beginners”.
A culture differing to your own is difficult to miss — many embrace the eclectic human form and adopt key distinctions from others (i.e: black culture popularized in America and globally) whilst heralding back to the founders and innovators of said culture. We can trace cultural norms back to traditions held in a county or region via specific ‘data sets’ (nations forged on war, shipping nations, areas fueled by trade or natural commodities) which in turn influences its people’s sociology (aggressive opinions for/against war, aquatically inclined sports/recreation, upper class lifestyle in a booming economy resulting in privileged wealth status).
Similarly, global digital cultures are just a process of cause and effect. to put it more eloquently, we can see Manovich’s problem expressed in the endless dovetails of interest and adoption of ideas at an exponential scale that people tend to do, thanks to developments on the internet. His dilemma comes in practically tracking these cultures to cite milestones in the development of the psyche. This is a beneficial integration of cultures that would definitely leave a digital footprint; I’m not even going to pretend to be able to develop an algorithm to even broadly follow trends required to follow the development of global digital cultures. A strategy would be to integrate all key and current information saharing websites and posting communities that are currently active or slowly developing. I would use Facebook’s invasive marketing measures and Tumblr’s hive mind nest to pinpoint social media trends and popularity — Reddit and Voat to collate where those trending topics are sourced from. These complimentary resources would then again use Facebook’s data on age, gender and locale to pinpoint which areas are most affected by certain cultural norms and adversities. This would be collated in the long term and data sets formed to increase the understanding of this phenomenon.