ENGL685 – Final Paper

Below link should give you the PDF:

The New Celebrity: How Funny Novelty Accounts Gain Social Influence on Twitter

Abstract: Twitter, the second most popular social networking site in America, is host to a wide variety of users. The site design allows for more room for identity play, giving way to a wide variety of accounts that range from authentic representations of real life to completely fictitious. Novelty accounts fall on the fictitious spectrum, as personas developed for the purpose of tweeting comedic material. This has led to the rise of a new type of celebrity—one where a fictitious persona can be more popular than real life celebrities. I perform a case study examining the types of humor used by three accounts representative of a genre of female, white novelty personas—@girlposts, @WomensHumor, and @TweetLikeAGirl. The type of humor used by the accounts can help contribute to understanding how this new type of celebrity functions.

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ENGL706: Final Project

Postmodern Identity in the New Social News:

A case study of GIF discourse in Gawker Media

Introduction

News is not consumed in a vacuum, and that is especially true in the age of digital media. Bird (2011) suggests that “the ‘stories’ of news emerge as much through interpersonal communication as from the specific texts” (p. 495). What was once discussed around water coolers now has a space in comment sections at the bottom of online articles. While textual discourse remains constant, the secondary mode of news discussion is increasingly shifting from verbal in person to visual online.

Gawker Media is a representative example of news discussion that takes place by means of multimedia. While many traditional news outlets such as Washington Post and New York Times limit the comment sections to text-only responses, Gawker, which was founded as an online-only news organization, supports hypertext, images, embedded videos, and GIFs.

The subject of this paper will be a consideration of how GIFs are used in online discourse. A GIF (Graphics Interchange Format) is a type of image format that supports both static and animated images. When I discuss GIFs in this paper, I mean the animated format; this is how they are colloquially understood to by the average online user. GIFs are unique in that, in terms of multimodality, they are a halfway point between static image and video—they provide a more energetic, robust visual than a static image, but lack a video’s mode of sound. They are used in many contexts online—when the site format allows, GIFs can be found across social media, discussion boards, blog posts, and, increasingly, as content for news articles.

This paper will be a case study of GIFs posted by readers in response to Gawker Media articles. I argue that commenters use GIFs as a means to craft temporary identities in response to the postmodern, anonymous nature of online news discourse. Kress and van Leeuwen’s (1996) theoretical framework of social semiotics and modality will help to shed light on the visual nature of GIFs and their semiotic function in the specific rhetorical context of Gawker’s article comment sections. I seek to answer the question: in which instances are GIFs used in favor of other available textual and multimedia formats in the context of the news comment section?

First, I will discuss the historical context of GIFs and the current context of the Gawker website. This is to situate the rhetorical context of GIFs, as well as to justify my methods of data collection. Next, I will discuss postmodern identity performance in reference to crafting a persona for online discussion. Then, I will apply social semiotics and modal affordances to identity performance online. Finally, I will conclude by pointing to future research possibilities.

Contexts and Methods

The GIF, developed in 1987, has been around for about as long as the internet (Ram, 2012). The format’s origins can help situate its current reputation as a humorous means of communication. As Buck (2012) points out, the GIF has always had “an inherent element of fun.” It was developed as a moving image format that could be loaded on the slow modems of early internet connections, and gained widespread popularity with silly images such as the pixilated dancing banana and the dancing baby. The recently-fashionable genre of “reaction GIFs” was popularized in 2012 by a tumblr blog entitled “#WhatShouldWeCallMe,” in which the authors posted GIFs with textual descriptions creating fictional scenarios (Buck, 2012). This was the precursor for the current trend of posting GIFs in reaction to Gawker articles. While there is no universally acceptable way to use GIFs, this paper will attempt to use examples from Gawker to identify general trends.

The rhetorical context of Gawker as an online-only news format developed in the age of social media is relevant to understanding identity performance via GIF, so an overview of Gawker’s functionality is necessary. The difference in site design between news companies that originated online versus established news providers that transferred online from print warrants its own robust rhetorical visual analysis; however, the purpose of this paper will be to focus on Gawker in reference to its user-generated media. While Gawker does not shy away from serious subject matters, the tone of their content is much more lighthearted than that of articles produced by news organizations with a more traditional, serious ethos. The navigation of the Gawker site is set up similarly to a social media site; whereas a traditional news site has archives, Gawker only has chronological posts. The effect of this gives Gawker a similar feel to the timeline layout on social media platforms such as Facebook or Twitter—a post is only so visible as it is recent. As time passes and more posts are created, older posts fade to obscurity. This temporal layout that discourages re-discovering old articles speaks to the value of timeliness, while an archival system would be appropriate for an organization that values timelessness. A more explicit example of Gawker’s prioritizing of current news is the left column of the site—secondary to the main story feed—where there are two separate “trending” feeds that update with the most popular stories at the moment. I suggest that the news content on Gawker combined with the timeline layout makes for a new hybrid news-social media platform.

The social media context is essential to the use of GIFs. While the news content provides a platform for discussion, the social media-esque site design encourages a playful tone of discussion, which then leaves room for the historically silly GIF format to be used. The discussion, however, is not always civil. Gawker’s allowance of multimedia in comment sections became notorious when Jezebel, a feminist-centered branch of Gawker Media, received an influx of violent, sexually-explicit GIFs (Coen, 2014). The current comment system is a result of Gawker’s response—comments are heavily moderated, and only appear automatically at the bottom of the article once approved by a site administrator (Jezebel Staff, 2014). Readers have the option of viewing unmoderated comments, but they do so at their own risk.

The site layout of Gawker and the history of the site’s comment system informed my methods of data collection. I collected 13 GIFs total over a period of one day, April 2, 2015. April 2nd was somewhat random and somewhat strategic—it was a typical news day, with no major holidays or world events that would have affected the regular news flow. Because Gawker does not have an archival feature, I was not able to view a comprehensive list of the articles published on one day. I collected data from all articles posted to Gawker’s Twitter on April 2nd: it was easier to navigate Gawker’s Twitter timeline, which loads a new page automatically as the user scrolls, rather than the Gawker.com site, which only displays a few articles at a time and requires the user to click through each page to go back. I collected 26 articles total, with 10 containing comments with GIF responses. For the sake of maintaining a consistent rhetorical context, and to limit my data sample to a manageable amount, I only collected GIFs that were posted in direct response to the article, not in response to another commenter.

Identity

Irving Goffman (1956) characterizes identity as a performance. He says that “when an individual plays a part he implicitly requests his observers to take seriously the impression that is fostered before them” (p. 10). Clevenger (2003) notes the appropriateness of applying Goffman’s sociological theories, which were developed for physical interpersonal interactions, to “computer-mediated exchanges” (p. 12). She says that this highlights the online environment as “an alternate venue for examining” interpersonal encounters (p. 13), rather than the death of human contact. This falls in line with Bird’s (2011) observations on the migration of the social function of news from in person to online.

With identity performance in the context of online discourse in mind, Abrudan’s (2011) discussion of postmodern identity in a world “strongly mediated by the images though mass-communication means” (p. 23) leads us to the role of GIFs in Gawker’s comment section. Gawker’s commenter identities are anonymous—the screen name of the user is almost never tied back to the real-life identity of the user. Individual user profiles, while available, are not a central or widely-used feature of the site. Therefore, a user is reinventing his/her identity performance with every comment on a new article. Abrudan (2011) notes that “in a more and more complex world, excessively fragmented and with an accelerated rhythm of renewal, identity becomes more fragile, it loses stability, always being subjected to changes, adjustments” (p. 25). Because identity can change so rapidly, people rely on mass media for representation of self, as “familiar, easily recognizable narratives, topics, and characters” (Abrudan, 2011, p. 25) are easily understood by a wide audience, with little effort on the performer’s part. GIFs are rarely original content; in most cases, they are clips from mass media, stripped of their larger context, and remixed into a silent story of mere seconds.

I observed several types of ways that GIFs were used to perform identity by commenters on Gawker: as a hyperbolic projection of the commenter’s own reaction, as a playful imitation of another’s reaction, and as an insider to the Gawker community. The article “Furious 7’s Paul Walker Tribute Made Everyone (Not Me) Cry (Okay Me)” (Evans, 2015) contains instances of all three types of responses. Manitoidian responded with a projection of his/her own reaction to the sad reminder of a popular actor’s death:Furious_7_2_commentFurious_7_2

The user appropriated a moment from a popular Youtube video to reflect his/her sadness. It is clearly self-referential; the image itself contains a first person singular statement, and Manitoidian anchors the image with another first person singular line. Had the GIF been posted without text, it would still be understood as self-referential by virtue of the fact that when a person replies to an article, they are doing so in reaction to the article. Without text indicating otherwise, it can be inferred that a GIF is standing in place of what would have otherwise been a verbal or textual response.

Burlivesleftnut’s response is an example of a playful imitation of another person’s reaction; in his/her comment, he/she explicitly attributes the reaction to other people in the theater.

Furious_7_1_comment

Furious_7_1

Here, Burlivesleftnut takes on two identities: she/he uses the GIF to imitate the audience’s reaction, and describes his/her own reaction in the text accompanying the GIF. He/she seperately says that they “cried,” textually indicating their own reaction as separate from the GIF.

Finally, DickRick performs the identity of a community insider, responding with a GIF referencing a joke from a popular sitcom, Seinfeld:

Furious_7_3_commentFurious_7_3

The user’s comment is vague, creating a sense of an inside joke, where only those who “belong” will understand. He/she is posting the GIF with the expectation that those in the Gawker community are part of his/her community, with a shared cultural knowledge.

While the GIFs assist in performing the users’ identities in different ways, there are some important similarities to note. First, the subjects of the GIFs are all humans, popular culture icons, and, more specifically, white males. In the world of postmodern identity crafting described by Abrudan (2011), users need easily recognizable identities. A human face (the GIF subjects) is the easiest way to represent a human face (the commenter). Popular culture icons create instant recognition, and viewers can immediately grasp on to any cultural undercurrents associated with the icons. The second image, provided by Burlivesleftnut, shows an old man crying. The scene is from a popular movie franchise, The Hunger Games. To an American audience, it is generally understood that the subject is a famous actor, in the middle of performing as a villain, so we are comfortable with seeing him cry, and can appreciate Burlivesleftnut’s joke. If the subject was an unknown old man crying, however, the audience would be distracted, likely even offended, that Burlivesleftnut would exploit an old man’s sadness as a joke.

A final point to note is that is that the subjects from all three GIFs are white men. I will suggest two possible reasons for the homogeneity, being especially cautious to avoid generalizing such a small sample size. One explanation falls in line with the previous two characteristics: a white male is an easy identity to slip into. Privileged as the “norm” in society, a white male would not cause other readers to think twice. A culturally underrepresented subject, however, would be more likely to make readers pause, taking a moment to contemplate if there is a deeper meaning. Because black women, for example, are not considered the default cultural norm in popular culture, users may wonder if there is subtext to the commenter’s post, and become distracted from the joke. The second explanation is that because white men dominate popular culture, there are less GIFs readily available featuring other races and genders.

The second major similarity between the identities that all three GIFs perform is that there is an element of humor. The first GIF references a silly YouTube video, and even if that was not known, the text within the GIF is a joke euphemizing crying. The second GIF is an overreaction; the subject is openly sobbing, while the subject of the Gawker article is a pop culture piece (an admittedly sad piece, however it would be unrealistic to react by sobbing in real life). Not only does the last GIF reference a funny scene in a popular TV show, but it is a seemingly incongruous connection, until DickRick makes the connection between Junior Mints, the main topic of the skit, and movie theaters, the main topic of the article.

Social Semiotics

As demonstrated by the discussion in the previous section, GIFs are highly contextual, and their meaning is contingent on both the context of the article and the larger cultural associations with the visual of the GIF itself. As Kress and van Leeuwen (1996) note, meaning-making within a culture operates under constraints. They say that “as mature members of a culture we have available the culturally produced semiotic resources of our societies,” and that out of the interest of creating meaning in the face of contingencies, sign-makers are producing signs “always as transformations of existing semiotic materials, therefore always in some way newly made” (pg. 12). However, despite the seemingly limitless ways of making meaning, signmakers are limited by convention, which is a “constantly present constraint on how far one might move in combining signifiers with signifieds” (pg. 12).

GIFs as performances of identity function under the two principles of social semiotics put forth by Kress and van Leeuwen (1996): “communication requires that participants make their messages maximally understandable in a particular context” (p. 13) and “representation requires that sign-makers choose forms for the expression of what they have in mind, forms which they see as most apt and plausible in the given context” (p. 13). Images co-opted from popular culture satisfy the requirements for “maximally understandable in a particular context” and “apt and plausible” because, as previously discussed in reference to Abrudan (2011), postmodern online identity is fleeting, and must be constructed quickly, so culturally significant people are often the subjects of Gawker’s GIFs.

For example, in response to the article “Screaming Kids, Feckless President Ruin Innocent Bee’s White House Visit” (Hongo, 2015), which discussed a reading by President Obama that was disturbed by bees, commenter Syphilitic Scalia Says responds with a scene from the show Arrested Development:

Bees_2_comment Bees_2

The context of the Gawker article is a silly story about bees. Syphilitic Scalia Says responds, creating a new semiotic meaning with an even more silly scene about bees. This is an example of the user performing an “insider” identity, responding with a cultural reference that is only funny to those who have seen the show. We can infer that Syphilitic Scalia Says posted under the expectation that other Gawker readers are familiar with Arrested Development, thus satisfying the “maximally understandable” principle of semiotics.

The multimodal nature of a GIF contributes to its meaning-making function. As discussed in the overview of Gawker’s site, the comment section of articles allows for text, hypermedia, static images, and videos, along with GIFs. All media objects are afforded the mode of the screen. Hypermedia is afforded the mode of interactivity, allowing for users to click a link and travel to another website. Static images are afforded the visual mode. GIFs operate with motion and visuals. Videos have the most modal affordances, with the interactivity of hypermedia, the visuals of static images, the motion of GIFs, and the added mode of sound.

As Kress (2004) states, text limits readers to a point of entry. The benefit of digital media, he says,  is that “the reader’s interest determines where he or she wishes to enter the page” (p. 114). His text point-of-entry argument holds true for hypermedia and video as well. The user has little control over the destination of a hyperlink, and videos operate temporally. Static images and GIFs, however, have multiple “points of entry.” While there is a temporal element to GIFs, the duration of the movement is short and repetitive, so that it is still accessible. The moving image is a short, precisely selected clip, stripped of sound. The movement is not implied, such as in a static image, but explicit.  Comment sections are meant to be skimmed, and GIFs do not interrupt the flow in the same way that a video would.

To return to GIFs as semiotic means for identity performance, the accessible point of entry and the explicit motion make it ideal for crafting a reaction. While static image may still be favored by many Gawker posters for its simplicity, in many cases a GIF is the more “apt” form to express what a sign-maker has in mind (Kress and van Leeuwen, 2006, pg. 33). For example, in response to the article “Rest in Peace, World’s Oldest Person” (Evans, 2015), about the death of the oldest person in the world, the below commenter responded with the following:

RIP_commentRIP

This is an instance of a GIF functioning as a playful identity imitation. The user, in his/her choice of an elderly person as the GIF subject, is referring back to the subject of the article. The temporal nature of the GIF is what contributes to the meaning of the GIF, and thus  the semiotic nature of the user’s identity play. The GIF shows the woman falling of her own accord, and irrationally blaming President Obama as the punchline. It characterizes her as ridiculous. Below are still frames of the GIF.

Screen Shot 2015-05-05 at 6.14.05 AMScreen Shot 2015-05-05 at 6.13.54 AM

Each frame does not adequately tell the story contained within the GIF. The first frame shows the woman lying on the ground with the text “Thanks, Obama.” Without the initial frames of the woman falling, the audience could erroneously believe that the woman is a victim of Obama. The second frame shows the woman in the process of falling, without the “Thanks, Obama” punchline. In this frame, the humor of the GIF is lost; she is rendered as a helpless, elderly woman who is suffering. It is the motion, and the unfolding story of the GIF that ultimately conveys the full meaning.

Conclusion

In the era of postmodern identity, where mass media is widely available for consumption, manipulation, and reproduction, GIFs have risen to popularity as a means of online discourse. This paper has examined GIFs that function as a performance of the commenter’s own identity, as a playful imitation of another’s identity, and as a way for a commenter to establish his/her identity as an insider to the community. This list of functions is by no means exhaustive; Gawker Media is only one context where GIFs are used as means of a semiotic identity performance. However, as a popular hybrid social media and news site, it is a representative example. GIFs multimodal affordances coupled with their multiple points of entry to the text allow users a more detailed meaning-making tool without the tradeoff of an unwieldy format.

Future research should focus on the function of GIFs in other platforms. Where there is human interaction, there is identity performance; a comparison of how GIFs are used to perform  in different contexts would be useful to gaining insight to how people view themselves and each other in public discourse. As the GIF’s popularity continues to grow, I expect to see it on more GIF-friendly platforms in the future.

Resources

Abrudan, E. (2011). The dynamics of postmodern identity. Journal of Media Research-Revista de Studii Media, (1 (9), 21-30.

Bird, S. E. (2011). Seeking the audience for news: Response, news talk, and everyday practices. The handbook of media audiences, 489-508.

Buck, S. (2012, October 19). “The History of Gif.” Mashable. Retrieved from http://mashable.com/2012/10/19/animated-gif-history/

Clevenger, R.J. (2003). “Writing cyborgs: An ethnography of online composition” (Doctoral Dissertation). Dissertation Archive. Retrieved from http://aquila.usm.edu/theses_dissertations/1890. (Paper 1890).

Coen, J. (2014, August 13). “What Gawker Media is Doing About Our Rape Gif Problem.” Jezebel. Retrieved from http://jezebel.com/what-gawker-media-is-doing-about-our-rape-gif-problem-1620742504

Evans, D. (2015, April 6). “Rest In Peace, World’s Oldest Person.” Gawker. Retrieved from http://gawker.com/rest-in-peace-worlds-oldest-person-1696048799

Goffman, E. (1956). The Presentation of Self in Everyday Life. Edinburgh, Scotland: University of Edinburgh.

Hongo, H. (2015, April 6). “Screaming Kids, Feckless President Ruin Innocent Bee’s White House Visit.” Gawker. Retrieved from http://gawker.com/screaming-kids-feckless-president-ruin-innocent-bees-w-1696113965

Jezebel Staff. (2014, August 18). “We Have a Rape Gif Problem and Gawker Media Won’t Do Anything About it.” Jezebel. Retrieved from http://jezebel.com/we-have-a-rape-gif-problem-and-gawker-media-wont-do-any-1619384265

Kress, G. (2004). Reading images: Multimodality, representation and new media. Information Design Journal, 12(2), 110-119.

Kress, G. & van Leeuwen, T. (1996). Reading Images: The Grammar of Visual Design (2nd ed.). New York, NY: Routledge.

Ram, A. (2012, December 6). “A Short History of the Gif, in 2 Minutes.” The Atlantic. Retrieved from http://www.theatlantic.com/video/index/265962/a-delightful-history-of-the-gif-in-2-minutes/

ENGL539 Designer Blog: Word Cloud

In preparation for my final project reflection, I made a word cloud of my designer blog:

Screen Shot 2015-05-04 at 1.32.19 PM

The most used words aren’t surprising, although I think it’s funny that “really” is one of the top words—I think that might be a result of how strongly I felt about coding (frustrated). “Basic,” “found,” and “tried” highlight the exploratory nature of the project. I’m surprised “hard” wasn’t used often…I suppose that I was trying to stay optimistic about the whole thing!

ENGL539 Designer Blog: (AP)I need a code

I’m updating one more time before the project is due, because clearly by my last post I was not anywhere close to having a viable code to show for the final project. According to my proposal, my code should have been perfected by 4/24/15, but I abandoned hope of that long, long ago.

Via my Googling, I found this blog, explaining how to collect tweets using Tweepy. I didn’t fully understand Tweepy, but from there, I learned that there’s a difference between a “Rest” and a “Streaming” API. From the descriptions, I learned that I would need a Rest API, and from there I found a line of code that would do exactly what I need, called GET statuses.

Returns a collection of the most recent Tweets posted by the user indicated by the screen_name or user_id parameters.

The site doesn’t really explain how to use it in context. Based on their example request line of code, I tried:

Screen Shot 2015-05-04 at 10.48.11 AMUnfortunately that did not work.

I went through some tutorials trying to find any that may have used that function. I didn’t see any, but I noticed that all of them had this code at the top that somehow authenticates your bot. I tried including that as well to see if it worked (spoiler alert: it didn’t).

Screen Shot 2015-05-04 at 10.51.05 AM

With a week left, I’m going to spend my time playing around with Tweepy API tutorials. I still haven’t made the jump from Dan’s tutorial on how to have bots produce work to my own goal of having bots collect work. If I figure that out, I’ll be satisfied!

ENGL 539 Designer Blog: IT’S ALIIIIIIIVEEEE (I Made a Bot)

Last week on 4/13/14, our Second Tutorial assignment was due. That forced me to get on track with my coding tutorial and put some of my knowledge to use. I was really happy with how it turned out! It was very basic code, and I’m sure I didn’t go about it in the cleanest, easiest way possible, but I wrote it from scratch and consider that in and of itself a huge accomplishment. Once I had gone through the first few chapters of the coding tutorial, I was able to start Googling smartly. By that I mean I had the basic jargon down, and had a better idea what search terms I should use to get the results that I want.

I still wasn’t sure how to go about applying the code (once I got it) to Twitter to start grabbing tweets for me. Luckily, in class last week we were in groups discussing the progress of our projects, and Dan mentioned that he has a tutorial on Twitter bots. I saw him post it on Facebook, but a. I didn’t know that a bot could do what I was asking—I associate them with Twitter profiles that interact with other users and produce things an b. I was hoping to start from scratch and figure it out on my own so I could understand the logistics of it. That definitely was not working, so I tried out the tutorial.

It went really well! It was very simple—I still ran into problems, but they were Google-able. For example, he instructs to install “tweepy” by using the line:

pip install tweepy

I kept receiving the error message:

-bash: pip: command not found

After a little searching, I found out that “pip” (whatever that is) does not come automatically installed with the version of Python I was using. I tried to manually install it, but after failing, just upgraded my version of Python and it was fixed! Finally, I got the bot to work:

Screen Shot 2015-05-03 at 11.07.32 AMBot results

My aim is to collect data, rather than reproduce tweets, but this was an excellent introduction to bots…now I have a better idea of what I’m working with, and how I use the code to make it do things. I am still woefully behind on having a code ready, but this tutorial introduced me to Tweepy, and I’ve seen a few tutorials about using it to data mine, so I think I’m getting closer.

ENGL706 Pre-class exercise: reflection

Reflection Piece – I’d like you to connect now all that you’ve learned in this class to your own work, be it as a teacher, as a scholar, as a professional writer/technical communicator. How do these new ways of thinking about the visual influence:

  1. How you see information being understood by your audience?
  2. How you approach the invention portion of your projects?
  3. The relationships between the visual and other modes (linguistic, auditory, kinetic, etc.)?

As a scholar, considering the visual influence is important in order to conceptualize information in new ways in the digital age. Burdick et al (2012) argue that the modern role of digital humanists is to democratize information that was once only available to the privileged elite. In order to do so, scholars need to take advantage of the affordances of technology to find creative ways to curate, organize, and transmit ideas. As Lemke (1998) points out, informatic literacy—the understanding of how to retrieve information—is vital in the digital age. He says that without those skills, “future citizens will be as disempowered as those who today cannot write, read, or use a library” (p. 79). As a scholar, making design choices to facilitate the audience’s ability to navigate information is an ethical obligation. Digital media has provided almost unlimited modal affordances to craft realities (Kress and Van Leeuwen, 1996); understanding of design principles and visual rhetoric theories is necessary in order to navigate the vast landscape of digital media.

This class has given me a deeper understanding of how the underlying structure of visuals and design communicate meaning, not just the explicit images. I have taken a visual rhetoric class at the undergraduate level, but the focus was on images, not visuals (a distinction I now understand is very important to note!). The structure of a piece is the way that a rhetor communicates his/her position to the audience. That includes the modalities used, the interactivity allowed, the design choices made, and the content. I now appreciate that the content is only a piece of the overall meaning of a work.

The underlying, less obvious structures related to reaction GIFs (my class project) is essential to drawing conclusions about their rhetorical functions. I’ve expressed some frustration over the semester about how their meaning is highly contextual, and it’s difficult to come to overarching conclusions about them. But I think that talking about the context of where they appear and their modal affordances has really helped ground my arguments. On a purely visual basis, there are a million meanings; each image is associated with different cultural understandings, and it’s impossible to account for all of those. Considering the modal affordances, site structure, and ethos of Gawker Media are much more manageable and useful frames to discuss reaction GIFs.

Because GIFs have been growing in popularity over the last few years, it’s important to understand their rhetorical function as a scholar who studies digital humanities. There are definitely “right” and “wrong” ways to use GIFs (the House of Representatives is famously bad at it). Understanding the “right” ways to use them is essential to establishing ethos. GIFs are a fun, relatable way to relay information,  and I hope that my work from this class helps to highlight a unique, under-studied way to relate to audiences.

icet

Here’s to the end of the semester and a great class!!

(I’d be a hypocrite if I didn’t have at least one GIF in here)

ENGL 539 Python Tutorial: Create Your Own Mad Libs

A note on resources: This tutorial was created on Mac OS X Version 10.9.5. I used the default pre-installed version of Python, version 2.7.5. I used Apple’s pre-installed Terminal application and Bare Bones Software’s free TextWrangler download.

  1. Open TextWrangler
  2. Enter # -*- coding: utf-8 -*- to allow for unicode characters. I had trouble with question marks and semicolons causing errors when running my code; this discussion seemed to fix the problem.
  3. Determine which parts of speech you will be replacing in your text for the Mad Lib. Start simple; for this tutorial, I replaced singular nouns, present tense verbs, adjectives, and adverbs.
  4. Define the part of speech
    1. Type the part of speech label
    2. Type = to indicate the words it will representScreen Shot 2015-04-13 at 3.06.55 PM
    3. Type your values between brackets [ ]Screen Shot 2015-04-13 at 3.07.16 PM
    4. Each value should be enclosed by quotation marks ” “Screen Shot 2015-04-13 at 3.07.27 PM
    5. Separate each value by a comma outside of the quotation marks “,Screen Shot 2015-04-13 at 3.07.37 PM
    6. Repeat for each part of speech you are defining; each list should be on a different line (example below)Screen Shot 2015-04-13 at 4.11.40 PM
  5. Type import random on a new line to import the module that will allow for you to randomize your textScreen Shot 2015-04-13 at 3.07.58 PM
  6. Paste your text on a new line and format. You can use any text, or create your own!
    1. Type print in front of your textScreen Shot 2015-04-13 at 3.09.57 PM
    2. Ensure the text is between quotation marks – this will ensure it appears as-is when the code is runScreen Shot 2015-04-13 at 3.11.04 PM
    3. Consider separating your text into multiple lines, if it is not already, in order to make the code readable
    4. Edit your text to suit the code. Ensure that \ are properly added to words with apostrophes or quotation marks that you would like to print when the code is run. Ensure all apostrophes/quotations are opened and closed properly. Ensure each line has “print” in front of it.Screen Shot 2015-04-13 at 3.12.49 PM
    5. Replace the words with the name of the part of speech in all caps and exclude them from the parentheses enclosing the string. This is so you can easily identify where you will paste the code for the random text generator.Screen Shot 2015-04-13 at 3.26.25 PM
    6. You can take this opportunity to paste in the random.choice code from step 7a, however I suggest waiting so that you can easily review the words that you removed.
    7. Ensure that the words you are replacing match the tense; for example, do not replace a plural noun if the words in your noun list are singular.
  7. Enter the randomization code into your text
    1. Replace the parts of speech in the text with random.choice(part of speech)
    2. Separate the random.choice code from the string of text with a comma
    3. Enclose the word before and and the word after the random.choice code with a quotation mark.Screen Shot 2015-04-13 at 3.34.21 PM
    4. Here is an example of the final code:Screen Shot 2015-04-13 at 3.34.09 PM
  8. Run your code through Terminal by opening the directory where your TextWrangler file is saved typing python filename.pyScreen Shot 2015-04-13 at 3.36.49 PM

Here is what the code looks like in its entirety:

Screen Shot 2015-04-13 at 3.40.35 PM

Here are a few examples of the results:

Screen Shot 2015-04-13 at 2.17.53 PM Screen Shot 2015-04-13 at 4.28.36 PM Screen Shot 2015-04-13 at 4.28.53 PM

ENGL539 Designer Blog: My Proposal Timeline is a Joke

So I turned in my official project proposal on 3/30/15, anddd I’m already off track. I vastly overestimated how quickly I’d get the language. I was basing my time estimations off of when I learned HTML a few years ago…I remember thinking it was really easy as I Googled everything. This is way more complex.

I started going through the Harrison video referenced in my last blog (and stated in my proposal that the tutorial would be done by 4/2/2015), however I just couldn’t get through it. I was lost within the first five minutes when he mentioned importing “cookielib” in the third line of code and didn’t explain further. Google couldn’t even give me a good explanation of what it was. The tutorial assumed you had some basic knowledge about Python, and it was tedious to keep googling along. From the Python beginner’s wiki, I found a very basic tutorial called Learn Python the Hard Way by Zed Shaw. It started as small as teaching how to become comfortable with Mac’s Terminal and TextWrangler, so I feel good about it. It’s very similar to Code Academy; the author strongly encourages readers to follow along and write out the code, even if it’s super basic. I feel much more comfortable with this format over a video tutorial. Unfortunately, I’ll need to take the time to go through Shaw’s book, and in my Proposal I projected that I’d be manipulating some form of my final code by now. Coding is hard 😦

ENGL685: Visualize how you see theory

Wednesday 4/1 Warm-Up: “Use Google Drawings to visualize how you see theory working in your project.” The poor quality wasn’t intentional, but the more I look at it, the more I think it was a subconscious representation of how I’m feeling at this point in the semester. Also, you probably wouldn’t believe that the person who created this has taken some visual rhetoric/design courses, but you’d be wrong.

Ashley's theory visualization