DIG 210 Group 3 Readers

      The central theme in this week’s reading is the use of data to distill the human condition to a few data points. In Capturing the Criminal Image: From Mug Shot to Surveillance Society, we are presented with the history of collecting crime statistics. Here, Finn argues that the progress in forensic science over the years has helped law enforcement identification, but emphasizes that the mold of identification could manipulate how the country views law enforcement at the local, state, and national levels. Though Finn does not believe that we have reached the point of no return. He concludes that the advances in crime-based data representation are a necessary evolution from the limited criminal investigation tactics utilized in the 19th and 20th centuries. At the same time, Finn stresses that these advances need to be checked by academics, professionals, and common citizens to inspire trust in law enforcement officials and new technology. If this data collection continues unchecked, a sense of mistrust might develop between the people and law enforcement agencies.

 

   In the Lyon reading, we see how societal anxieties against the super-panopticon, the ability to asymmetrically survey large numbers of people (essentially the entire populace), in novels like George Orwell’s 1984, and The Handmaid’s Tale. If you can think of a dystopian novel, it likely resembles Foucault’s version of a super-panopticon. In these dystopian societies, the powerful creates an environment where a person believes they are constantly surveilled, and therefore must comply with the hegemony in every decision. To us, this anxiety is quite obvious: we are not perfect beings, and therefore we are bound to make a mistake that will put us in the cross hairs of the powerful. Like our fictional counterparts, do we feel the cold stare of the government on our necks at all moments? Or do we truly feel free to do whatever we deem right?

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Social Network Analysis workshop

Social Network Analysis workshop
Thursday, Sept 28, 1:40–4:30pm
Wall 210

Network visualization with R

Katherine Ognyanova, Rutgers University Web: http://www.kateto.net
E-mail: katya@ognyanova.net
Twitter: @ognyanova

Network science is a rapidly growing multidisciplinary area that allows us to understand the dynamics of interconnected systems: social, digital, physical, biological, economic, and others. Visualizing network structure provides a key avenue for data exploration and presentation.

This workshop will cover network visualization using the R language for statistical computing (cran.r- project.org) and RStudio (rstudio.com). Participants should have some prior knowledge of R and network concepts. The session will provide a brief overview of network formats, focusing on their structure and representation in key R packages. Attendees will also receive an introduction to major principles of graphics used in the R environment.

The workshop will provide a step-by-step guide describing (through series of examples) the path from raw data to graph visualization in the igraph and Statnet frameworks. The advanced portion of the workshop will touch on dynamic visualization for longitudinal networks and combining networks with geographic maps. We will also discuss ways of converting graphs in R to interactive JavaScript/3D-based visualizations for the Web.

Instructions for participants:

The following steps will help you prepare for the workshop (though you can also complete them when we meet if you have not had the chance to do so in advance).

Please download and install the latest version of R (cran.r-project.org) and RStudio (rstudio.com).

We will use several key R packages that you can install by typing: install.packages(“igraph”)
install.packages(“network”)
install.packages(“sna”)

install.packages(“ndtv”) install.packages(“threejs”) install.packages(“visNetwork”)

Optional packages that are not mission critical for the workshop but will allow you to do more advanced work can be installed by typing:

install.packages(“RColorBrewer”) install.packages(“extrafont”) install.packages(“ggplot2”) install.packages(“png”) install.packages(“animation”) install.packages(“maps”) install.packages(“geosphere”)

At the end of the workshop, participants will have some time to do hands-on work visualizing their own data. If you do not have a network in mind, you can work on one that will be provided by the instructor.

If you have questions about the workshop or network visualization in general, feel free to contact me at katya@ognyanova.net or on Twitter at @ognyanova.

DIG 210 – Responders – Group 5 – Week 5

In class on Tuesday, we examined the importance of raw data in our day and age. A few students expressed that they regarded data as rather useless in making accurate predictions about a user’s psychological profile or behavior, whereas others were convinced that data has highly predictive potential itself, but its usefulness depends almost entirely on its interpretation in an appropriate context. In other words, data about the hair color of an individual student at Davidson College has no standalone relevance, but somebody intends to dye his or her hair in the most popular color in order to fit in more seamless way, such specific data becomes important in this particular situation.  In other instances, the data provided was not recent or relevant, and seemed to skew the interpretation.  While these insights are useful, they are only as accurate as the data provided to them.

Aside from this rather silly and purely illustrative example, data enables us humans to answer the most complex questions about our surroundings if and when we ask the contextually appropriate questions. Reciprocally, the underlying power of data analytics and statistics empowers those in possession of data. Our class discussion focused on this aspect for the majority of the time due to its contemporary relevance considering the recent Equifax data breach and eye-opening NSA scandal in 2013. It appeared to be rather easy for most students to point out the looming threats ranging from mass surveillance to data-adjusted insurance premiums. However, we are curious why the perceived dangers of big data analytics seem to far outweigh the perceived possible benefits. Hence, the question remains whether our public perception of data has been skewed by Western pop culture (e.g., “1984” by George Orwell, “The Circle” by Dave Eggers and countless futuristic Sci-Fi movies) or if it really poses a threat to our societal and democratic well-being. It is a question that requires immediate attention because only effective data privacy policies will ultimately protect us when everything has been converted into ones and zeros.”

Data Culture- Responders, group 1

I like the analogy that Group 4 used in their post to explain cookies.  I have seen the word “cookies” and have been given the option to disable or enable cookies on my computer before but never knew what they were.  What stuck out to me even more in the Do Not Track mini series was the insane number of third parties that monitor your viewing history.  There were 63 third-party trackers for the Wall Street Journal!

 

In addition, I agree with Group 4’s analysis of the quote from Dataveillance and Counterveillance.  It may seem like cookies are helpful and allow you to shop and Google search more easily; however, I would feel a lot more comfortable without these cookies and with more privacy.  A quote that I found interesting from this reading is when Google said that they were “recognizing your browser, not you.”  But when Google is using 57 different signals to personalize each person’s search, how different are you from your browser?

 

Group 4’s final question posed, “if it is just for the government to arrest an undocumented immigrant based on social media insights, especially conversations that cookies have recorded” is a very interesting and thought-provoking one. On the one hand, undocumented immigrants have committed a crime, so it is reasonable for the government to use any information available to them to pursue an arrest. However, if the government does decide to use such information, one must seriously question the boundaries to the government’s access to data.

DIG 245 Week 5: Group 2 Reviewers

PBS Frontline investigates the following question: What are companies doing to our kids through the technology and are kids aware of any of this?

Through a series of interviews by various actors in the social media and advertising industries, PBS discovered a reversal of previous advertising trends. Companies were chasing the kids when MTV was the go-to entertainment center, but now kids are posting themselves on the social media. Indeed, when you like a product online, it becomes a part of your identity. Companies can track likes and posts and every move and turn this data takes. They constantly analyze this information to maximize revenue. In effect, kids are turning into walking billboards. Companies help or bait young people with achieving “fame” in exchange for supporting their brand. This raises important questions: Are such relationships ethical, or perhaps inevitable?

For more than any generation before, current young people have the ability to directly contact celebrities. This has multiple effects. It both encourages kids to be more active on social media platforms as well as provide them with empowerment–they can express themselves with very little effort.

On the marketing side of things. Youtube collaborations allows creators and users to widen their reach. PBS argues that both the content creators as well as the companies that run platforms such as Youtube or Instagram are benefiting from their relationship. These online celebrities are able to gain fans, gifts and money for sponsoring or endorsing companies while the platforms showing their content receive money from advertisements brought in by the viewers. These “media in

fluencers” are very powerful, indirectly selling products for companies because they are viewed as more relatable towards their viewers. The generation growing up in this culture are not just victims but also capable of taking advantage of it. The 22 year old with the tech company, Kiip, advertises to people for completing tasks. The middle school girl who interacts with her audience and even offers rewards for people who subscribe, like putting their name on her wall, serves as another example.

Perhaps the most interesting part of this story the documentary highlighted was the fact that “selling out” appears to no longer be relevant.  Now people aspire to become famous enough that companies want to use their brand. Does this mean kids now less ethical than they were before? Is the internet as a whole the cause of this or simply current social media? If fame itself is the end for these students, then it seems rather shallow. Whatever the case, the line between “internet fame” and real “fame” is becoming blurry.  Internet sensations are often featured on the show Ellen.  And the number one youtube personality, Pewdiepie, was recently on the Late Show with Stephen Colbert.

DIG 245 – Week 4 – Group 5 – Observers

Patrick – Computers brought to DIG 210 class on 9/14

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I was curious to see the distribution of computers that students were using during class.  Personally, I am a Mac user, who has developed a preference for operating in Windows while programming or using Microsoft Excel.  Though I have no basis of comparison, I was curious to see if students in a class focused on Data had a stronger preference towards non Mac computers.  

My results were found simply by counting the number of computers of each brand that students had during class on 9/14.  14 of the 20 students attending class brought Mac computers with them, while there was one student with each of the following brands: Asus, Dell, Toshiba.  The remaining 3 students did not bring computers to class.  70% of the students in class had Mac computers, which is an overwhelming majority.  Even in a data focused class, Mac appears to be the most popular choice.

Seth – What can the clothes we wear tell us about the weather outside?

I recorded what each student in class on Thursday (9/14) was wearing. Either shorts, pants or a dress and either a short sleeve shirt or a long sleeve shirt/jacket.

This past week I decided to observe how people decided to dress as the weather begins to turn to lower fall temperatures. On (9/14) during our class it was approximately 70° F and one of the first cooler days of the season. I was curious if people would start to wear warmer clothes and what percentage of people would wear pants and jackets vs. shorts and t-shirts. Furthermore, in this period of time when the weather cools off from the much hotter months I was curious of the probability if someone wore shorts, would they wear a short sleeve shirt or a long sleeve shirt etc.

I found that 65% of the class wore shorts, 25% wore pants, and 10% wore a dress. I also discovered that 50% of the class wore both short sleeves and shorts, while 20% wore long sleeves and pants. These were the two highest combinations, however, some people also mixed what they were wearing (shorts and long sleeve, pants and short sleeve etc.) Also I found that if someone wore shorts, the probability they would wear a short sleeve shirt, was 77%. And if they wore pants there was a 20% chance that they would wear short sleeves. The findings from this experiment coincide with my hypothesis that warmer clothes will appear with the cooler outside temperature. It was also interesting to see that people either wore both pants and long sleeves or shorts and short sleeves, which could definitely coincide with fashion and other factors.

I think it would also be interesting if I made this observation every class period and graphically showed the change in clothes worn to class as well as the outdoor temperature side by side. This would obviously take more than one week but nonetheless could be an interesting representation of average temperature over time and its effect on the clothes we wear.

Charlotte – DIG 210 use of filler words in class discussion, by gender

4_CW data week 4

In my study, I focused on the use of “filler words”, such as ‘like’, ‘um’, ‘uh’, ‘er’, ‘okay’, ‘right’, ‘you know’, and so on, during Tuesday’s class discussion of the week’s reading. I was interested in observing the use of these words as we never notice our own, personal use of them, but we do tend to become aware of their repetition in our language once somebody tells us to look out for them.

In terms of methodology, I split my observations up into male and female groups. I was interested in seeing if one gender used more filler words than the other. From my observations, women used 3.14 filler words per comment in class discussion and men used 3.26 filler words.  However, our DIG 210 class is not split equally between genders. Tuesday’s class consisted of 14 men (n = 14) and five women (n = 5). Therefore, it would be unwise to find a conclusion is my data as the subject, or sample size (n), groups were not the same and a fair comparison cannot be drawn.

I think this topic would be very interesting to extrapolate upon in multiple classes across Davidson departments. In particular, I wonder if filler words are more common in specific departments and whether men or women speak more frequently in class discussions at Davidson.

Daniel – DIG 210 Student Hair Color

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In our study regarding the DIG 210 students’ hair colors, we found that 64% of students had either brown (n = 6) or blonde hair (n = 6), while students with red hair represented a clear minority with only 10% of the class. As a result, our class differs from other sample groups such as the one that was recently the research subject at Rice University (2016), which showcased an overwhelming majority of 48% (n = 286) as brown-haired while only 21% (n= 127) had blonde hair.

How can it be the case that these two data sets of US student classrooms allow for completely different inferences? On a qualitative level, a variety of reasons ranging from geography to social demographics could be responsible for the differences in probabilities. However, the real statistical illegitimacy that is embedded in these data sets originates from the limited number of observations that prevents the Law of Large Numbers to eliminate selection bias to a sufficient extent. With so few observations, the inferences that can be made about the hair color demographics of Davidson College’s general student body will be inherently flawed.

 

Data Culture — Responders Group 4

In the Do Not Track mini-documentary series, Brett Gaylor, explains how cookies record our online behavior and every time we log into a site, click on something, or browse, the cookies will ultimately be able to build more complex insights into who we are, what we like, and what we may want to see or do next.  If you are having a hard time understanding cookies, think about it as the black parasite in Spider Man 3, that latches on to Peter Parker and fashions a black Spiderman suit.  The more Peter wears the black suit, the more the black parasite can adapt and understand its host, and thus ultimately exploit Peter.  

In Dataveillance and Countervailance, Rita Raley explains, “there are basic steps one can take to delete cookies, but it seems unnecessary to do so because they do not interfere with everyday computer use; in fact, some of them are functionally necessary and the end result is that one encounters advertisements that may be of interest.” So despite cookies’ ability to spy and record, many do not care and are not paranoid. Although our group has come to the consensus that this offers conveniences when it comes to online shopping and finding the best discount packages, there are some major concerns to this level of surveillance sophistication.   

In the same reading, Raley writes, “For every system of disciplinary power, as Anthony Giddens puts it, there is a “countervailing” response from those in precarious, subordinate, or marginal positions, which is to say that dataveillance and countervailance must be seen as inextricably connected” (131).  As of right now, the Trump administration is placing a significant emphasis on deporting undocumented immigrants, and emotions have been heightened with the repeal of Obama’s DACA act.  Ethics and opinions aside, one question we would like to bring up is if it is just for the government to arrest an undocumented immigrant based on social media insights, especially conversations that cookies have recorded.  

DIG245 Group 4 Week 5 Readers

This week’s readings all focused on the principles and philosophies of web design. The readings discussed responsive websites, grid based web designs, and Swiss style graphic design, each of which is important to consider when designing a website, for their own reasons.

 

The responsiveness of a website determines how the site’s layout changes in accordance to the size of the screen that the user is viewing the site on. This relates not only to desktop vs mobile sites, but also to different sized screens in general. It’s impractical to design a website with only one screen size in mind, so we need to make sure our websites are responsive to create a pleasant viewing experience for the reader. The article specifically touches on 9 principles of responsive web design that need to be followed to create a properly responsive website.

 

The Swiss design article discussed exactly that: Swiss style designs. As the article outlined, the Swiss style design can be defined as the chase for simplicity within a design. As this relates to web design, that definition turns into creating a user-friendly interface. The philosophy of this style is basically the idea that less is more. Common themes of this kind of design are clever use of whitespace, the use of different font sizes to make readers notice about the text hierarchy and important lines, and heavy use of uniform geometric figures to create a design that is easy and pleasant to use.

 

Finally, the article discussing grid-based web designs focuses on the process of designing a website. According to this article, one should first plan the basics of their website via separating the site into different grids that all hold certain information. From there, the designer should be able to test their layout by actually beginning to create the website. This phase involves thorough testing. That doesn’t mean test one design and, if it works, forget about it forever. You should be testing different ways of laying out information and formatting until you find something that solves your design problem. One interesting philosophy behind the grid-based design is that, as the article points out, it allows the designer to “spend less time on the basics and think more about more advanced principles, such as paragraph and sentence rhythm, division of space and other typographical principles.” One interesting point about this philosophy is that it focuses on dealing the simple aspects of web design (basic information organization) to give the designer more space to focus on smaller details that bring the website together. This contrasts interestingly with last week’s readings about technology doing too much for us. One question we considered is whether the grid is too helpful in this respect. Does the grid layout make designers too lazy about fundamental aspects of their websites’ design? Is the ubiquitous use of the grid-based layout clouding designers’ ability to come up with new ways to design their websites?