Course blog

http://www.moma.org/interactives/exhibitions/2012/inventingabstraction/?page=home

This JPEG is the static version of an interactive network diagram made for the website of MoMA’s Inventing Abstraction exhibition that ran from December 2012 to April 2013. It was made by Paul Ingram and Mitali Banerjee, a professor and doctoral candidate, respectively, from Columbia Business School, in collaboration with the curatorial and design team of the exhibition. “Vectors connect individuals whose acquaintance with one another…could be documented,” states the description on the website, i.e., their relationship was explicit. Each node represents an artist whose work was in the exhibition, and they are arranged more of less geographically. The names marked in red have 24 or more connections. The interactive version on the website serves as a navigational device; clicking on a name zooms in on that artist and their network of relationships. Simultaneously, the webpage presents thumbnails of artworks that are in the exhibit and a short biography for the more highly connected artists marked in red.

While this network diagram was not made for research purposes, it still raises issues that face the use of network analysis in the digital humanities. One of the dangers that Scott Weingart discusses in his blogpost “Demystifying Networks” is the reduction of data that is imposed by current limitations in network analysis algorithms. In order to keep the network manageable for software and sparse enough to visually comprehend, all of the possible relationships that can exist between artists are reduced to the vague concept of “acquaintance.” Unfortunately, the website never defines what they mean by that word. Did they meet in person? Did they carry on a correspondence? Did they work together or otherwise exchange artistic ideas? None of these different types of relationships are depicted in this diagram. Furthermore, the website does not explain why 24 connections was chosen as the criteria for an artist being marked in red. What does having at least that many connections signify, if anything? Does it mean that their ideas were more influential? That they were more extroverted? That they traveled more? MoMA have produced a very provocative data visualization, but it would have been more revealing if they had included a little bit more documentation.

That said, this diagram is interesting because it highlights some artists who may not be as well known as others. Three artists are marked in red that I would not have thought were highly connected: Sonia Delaunay-Terk, Natalia Goncharova, and Mikhail Larionov. The significance of these lesser known Russian and women artists is one of the many questions that this network diagram raises.

When most of us hear the term “social network”, we think of Facebook, Instagram, Twitter or any other popular social media applications. In the article, “Demystifying Networks” by Scott Weingart, Weingart gives a much more simple definition of a network. He defines it as “a net-like arrangement of threads, wires, etc”. So Facebook is a network because it connects us to those we communicate with in various ways (pictures, chat, messages and status updates). Social media networks allow you to have a much larger “network” of friends than you would be able to if you only talked to those in your network in person. In a similar article, Social Network Analysis, A Brief Introduction, it is described that social network analysis is “the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities”. This article also provides a helpful visual and example of a “Kite Network”, where two nodes are connected if they regularly talk to each other. If two nodes are not directly connected, but appear on the same network, the groups they are representing could be considered “mutual friends”. They may not know each other, but know someone in common. This is probably how Facebook suggests “People You May Know”. There is probably a network in their data base set up like this and the more mutual connections you have to someone, the more likely they are to be suggested because you are more likely to friend someone you have 50 mutual connections with as opposed to only two. Instagram also recently started suggesting pictures you may like under the explore tab of the application. When you open any of the posts, it will say if it was suggested “based on people you follow” or “popular in your country”. I think this version of the explore tab is much more effective than the previous “popular” tab, which just displayed the most popular pictures on Instagram because the suggested photos are much more personalized and you are more likely to like something that is similar to other photos you’ve liked than just what is popular among everyone. Instagram’s new explore tab and Facebook mutual friends show the importance of social networks and how they are used in social media. Now when I hear the term, “social network”, I will think of the interlocking nodes behind it and not just Facebook in general.

 

 

While reading “Demystifying Networks Part 1 of n: An Introduction,” I noticed that Scott Weingart mentioned Google’s PageRank as an example for a directed edge. I followed along with what he said when explaining that directed edges were relationships in which “…you cannot switch out Node1 for Node2.” Weingart uses authors and books in his demonstration showing that the connection between the first node (an author) and the second node (the book) is that the author writes the book. It would be silly to say that the book writes the author so these terms cannot be switched. How does with relate to Google?

Google’s PageRank uses an algorithm that ranks sites based on how many visits and links there are to the site. In Manipulability of PageRank under Sybil Strategies, Alice Cheng and Eric Friedman use a term, sybil, which can be described as when “a single user creates several fake users […] who are able to link to (or perform false transactions with) each other and the original user” allowing the websites that the real user wants to gain popularity to do so. One of the girls that works at the psychiatrist’s office that I intern in told me that at a previous job she worked in, the company had about four laptops solely for that purpose of increasing the number of times their website was visited.

So the first pages that appear when you search for something on Google are the ones that are most visited and linked to, right? Not exactly. Above is a screenshot I took when entering “Beverly Hills Psychiatrist” and these are the results I got. What I was searching for when I typed in “Beverly Hills Psychiatrist” was actually the website for the psychiatrist I intern for, “drsolution.net”, which appeared right below the results shown above and only appeared on the map when zoomed in. Notice that all seven of the results in the screenshot are partnered with Google leading me to believe that the largest determinant in the algorithm is money. I decided to check how these websites rank globally on “alexa.com”, which ranks all of the websites in the world based on how many visits they get. From the seven results in the screenshot, only two have websites separate from Google so I searched them both on Alexa and compared the results to “drsolution.net” and found that “bowmanmedicalgroup.com” ranked higher than Dr. Solution did but “www.beverlyhillspsychiatry.net” was so low that it did not even have a rank leading me to confirm my suspicions that money is a huge factor in who shows up first in the listings.

Works Cited

 

http://www.cs.duke.edu/nicl/netecon06/papers/ne06-sybil.pdf

http://www.scottbot.net/HIAL/?p=6279

 

Reading about networks I had an interesting thought in regards to the TV show CSI and a concept I had learned of from the show. Six degrees of separation is a concept developed by Frigyes Karinthy in 1929 theorizing that everyone in the world was somehow “related” (by friendship or some association) to everyone else up to the sixth degree. Essentially, a friend of a friend of a brother of a coworker…etc. This concept has been popularized in mass media. For instance, Strangers on a Train, associates to the second degree with the main characters killing the other’s enemy so as to avoid blame. This is where the show CSI brought in the example. I also recall an old teacher telling me how she and her friends played the game “Kevin Bacon” in school. The game uses the concept of six degrees of separation by naming a random movie or something in TV and the players have to relate that film or actress or director back to Kevin Bacon.
Later apparently, Microsoft used billions of messaging systems to confirm this theory. All people are connected up to the sixth degree. I am curious however as to whether or not this system works temporally. can we all be connected going back in time? If so, how many degrees? I am aware that Kindred Britain tests this idea but how closely can all human beings who ever lived be related? For the Paul Revere article, the woman was doing the same thing with her data. Using information she was not necessarily familiar with, she could form a network of relationships thereby determining how closely people were interconnected. It is a strange thought to think that we are all somehow socially related to the Pope or Queen Elizabeth II how important are these relationships? Aside from determining an immediate circle of friends why should we care how an individual is related to another? Everyone loves to learn about their own genealogy but are distant social relationships something to actually care about? Or is it just another boasting war? For past individuals and research I can understand how this would be relevant in determining social environments. The degree to which everyone knows each other is determined by how tight knit the community is. This would affect how news spreads, social views, childhood, and emotional availability of an individual. However, until we cannot interview the individual in person, I see no need for networks to be used.
http://www.theguardian.com/technology/2008/aug/03/internet.email

 

 

original file: https://s-media-cache-ec0.pinimg.com/736x/96/ce/19/96ce193cba270dbad17940fd7c84a235.jpg

This week’s reading, “Humanities Approaches to Graphical Display” by Drucker really struck a chord in me. Coming from a humanities background (I am a Philosophy major also looking to create my own major), taking this class has been a challenge for me as I adapt my arguably more hermeneutical approach to processing information to the framework of the class.

 

This challenge manifested itself also as I was picking a topic for my final project on Subcultures. I was interested in the psychology behind Otaku Culture, which describes an obsessive consumerist culture dominated by manga and anime fans- but how was I to translate this information in data form? I was unsure of how numbers, statistics and figures possibly represent the depth of thought and complexity portrayed in interpretations of Otaku Culture.

 

Reading Drucker, however, attuned me to the idea of approaching information as “capta” rather than “data”. Noting the “etymological roots of the terms data and capta” could, in her view, “make the distinction between constructivist and realist approaches clear”. The idea of “capta” would appeal to the need for a humanistic, rather than robotic approach to handling and classifying information. This, in turn, would acknowledge the “partial, and constitutive character of knowledge production, and recognize that knowledge is constructed, rather than given as a natural representation of pre-existing fact”.

 

However, upon reading reviews of Japanese philosopher Azuma Hiroki’s book Otaku: Japan’s Database Animals, it was interestingly noted that Otaku funnels the works they worship into a kind of character-based slavery, as opposed to the narrative-based freedom that we expect forms of entertainment and escapism to offer us. In other words, Azuma’s approach seems to refute the need for “capta” and interpretive autonomy in light of consumer/ desire driven markets. Just as Drucker acknowledges that “realist approaches depend above all upon an idea that phenomena are observer-independent and can be characterized as data”, Azuma concedes to this by mentioning that “it is only the surface outer layer of otaku culture that is covered in simulacra”, and that underlying all of that is a database or factory for creation. He further argues that scrutiny to this database yields that beneath the “chaotic inundation of simulacra”,  anime and mange character constructions become “ordered” and “understandable”. In the picture above, we see how different female characters in anime and manga may be classified, effectively breaking down the person’s story and reducing it to a set of interchangeable characteristics. What is perhaps more unfortunate is that these stereotypes double up as rules/ guidelines for “simulacra to be successful”.

 

The prospect of anime culture standing behind a veil of originality and at its simplest being no more than a convoluted system of mixing and matching features from a fixed and limited database is heartbreaking- and while I am hesitant to too readily accept his view, I am also excited to read Azuma’s primary text further. I look forward to how his analyses will better inform the outcome of my final project.

Secondary source: http://eyeforaneyepiece.wordpress.com/2013/07/10/notes-on-otaku-japans-database-animals-part-4-moe-elements/

Primary source: Hiroki Azuma, Otaku: Japan’s Database Animals

What I found interesting about this article was the fact that Drucker essentially annihilated our preconceived notions of authority pertaining to knowledge in the form of graphical representations. Our understanding of a certain statistic is based on our ability to relate one set of data to another. She describes our existing paradigm of communicating information in the digital humanities as a process that is based on a fundamentally different set of epistemological assumptions drawn from disciplines that are “at odds with a humanistic method”.

An immediate connection I made when reading this article was the June 2014 Coca Cola advertisement “Happy Cycle”. The happy cycle is a Goldberg contraption designed to deliver cans of coke by getting a user to exercise on the bike. Coca Cola reported that it would take a 140 pound male or female around 23 minutes to burn off the amount of calories in one can of coke on this over-sized bike. Bystanders who who took a turn on the bike were given a can of coke as a reward. However, the problem was that most Americans weigh over 140 pounds, therefore the statistic used by Coca Cola is largely irrelevant as it pertained to a small sector of the population. Statistics from the article collected by the CDC show that an average 20 year old American man weighs approximately 196 pounds, 50 pounds well over what Coca Cola uses as a standard. The misleading assumption of a person being able to burn off a can of coke through a uniform exercise pattern throws any notions of human diversity out the window. It does not take into account individual differences from person to person such as rate of metabolism, gender, fitness, BMI just to name a few. Furthermore the idea of having to do 23 minutes of exercise pedaling on a bike to burn off the sugar in a can of coke, also neglects other health aspects of coke such as cholestrol or blood pressure. (God knows what they put in Coke.) Representation of information is not only about the capta itself, but largely pertains to the medium by which a person digests it through. Their initial exposure to the data largely shapes the way in which they perceive and formulate an opinion on the matter.

http://www.businessinsider.com/coke-happy-cycle-ad-calorie-statistics-2014-6

Even though Drucker’s article was mostly about how we visually represent knowledge it was definitely about how we represent knowledge as data too. Today I looked at Max Fisher’s map of the world’s most and least racially tolerant countries. A few questions came to mind. The main one being how did he turn the data of racial intolerance into capta? How was he able to take something as hard to define as racism and qualify it into a visual representation? The short answer is, he didn’t even collect his data correctly. Racial intolerance is something that changes from group to group and definitely from country to country.

According to Siddhartha Mitter’s article about Fisher’s map “The Cartography of Bullshit“, Iranians were asked about Zoroastrians; Puerto Ricans about Spiritists; Tanzanians, about witchdoctors, etc… Despite the questions being different the answers were presented as the same. All of the answers to the leading questions in Fisher’s survey I assume got put into an algorithm with the output being a country’s intolerance level. But this data is not something that can put on the nice blue to red color axis we see on his map. It would be much better to use a model similar to Drucker’s bar graph for gender data as both gender and race issues do not fit on a simple binary and it’s misleading when they are presented as such in data visualizations. This type of data cannot be measured on one metric.

The main problem I have with Fisher’s map is that even though it would be incredible difficult to visually represent this data he messed up at the initially step of turning the capta into truthful data. I think that the survey might have been designed a certain way or the data might have been manipulated in order to be able to represent that data in a visual format, specifically the map. Unfortunately with the current state of out understanding of data visualization, a strict textual representation might be the only way we can truthfully represent knowledge that ambiguous classification. But forcing data that can’t be easily defined as capta into a visual format Fisher misled and created a problematic info-graphic that people are going to be ready to believe even though that the data itself on a fundamental level is flawed.

 

 

http://designtaxi.com/news/370393/Infographic-The-Life-Of-Bill-Gates-How-He-Started/?interstital_shown=1

http://www.gatesnotes.com/globalpages/bio

http://en.wikipedia.org/wiki/Bill_Gates

 

Humanities Approaches to Graphical Display by Johanna Drucker is a brief about how digital humanists have started to incorporate visualization tools and methods to display information on their work about the social sciences. The paper’s thesis is that “we need a humanities approach to the graphical expression of interpretation” in order to have an accurate representation of data. “Assumptions of knowledge as observer-independent and certain, rather than observer co-dependent and interpretative” are made while visualizing information, and without the process described data will not be understood as “capta” and the wrong message will be interpreted. “Capta is “taken” actively while data is assumed to be a “given” able to be recorded and observed.” Drucker argues that data visualization needs to be reworked to the digital humanists’ process so for the smooth transferring of knowledge.

Infographics are a recent internet fad and a form of visualizing data. Performing exactly what Drucker discusses in her article, most of the time they refer to tall, skinny (to fit Pinterest dimensions) images with eye-catching colors and multiple graphs to stop an internet user in his or her tracks. A lot of backlash from digital humanists is that they are intended to look pretty instead of providing accurate information.

Recently I stumbled upon an infographic that tracks Bill Gates’ life story. A lot of the time when I stop and look at infographics, I immediately assume that their figures are correct and that its sources are reliable. Because of Drucker’s article, I’m going to use this blog post to quickly double check whether the Bill Gates infographic is accurate or not.

Anna Vital cited two sources for her infographic: Walter Isaacson’s book The Innovators and gatesnotes.com. Gatesnotes.com has a timeline of Bill Gates’ life, which aligns with Vital’s infographic. However, Vital’s infographic also includes the not-so-amazing parts of Gates’ history, such as designing a program to schedule students into classes which he used to schedule himself into classes with more girls. How do we know if that information is correct?

A quick Wikipedia search reveals that Bill Gates quoted that story in a press room speech, which was retrieved on July 13, 2013. Since more than 50% of the information is accurate, I’m going to assume the entire infographic to be accurate. Obviously you’d want to check the entire infographic, but most people (including myself) are not willing to go through every detail to make sure what they’re reading is correct. This goes back to Drucker’s point that by approaching graphic visualization from a digital humanities viewpoint is important to make sure the data we portray is accurate for users.

Andrew Smith’s commentary on William Turkel’s work on the Old Bailey project was particularly eye-catching this week to me. Comparing this study with another recent study on speech analysis from the Civil War, Smith humorously points out not only the speech-analysis project’s lack of foresight, but a more broad problem the Digital Humanities faces is it seems to feel increasing pressure to be quantitative and technically in order to substantiate itself. The Civil War speeches, when analyzed, were concluded to be incredibly preoccupied with notions on slavery – a fact that isn’t entirely surprising. On the other hand, Turkel’s analysis and finding of an unusual rise in guilty pleas in British courtrooms complimented the changes in the way criminal trials were structured to proceed and the kinds of punishments and consequences would be executed for the individuals each person on trial depending on their crime and sentence. This compare-and-contrast demonstrating the “right” and “wrong” ways in which the digital humanities structures and approaches questions in the humanities reveals the importance of the question and issue in the first place, and not just the elaborateness of the tool being used to study the same “artifact”.

This notion of a solution being implemented to inefficiently solve a project because of a seeming fixation over “form”, the way in which an artifact is studied, and the “content”, the significance of the artifact itself, reminded me of the popular anecdote/urban legend of the US creation of the space pen. Noticing the difference in atmosphere in space and on Earth affecting astronauts’ ability to write with a regular pen, US manufacturers allegedly poured millions of dollars into developing a pen that could compensate for the pressure differences and then, could be used in environments like space. This pen was then manufactured as the “space pen”, a pen that had a pressured ink cartridge that allowed the individual to write in conditions where gravity is inconsistent with the way it is on Earth. Russian astronauts, on the other hand, solved the issue with a pencil. Just as the research on the Civil War documents should have considered the purpose of their project before embarking on it, the space pen manufacturers should have considered the purpose of theirs – making both instances humorous and didactic. While both projects admittedly used and developed incredibly fancy, remarkable pieces of engineering and technology, these projects are demonstrated as clearly ridiculous because of their inability to consider the needs they were expected to fill, erroneously focusing their efforts elsewhere.

Space Pen: http://www.snopes.com/business/genius/spacepen.asp

Smith on the Old Bailey Project: http://pastspeaks.com/2011/08/21/the-promise-of-digital-humanities/