It is now abundantly clear to librarians, archivists, computer scientists, and many social scientists that we are in a transformational age. If we can understand and measure meaning from all of these data describing so much of human activity, we will finally be able to test and revise our most intricate theories of how the world is socially constructed through our symbolic interactions.
Introduction: Living in the San Francisco Bay Area, one quickly develops an allergy to any claim of a “revolution” in a particular field. But it is now abundantly clear to librarians, archivists, computer scientists, and many social scientists that we are in a transformational age. Terabytes of textual and video data are being created or scanned into existence everyday. While these data include silly tweets, they also include the archives of national libraries, news accounts of activities around the world, journal articles, online conversations, vital email correspondence, surveillance of crowds, videos of police encounters, and much more. If we can understand and measure meaning from all of these data describing so much of human activity, we will finally be able to test and revise our most intricate theories of how the world is socially constructed through our symbolic interactions.
But that’s a big “if.” Natural language and video data, compared to other data computer scientists have been pushing around for decades, are incredibly difficult to work with. Computers were initially built for data that can be precisely manipulated as unambiguous electrical signals flowing through unambiguous logic gates. The meaning of the information encoded in our human languages, gestures, and embodied activities, however, is incredibly ambiguous and often opaque to a computer. We can program the computer to recognize certain “strings” of letters, and then to perform operations on them (much like the operator of Searle’s Chinese room), but no one yet has programmed a computer to experience our human languages as we do. That doesn’t mean we don’t try. There are three basic approaches to helping computers understand human symbolic interaction, and language, in particular:
- We can write rules telling them how to treat all the different multi-character strings (i.e., words) out there.
- We can hope that general artificial intelligence will just “figure it out.”
- We can show computers how we humans process language, and train them through an iterative process, to read and understand more like we do.
The first two approaches are doomed, and I’ll say more about why. The third approach provides a way forward, but it won’t be easy. It will require that researchers like us recruit hundreds or thousands of people (i.e., crowds) into our processes. So, unpacking this post’s title: our ability to make sense of and systematically analyze the dense, complex, manifold meaning inhering in now ubiquitous and massive textual and video data will depend on our ability to enlist the help of many other humans who already know how to understand language, situations, emotion, sarcasm, metaphor, the pacing of events, and all the other aspects of being an agentic organism in a socially constructed world—all the stuff of social life that computers just won’t ever understand without our help.
The social data revolution will be crowdsourced
December 8, 2016 | Nicholas B. Adams | Parameters
Editors’ Choice: The Social Data Revolution will be Crowdsourced
December 13, 2016 | Digital Humanities Now