The language and tone of this article was initially confusing, and seemed out of place in the context of the content of the article. Written in the prose of 18th century England, the dated language of the article is a sharp contrast to the technological terms and concepts that the article is focused on. Regardless, the author puts forth a clear and easily understood explanation of the way s/he used matrices to draw conclusions about a certain dataset. Matrix functions can be used to manipulate data in ways that expand the bounds of the original dataset. This expansion allows us to draw new conclusions about the given data, conclusions that we could not have assumed by looking at the original dataset. Though the old English tone of the article felt out of place, the explanation was straightforward and the status of the author as a “low-level operative” allowed him/her to use simple wording to plainly paint what was going on with the data. Additionally, the placing of the article in the 18th century eliminated most of the technical jargon that often makes some readings dense and confusing. Thus, although the tone of the author seemed out of place, it was ultimately helpful in understanding the concepts discussed.

I really liked this article like many of my classmates, even though I did find the writing style a bit distracting at times.  It was really interesting and impressive to see what conclusions can be driven from a small amount of data (although we must be wary).  The concept of using metadata seems like an extension of what we’ve been calling “distant reading”, and I think I actually find it more interesting that close reading.

I was confused at a few points, like what “PRISM” is.  It would have been nice to have an explanation.  But for the most part the author does a great job at keeping a flow of logic and keeping the reader engaged.  I also learned new terms, such as “adjacency matrix”, that will be helpful in the future.  It felt especially relevant because I have been working with matrices this semester in linear algebra.  I wonder if I can use and linear algebra in data analysis?  The data visualization was also intriguing to me.  All in all, I thought this article brought together a lot of concepts from our class in an engaging way!

In Kieran Healy’s “Using Metadata to find Paul Revere”, we are introduced to the Network Analysis. Without any conversation data, but only using metadata such as organizations that each individual belonged to, one can illuminate connections between people and organizations. To take this one step further, one can use these connections and correlations in order to create a conclusion based on two different people or organizations within the dataset. One thing about network analysis is how reliant it is on assumption or bias? In other words would a network analysis of two hundred and sixty people living in the colonial Boston area be of any use without knowing that Paul Revere was a war hero. I would argue that the correlations and conclusions drawn from network analysis are only helpful in relation to prior knowledge or a data point that acts as a point of contention.

That being said, I still believe that this technique is useful however it raises concerns over the accuracy of our predetermined notions of what is wrong and right. If we think about using Network Analysis to fight terrorism, one could easily see how this model would feed off our preconceived notion of what a terrorist looks and acts like and as a result lead to racial profiling on a large scale.

I found it interesting how SJ related Network analysis to our daily lives. I agree with SJ in how often we use social media to provide us with metadata about our piers without ever talking to them. It is interesting to see how we use this metadata to form conclusions about people, categorize them, or befriend them all based upon information on their profiles. The way in which we see new people is changing which is another reason why this type of network analysis is extremely interesting.

From last blog post, I enjoyed reading “A Report Has Come Here Reflection”.  The author captured the essence of the article very well.  More importantly, the author also acknowledged how the methods employed in this paper can help uncover lost voices throughout history.  This is not something I really considered, but I think this is a really good point.

I enjoyed  the Paul Revere reading, despite the author’s writing style.  I felt it was very distracting from the main point of the paper.   I enjoyed the reading mostly because of its simplicity.  The data consisted of just dummy variables.  The analysis consisted of findings links using the data dummy variables.  And from of all of this, the author found that Paul Revere is for some reason at the center of the network.  At this point of the article, I was worried the author was going to speculate why this is.  However, and this is one of the strong points, the author does not give a reason, but simply just states the findings.  I think this reading highlights one of the ways networks can be responsibly used, highlighting something out of the ordinary, but stopping there, and perhaps leaving it up to another field of study to explain the why.

In the article, “Using Metadata to find Paul Revere”, data is used to find suspected individuals involved with terror groups in the 18th century within the Colonies of the United States. To do so, they used a “beta of PRISM has been used to collect and analyze information on more than two hundred and sixty persons (of varying degrees of suspicion) belonging variously to seven different organizations in the Boston area.” From there, various networks and connections were made among people and organizations. This data, at first, was very difficult to condense and use purposely. “Links between people and some other kind of thing, like attendance at various events, or membership in various groups” were used to start this type of analysis and provided the framework for networks. The article notes that there could be some inaccuracies and biases with this data, but a very simple and straightfoward method has the ability to “pick the name of a traitor like Paul Revere from those of two hundred and fifty four other men, using nothing but a list of memberships and a portable calculating engine.”

In CM-A’s article, “Secret Agent Paul”, the author makes comparison with this type of analysis to a FBI movie scene where we look into a blurry security cam. It is extremely fascinating to think that data has the ability to identify criminals by connecting their past activity and involvement among different organizations and individuals.

Even though “Using Metadata to Find Paul Revere” was written in a very distracting, old-timey style, it was still very interesting. Using metadata to find terrorists seems like a wonderful use of technology, but it also brings up many dilemmas in regard to privacy. Do we allow our government to spy on us in the hopes that it makes us a little safer?

Using this data to see what groups each person is a part of creates a giant web that interconnects all the people in the world. It becomes easy to see where and when people are talking, which then leads to knowing what they’re talking about even without being in the room or listening in.

As MLC said in “Social Networks and Paul Revere” social networks are a great way to stay connected in an ever changing world. It also helps track communication and can see what persons of interest are saying about certain things.

Lauren F. Klein’s article discusses issues surrounding the social network analysis of Thomas Jefferson’s letters as president. Klein brings interesting points into account, such as the power relations evident in the relationships among various figures in Jefferson’s letters. For example, the subject most prominent in this article is Jefferson’s chef, James Hemings. Seen in several letters written by Jefferson, Hemings is continually referred to indirectly due to his status as an enslaved African American. Although Jefferson and Hemings shared a uniquely close relationship, from master to slave, Jefferson still referred to him and wrote about him as any other slave on paper. Phrases such as “servant James” were used by Jefferson to write to or about Hemings, despite Hemings’ ability to speak and write in two different languages. It’s intriguing that a search for “James Hemings” in the archives of Jefferson’s letters shows no results. This is because even after Hemings was emancipated, Jefferson referred to him in writing as “former servant James”. Jefferson would even ask to “receive him” or ask for someone to “send for” him.  Through language that we can see with the text mining exercised with the letters that Klein brings into discussion, Jefferson shows a consistent position of power over Hemings regardless of his emancipation and true loyalty as a servant.

As I touched upon in my last response, we have recently been approaching forms of data analysis apprehensively. Although they were in no doubt useful, we were paying close attention to what they may lack -issues in code, inability for computers to replicate/understand human sentiments etc. However, in our more recent readings, the highlight has been more so on the potential of data analysis – in a mostly positive sense. As one of my colleagues said, the Jefferson reading demonstrated how “digging deeper into the absent stories can help piece together many more voices” that were lost in history. The current article demonstrated just how powerful certain forms of analyses can be in extrapolating data. “From the merest sliver of metadata about a single modality of relationship between people…we have gotten a picture of a kind of social network between individuals, a sense of the degree of connection between organizations, and some strong hints of who the key players are in this world.” As I read the article I felt as though I was watching one of those movie scenes where the “FBI” hacks into all the security cameras in the world, pins one blurry image from Grand Central, and magnifies a picture 300x to reveal a crystal clear picture of the suspects face. Whereas those scenes are entertaining given their impressive (and impossible) nature, this data analysis was similar AND possible. Too cool.

When we think of the term ‘social network’ we don’t think about that network expanding back into colonial periods. Social networking is widely used today to communicate with lost acquaintances, and stay updated on trends. In a less modern sense, this social networking scheme can be used to track correspondences and give a detailed analysis of which groups are in communication. In the article, the strength of the relationship between the groups can indicate whether the groups were allies or not. This is useful to track “terrorists” as seen in the article.

The use of the betweenness scores was very interesting because it showed who was in the midst of communications, with Paul Revere having the clear highest score. This article also described the use of eigenvectors to further analyze the matrices, which is a very useful tool when using large dataframes.

As SJ mentioned in their blog, It is cool how we can use data that was not collected by direct communication. Secondhand communication suddenly comes into play in a large way, which introduces a whole new world of data collection.

I liked how the spread of prints was embedded in a historic context. It would have never been possible for printmaking to flourish without the golden age of the Netherlands. While the authors were unable to identify the historic events behind the inward shift experienced by printmakers across Europe, an effort was made to do so. We also learnt that population changes were the largest determinants of changes in printmaking.

While I thought it was a nifty paper, a thought kept resurfacing; so what? Or as a classmate of mine put it, “Is this study even relevant?”. Now we know all this info about printmaking, but how is it going to make anything better, or even affect anything, outside of academic circles. I had a hard last couple of months (years) with this opinion, as this is not an overly helpful view to have in college.