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In the article “Using Metadata to find Paul Revere”, the author simplifies how to find connections between individuals and organizations. Although a social network has the potential has the potential to be incredibly complex, Healy emphasizes that a simple piece of information such as a man’s enrollment in a school, can help find the crossroad of not only individuals that went to the given institution, but also the relationship between the multiple institutions. He describes how little information about an individual can solve complex questions. “The foundational papers in this new science of social network analysis, in fact, are almost all about what you can tell about people and their social lives based on metadata only, without much reference to the actual content of what they say.”

The study “Continuity and Disruption in European Networks of Print Production, 1550-1750” takes what is mentioned above to a whole new level. The computational analysis of art to then use as social networking for painters in the Netherlands shows how far we’ve come in data cultures. The study not only finds trends between artists but finds whether popular artists set trends in the 17th century. Its always been known that the best artists can influence style and skill shifts in the world of art, but this computational analysis program allows us to find who, when, where and what exactly is shaping the artistic world.

JM mentioned something very relevant to these texts. She stated that “the problem with history is that people need to understand that everything that is written down comes from a perspective”. The past is very complex. The majority of history is proven to not have been recorded the way it actually was so it is vital for us to consider this when analyzing answers presented by a computer. Computers are smart, yet they can’t grasp the complexity of humanity’s flaws.