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Posts tagged with: digital humanities

Looking for (Lesbian) Love: Social Media Subtext Readings of Rizzoli and Isles

Here’s the abstract of my paper that was just accepted to IR16, the annual conference of the Association of Internet Researchers. This will be my first trip to IR, and I’m really excited to participate. See you in Phoenix!

Introduction

Using Fiske’s (1989) semiotic supermarket metaphor, I examine how Twitter users mix and match moments from Rizzoli and Isles to create a coherent lesbian subtext. To do so, I use tweets containing the portmanteau hashtag #Rizzles or the related tag #Gayzzoli posted during two different episodes of the show. Live tweeting affords us an opportunity to eavesdrop on viewers’ listening activities and provides data useful for testing theories about reading/viewing and participation. Here, I demonstrate the utility of analyzing live tweeting and provide examples of how live tweeters publicly read resistant subtexts.

Fiske (1987) argues that readers are able to assemble their own texts from television works by “’[listening’] more or less attentively to different voices” within the work (95). Though he didn’t introduce the term semiotic supermarket until later (Fiske, 1989), Fiske does provide a semiotic framing that is useful for analyzing social media readings of television texts. For instance, he argues that viewers exploit contradictions within the texts to locate their own social identities within the text (Fiske, 1986).

I argue that we should understand the lesbian subtext reading of Rizzoli and Isles as precisely this kind of polysemic reading. I show how #Rizzles readers locate their own social identities within the text of the show and then use social media to share those locations with others publicly.

Background on the Show

Rizzoli and Isles is a police procedural based on mystery novels written by Tess Gerritsen and produced by TNT. The title characters are Detective Jane Rizzoli, played by Angie Harmon, and medical examiner Dr. Maura Isles, played by Sasha Alexander. The characters in both are written as straight, heterosexual women who are also close friends. The creators[1] and actors[2] of the novels and shows have acknowledged the lesbian subtext readings. My analysis covers episodes from the fourth (“We Are Family”) and fifth season (“The Best Laid Plans”), so please be aware that the remainder of the paper contains spoilers. 

Collecting Tweets

I used TwitterGoggles (Maconi, 2013) to collect tweets containing either of the hashtags #Rizzles or #Gayzzoli. I’ve limited my analysis here to tweets posted on the date of the original U.S. broadcast of each of the episodes.

Live Tweeting and the Semiotic Supermarket

#Rizzles and #Gazzoli viewers mark common lesbian and romantic tropes almost immediately. When we first see the characters together, they are jogging shortly after Maura’s been cleared for physical activity after donating a kidney. Jane is trying to encourage Maura to keep jogging when she doesn’t feel well.

Dialogue Individual Frames
RIZZOLI: You’ll feel much better when you get back in shape. Ok? C’mon.

 

ISLES: Are you saying I’m fat and out of shape?

 

RIZZOLI: No, I am saying that you have got to stop hoping that they are going to send you some “thank you for your kidney” fruit basket.

Figure 1. Jane encourages Maura to keep going.

Figure 1. Jane encourages Maura to keep going.

 

Figure 2. Jane reacts to Maura's question.

Figure 2. Jane reacts to Maura’s question.


Viewers responded with tweet such as

“Are you saying I’m fat and out of shape? Oh look. First lover’s quarrel of season 4. #rizzoliandisles #gayzzoli” (Mirettesvertes, 2013)

“Where (sic) 90 seconds in and they’re already like an old married couple! #rizzles” (Nate, 2013)

“Nobody does bickering married couple like Rizzoli & Isles. #gayzzoli” (Marie, 2013)

We can already see from this first scene and these three tweets that the viewers are assembling a text in which Rizzoli and Isles enact love and marriage, not just friendship. None of these tweeters explicitly mention Jane’s or Maura’s gender, so they are marking their behavior not necessarily as lesbian but as romantic love. We can also see that viewers are not locating just their own social identities, but sometimes, as in Nate’s case, others’ subordinate identities. Nate describes himself as, “Just your average 35 yr old guy who enjoys Gilmore Girls, HTGAWM, Castle, NCIS & Scandal (& more) and writes fanfic. Feminist. Livetweeter. Liza Weil’s #1 fan,” in his own Twitter description and compares Jane and Maura to a married couple even though he doesn’t identify as a lesbian.

I introduced this project by situating it as a polesemic reading in line with Fiske’s Hart to Hart examples (1986). Rizzoli and Isles differs from Fiske’s examples because the show already resists dominant readings by having two female lead characters who have a relationship independent of their relationship to other characters. For example, Rizzoli and Isles passes the Bechdel test[3] each episode – the characters are often talking about their work (solving murders) or their own lives (struggles with their parents) without talking about men. In reading a lesbian subtext of the show, #Rizzles and #Gayzzoli tweeters are not just resisting the text but arguing that the text itself should have done resistance differently. The twin paucities of straight female friendships and loving lesbian relationships involving series lead(s) depicted in television and film in the U.S. makes Rizzoli and Isles an easy target for subtext readers. The show is susceptible to lesbian subtext readings precisely because viewers don’t see straight female friendships often enough for one to seem like an acceptable canonization.

Conclusion

The live tweets viewers post allow us to watch their readings of television episodes unfold. This data, especially when coupled with the episode text, allows us to test our theories of audience and participation. I demonstrated this approach and provided evidence of resistant readings that publicly mark polysemic moments in the text. These moments in Rizzoli and Isles are especially interesting because the multiple meanings are marked by viewers who don’t share a singular social identity.

References

Bechdel, A. (1988). The Rule. In More Dykes to Watch Out for: Cartoons (p. 22). Firebrand Books. Retrieved from http://dykestowatchoutfor.com/wp-content/uploads/2014/05/The-Rule-cleaned-up.jpg

Fiske, J. (1986). Television: Polysemy and Popularity. Critical Studies in Mass Communication, 3(4), 391–408.

Fiske, J. (1987). Television Culture. Methuen.

Fiske, J. (1989). Understanding Popular Culture. Routledge.

Hickey, W. (2014, April 1). The Dollar-And-Cents Case Against Hollywood’s Exclusion of Women. Retrieved from http://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/

Maconi, P. (2013). TwitterGoggles [source code]. Retrieved from https://github.com/pmaconi/TwitterGoggles

Marie [buknerd] (2013, June 25) Nobody does bickering married couple like Rizzoli & Isles. #gayzzoli [Tweet]. Retrieved from https://twitter.com/buknerd/status/349694021876203520

Mirettesvertes [mirettesvertes] (2013, June 25) “Are you saying I’m fat and out of shape?” Oh look. First lover’s quarrel of season 4. #rizzoliandisles #gayzzoli [Tweet]. Retrieved from https://twitter.com/mirettesvertes/status/349693940485734400

Nate [mrschimpf] (2013, June 25) Where 90 seconds in and they’re already like an old married couple! #rizzles [Tweet]. Retrieved from https://twitter.com/mrschimpf/status/349693974237286400

Penguin, Awkward [socawkpenguin78] (2013, June 25) The closer we get to the #RizzoliandIsles premiere, the more nervous I’m getting. What if it’s just a giant beardfest? #gayzzoli #Rizzles [Tweet]. Retrieved from https://twitter.com/socawkpenguin78/status/349625007711862784

[1] http://www.tessgerritsen.com/fanfic-and-rizzles/

[2] https://www.youtube.com/watch?v=CUu27ig9Wgw

[3] A popular tool for measuring gender bias in Hollywood, the “Bechdel test” is named for a comic strip by Alison Bechdel (1988). Her strip’s characters claim a movie passes if it (1) has at least two named women who (2) have a conversation with each other that (3) is not about a man. A recent Five Thirty Eight analysis found that only half of movies pass the test (Hickey, 2014), demonstrating that media “passing the test” is not the norm.



Introducing Text Analytics to Undergraduates

I teach a methods course called Research Methods in Digital Humanities that’s geared toward our digital humanities majors and technology and humanities graduate students. Our basic course materials include the useful Digital_Humanities text from MIT Press, Python tutorials from Codecademy, and a few videos on our class YouTube Channel. My goals for the course are to introduce students to research in digital humanities through a variety of case studies, hands on labs, and readings of transmedia projects. One of our hands on labs is based on John Laudun’s activity for introducing undergraduates to computational methods for textual analysis (see his excellent blog post for more info).

The lab asks students to use computational tools to compare two or more texts along a number of dimensions: e.g., structure, length, themes, and word choice. They can work on this part in groups or individually. Then, using the results they’ve generated, they must write individual blog posts discussing their findings.

While Laudun uses one text – Richard Connell’s “The Most Dangerous Game” – I require students to use two or more texts they find on Project Gutenberg. I do so for a few reasons:

First, earlier in the semester I ask them to contribute to a data census in order to learn about metadata and data reuse. Requiring them to use Project Gutenberg ensures that they get some practice re-using data, and they spend their time analyzing rather than collecting data. It also allows them to choose texts they are actually interested in. As I learned the first time I walked through this exercise, texts I think are going to be engaging often aren’t. My excitement about comparing Jane Austen’s Emma to the film adaptations Emma (1996) and Clueless (1995) was met with blank stares for 45 minutes.

Second, comparing texts rather than analyzing a single text seems to help students understand why we do text analysis at all. They begin to see why we care about features such as structure and word choice when they see that authors have made different choices.

Third, comparing texts requires them to do most of these activities twice, and repetition helps them learn. Especially because most of my students have never used Python or Project Gutenberg or done any text analysis, it’s important to give them a chance to practice. When they do the analysis for the first text, it’s rough. They fail a lot – they get results that don’t make sense, they forget to change some setting in the code, whatever. Doing it again allows them to end on a high note – the second or third or n’th time, the analysis goes smoothly, and they can see that they’ve made progress.

I assume you can install and run Python. I’ve forked Laudun’s Useful Python Scripts for Text repository. Clone the repo, run <code>pip install -r requirements.txt</code>, and you should be ready to get started.

Changes to Useful Python Scripts for Text
I’ve made a number of modifications to Laudun’s original scripts, and here’s some more info about what/why I did.

First, I’m working my way through each file editing them to use main() functions and if __name__ == “__main__”: main() calls. StackOverflow has a few good posts about why to do this. See What does `if __name__ == “__main__”:` do?, for instance. The gist is that declaring and then calling a main function separates the functions from the code that should execute. It also means that stuff in the main() function happens only when you call it as a standalone script (i.e., not when you use it in other programs).

Second, I’m dividing the scripts into sets of functions generally. Why? Functions run faster. Again, StackOverflow has more info on why. Also, functions are cleaner than scripts and can be used in other programs. If you’ve looked at any of my older code on Github, you know I used to write straight scripts all the time too. I’ve seen the function light.

Third, I’m changing the way stats.py counts lines, paragraphs, words, etc. to accommodate Project Gutenberg texts. In Laudun’s original code, each line was a paragraph, but Project Gutenberg texts have blank lines between paragraphs and multiple lines with paragraphs.

Fourth, eventually I will fix the wordcloud functions. They are based on word_cloud from amueller (read more on his blog), but they don’t work out of the box for me (or Laudun, apparently). For now, I have students use Wordle to make their word clouds. Adeline Koh recommends some other word cloud activities and tools in her Hybrid Pedagogy article about introducing digital humanities to undergrads as well. She doesn’t discuss the Python and computation approaches that Laudun and I use, though.