When reading this week’s article about Netflix’s use of metadata and use of categorizing genres, I was struck by the author’s question: “How do you systematically dismember thousands of movies using a bunch of different people who all need to have the same understanding of what a given microtag means?” This inquiry took me back to our discussion in class where we talked about how assigning a category to something implies a belief about that item or an ideology about the world that may not be universally held. If it were up to the viewers to assign the categories, their differences in perspective would therefore yield different interpretations of what the genres should be. The way Netflix was able to address this problem was through establishing a systemized rating system for different parts of movies; in other words, turning to the actual content to speak for itself when choosing a label for it. In this way, many different tangible parts of the movie came together to create a single, specific, and coherent genre for itself. By allowing the content itself to create the categories, the possibility of introspection (when genres tell the viewer not just what they would like, but what kind of things they would like) becomes possible and adds more to the viewer’s discovery of not just movies, but himself in general.
This introspection reminded me of Pinterest’s use of its “Guided Search” feature, described in the article “Pinterest puts metadata to good use with Guided Search” (http://www.techtimes.com/articles/6081/20140425/pinterest-puts-metadata-to-good-use-with-guided-search.htm ). Basically, the system uses user-generated metadata from the titles, comments, and descriptions made on individual pins to classify it with sub-categories that pop up when a user makes a broad search, allowing him or her to choose a more specific search within the broad category if he or she chooses. This use of metadata derived from the actual content of the pin allows users to stumble across subcategories that are actually pertinent to them, instead of being confined to only the website’s broad thirty-two categories. The more tailored your search, the more the system can detect the user’s specific likes, and thus make more suggestions to material it knows they will like. Similar to Netflix, this process also displays introspection in that it shows the user what kind of things they like, not just what they like. This reliance on content to complete the digital categorization of a topic mirrors that of the field of Digital Humanities in general. Our job is to unite human created content with technologically created classification systems to enhance the way we discover, view, and analyze information.