ENGL706 Pre-Class Exercise: Design Experience Timeline

For today’s pre-class exercise, I created a timeline mapping my personal experience with design.

Click here for the interactive timeline!
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ENGL 539 Designer’s Blog: Making the Switch to Python

I officially abandoned Ruby and made the switch to Python. I’ve been Googling around and have found a lot more information about Python in regards to data mining from Twitter, so I think that despite my Ruby technical difficulties, I would have ended up with Python regardless. I made the mistake of approaching the language from a “what will be easier to learn” perspective rather than a “how easy is it to find tutorials/information” perspective. In reality, I’m starting with pretty much nothing, so I don’t think one will be “easier” for me than the other. Python also seems more beginner-friendly—I just noticed that they have a whole wiki for non-programmer beginners on their site.

Since I wasted the first Tutorial Assignment on a language that I’m not using, I decided to start by finding a Python tutorial to make up for it. I came across this one, explicitly discussing how to use Python for Twitter analysis.


I still do not have the basic language, so I plan on spending some time on the wiki offered by Python in order to get a sense of what I need to learn before jumping into the tutorial.

ENGL706 – Annotated Bibliography #3

Rowsell, J. (2014). “The mood is in the shot”: the challenge of moving-image texts to multimodality. Text & Talk, 34(3), 307-324.

In this article, Jennifer Roswell performs a case study of two filmmakers with vastly different techniques to “challenge perceptions of what modes can do and what they can evoke” (pg. 307). In her theoretical framework, she combines Kress’ discussion of ‘“epistemological commitments’ designed by sign-makers to craft image representations” (pg 309), and Merleau-Ponty’s work on “embodiment of the world and the co-existence of space and things” (pg 310). In short, Kress focuses on the semiotics of filmmaking, while Merleau-Ponty argues that meaning is influenced by the history of visuals, from which we cannot sever ourselves. The objects of study are Robin Benger and Tobias Wiegand: Benger is a documentary filmmaker who takes advantage of available modes in order to tell a story; Wiegand produces animated films, in which the story is pre-planned and modes are afforded by the technical process of creation. In both instances, the filmmakers rely heavily on modes to invoke emotion, however their approaches are quite different.

Roswell determines that three themes emerge from her interviews: “the role of the producer as rhetor; how producers materialize ideas, emotions, and values in a text; and modal affordances with their potential to emotionalize scenes in moving-image texts” (pg. 316). Benger views his role as a rhetor as a way to “nudge the world in a better direction” (pg. 316); Wiegand, on the other hand, believes his role is to explore “the ubiquitous use, enjoyment, understanding of immersive worlds” (pg. 317). In terms of how the filmmakers produce emotions in their texts, Benger takes advantage of the optics available to emphasize a mood, while Wiegand develops mood from a more general starting point, creating it through specifically crafted details added in the filmmaking process. Finally, Benger takes advantage of available modes to “create a cinematographica rhythm” (pg. 320), while Wiegand emotionalizes scenes through the process of creating and then combining various modes.

Roswell introduces ”an ontology for moving-image production” (pg. 322). It begins with Merleau-Ponty’s idea of “senses, felt connections, perceived worlds.” These senses are materialized through “modes, affordances, communication, representation” as discussed by Kress. Next, the sign-maker, based on “habitus, interest, audience,” makes decisions that inform their modal choices. Finally, the audience views the text, and meaning is created by a communication between “habitus, interest, felt connections.” Roswell concludes that cases like Benger and Wiegand “call into question [her] efforts at a logic of production” (pg. 323). While there is a basic ontology to filmmaking, there is a “profound, often ephemeral role” (pg 323) in the modal decisions of signmakers and the interpretation of audiences. Therefore, Roswell concludes that she is inconclusive: she wonders if it is even possible to create a grammar for the perceptual.

Roswell’s work is very useful in that it demonstrates the challenges that rhetors face when creating multimodal texts. Each text-creator is faced with a different set of modal affordances, and it is up to him/her to determine the best use of those modes based on the audience. This is relevant to my discussion on response GIFs because in order to understand why a certain GIF was used by an online commenter, we need to understand these factors at play. The commenter has seemingly endless possibilities. A GIF can be created out of the millions of film/videos that have been made, however the GIF-maker is limited to what is available online, and then further limited by the software available. And, for a less technically-inclined commenter without software access/knowledge, he/she is further limited to GIFs that have already been created by others. Even then, there is a further limitation on how much time a commenter has to make and/or browse GIFs, because there is a temporal element to online comment sections, where the most relevant commenters are also the quickest. Considering all of these modal affordances and restrictions is imperative to understanding the rhetorical workings at hand for reaction GIFs. Roswell’s conclusion of inconclusiveness is unsatisfying, but an important limitation to keep in mind when discussing multimodal texts.

ENGL539 Designer Blog: Finding a Language

I’ve decided that my project will be to create a tweet-gathering algorithm, and I’ve spent my time   working with Ruby trying to get it to work. I started out last week at the very basic level of learning the differences between coding languages, and which ones were even capable of being used to gather tweets. Surprisingly (and dishearteningly), even this proved difficult. I’m realizing that I don’t even have the basic language to understand coding. I came across a few questions on forums asking about Ruby versus Python for Twitter, so I narrowed my potential coding languages down to those two. I’m still too much of a novice to really appreciate the differences between them, and there isn’t really a consensus which is “better,” but I came across this chart where Twitter is listed under “Ruby” for usage.

Ruby_vs._Pythonvia http://learn.onemonth.com/ruby-vs-python

On 3/2/15, our First Tutorial assignment was due. I completed a few modules in Lynda.com of a Ruby for Beginners tutorial. It was very easy to understand, and I was feeling good about my choice, but I couldn’t get it to instal on my computer! The tutorial said that Macs come standard with Ruby, but mine didn’t seem to recognize it.

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My terminal is just blank when I try to locate it

I also tried downloading it, but nothing really happened with the installation. My computer does, however, come pre-loaded with Python, so I am leaning towards making the switch, since my heart wasn’t really set on either one anyways.

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I have no idea what that Ruby file is, but it’s not showing up in the terminal!

ENGL706 – Infographic

For today’s pre-class exercise, I created an infographic showing the frequency of non-text media found in the comment sections of different Gawker Media sites. I used a very small sample—three articles per site, each with about 200 comments. The data was calculated manually, and the full data set can be seen below.Untitled Infographic

Data Sets:

Gawker

Report: Seth Rogan Smoked So Much Weed His Office Had to be Renovated

total comments: 217
image: 4 (2%)
GIF: 1 (0.5%)
Link: 2 (1%)
Video: 0
Text: 209 (96%)
Other: 1 (1%)

Worldwide Octopus Uprising Continues With Aquarium Near-Escape

total comments: 188
Text: 156 (83%)
image: 15 (8%)
GIF: 2 (1%)
Link: 5 (2.5%)
Video: 10 (5%)
Other: 0

Police Find Thing You’d Expect in Container Marked “Not Weed”

total comments: 239
Text: 211 (88%)
image: 15 (6%)
GIF: 3 (1%)
Link: 8 (3%)
Video: 2 (1%)
Other: 0

Gawker Totals

total comments: 644
Text: 576 (89%)
image: 34 (5%)
GIF: 6 (1%)
Link: 15 (2%)
Video: 12 (2%)
Other: 1 (0%)

Jezebel

Boston Woman Uses Tinder to Find Someone to Shovel Out Her Car

total comments: 190
Text: 177 (93%)
image: 3 (1.5%)
GIF: 7 (4%)
Link: 3 (1.5%)
Video: 0
Other: 0

Pregnant Women Should Maybe Eat More Tuna, Says FDA

total comments: 200
Text: 176 (88%)
image: 10 (5%)
GIF: 4 (2%)
Link: 7 (3.5%)
Video: 3 (1.5%)
Other: 0

Karreuch Tran Is Pretty Excited to Be Done With New Daddy Chris Brown

total comments: 205
Text: 175 (85%)
image: 12 (6%)
GIF: 9 (4%)
Link: 4 (2%)
Video: 4 (2%)
Other: 1 (0.5%)

Jezebel Totals

total comments: 595
Text: 528 (89%)
image: 25 (4%)
GIF: 20 (3%)
Link: 14 (2%)
Video: 7 (1%)
Other: 1 (0%)

Deadspin

Report: LeSean McCoy “Not Happy” About Going to Buffalo

total comments: 196
Text: 181 (92%)
image: 3 (1.5%)
GIF: 2 (1%)
Link: 6 (3%)
Video: 2 (1%)
Other: 2 (1%)

J.J. Watt Is A Goddamn Lying Clowfraud

total comments: 242
Text: 203 (84%)
image: 34 (14%)
GIF: 1 (0.5%)
Link: 1 (0.5%)
Video: 2 (1%)
Other: 1 (0.5%)

Fight At Avalanche Game Features A Dude Trying to Punch A Lady

total comments: 227
Text: 219 (96.5%)
image: 3 (1%)
GIF: 2 (1%)
Link: 0
Video: 2 (1%)
Other: 1 (0.5%)

Deadspin Totals

total comments: 665
Text: 603 (90.5%)
image: 40 (6%)
GIF: 5 (0.5%)
Link: 7 (1%)
Video: 6 (1%)
Other: 4 (0.5%)

In retrospect my life would have been a lot easier recording all this in Excel tables

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