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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.


#GamerGate vs #StopGamerGate2014 By the Numbers – 10/20 edition

Edited on 10/20: Added info about specific users, more numbers.

Carly Kocurek, one of my smart and savvy IIT colleagues, pointed out that the #GamerGate and #StopGamerGate2014 discussions on Twitter are worth examining. So, I fired up a TwitterGoggles instance to track those hastags and these others she recommended:

#quinnspiracy
#gamergate
#notyourshield
#StopGamerGate2014
#academicANDfeminist
#gamerfruit

I saw @Gaming_Sparrow‘s tweet comparing the popularity of the two main hashtags. @ybika asked for some response, so I ran a couple quick queries on the data I’d collected and found totally different numbers.

From 10/17/2014 – 10/20/2014, I see

#GamerGate

33,039 users

278,548 tweets

and

#StopGamerGate2014

6,303 users

16,099 tweets

A few things could be happening:

  • Keyhole may be using a case-sensitive search, and mine is case-insensitive
  • Keyhole and I are getting different data from Twitter
  • #GamerGate has gained popularity and is now used by every side of the argument

No more time to process this today, but I’ll come back to it. What do you think is going on?


More info added later on 10/20:

I’ve seen a couple other tweets or posts about the number of users and the distribution of #gamergate tweets (e.g., Waxpancake: 100 people posts 24% of the tweets). It’s difficult to compare my data to his/theirs because I don’t know how Keyhole and Waxpancake are collecting their data. I contribute to TwitterGoggles on GitHub and know it much better. Of course, it still relies on the Twitter Search API, so there’s lots I don’t know about what’s not in my data. Anyway, here are some things I noticed while looking at my data.

#gamergate dominates other tags

Here’s a quick graph I made using Tableau. In this chart, the x-axis represents time where each bar is an hour, and the y-axis represents the number of tweets posted. The colors of the stacked bars map to the hashtags that appear in the tweet: blue for #gamergate only, orange for #stopgamergate2014 only, and green for tweets with both tags. This graph isn’t designed to make detailed comparisons easy – it’s just to show how incredibly popular #gamergate is compared to other tags. I also found it interesting that tweets contain both tags since they’re mostly at odds. Of course, one of the tweets with both is my own because I wanted it to show up in both conversations. Though, I may regret posting at all. Isn’t that the problem?

@mfreema55 asked me to post a higher-res image and explain the time info. So, here you go. The hours are GMT – so the graph says when people in the U.S. get off work, they start tweeting about this stuff.

Some of the most active voices change their names

Twitter assigns accounts unique user id’s, but users can change their full names if they’d like. A few accounts in the #gamergate conversation (I use the term broadly to refer to all the data associated with the hashtags above) have changed their names while tweeting. For instance, @nahalennia changed zir* name from “You Didn’t Listen” (160 tweets) to “The Future You Choose” (590 tweets) at some point in the last 3 days. So did @PsychokineticEX. Zir changed names from ADMIRALOF#GAMERGATE (528 tweets) to THE ADMIRAL (174 tweets). Both accounts are among the top 25 most active.

Users can also change their handles (the part after the @), but that seems far less common in this group. User #2815636153 is an interesting exception. Zir used names “and_next_name,” “my_next_name,” “need_next_name,” “the_next_name,” “their_next_name,” and “your_next_name” this weekend.

Skewed distribution of tweets/user

Like much of online activity, a few people are responsible for most of the content. This isn’t the most skewed distribution I’ve ever seen, but it’s definitely skewed. Or, it has a long tail. Depends on how you look at it. I haven’t normalized this (for anything, including how many tweets this account usually post), but that would be interesting too. I.e., maybe @SomeKindaBoogin just tweets constantly, so it’s not suprising that zir tweeted in this conversation a lot. Again, this graph isn’t about details. It’s unreadable at that level because I wanted to show you how incredibly long this tail is. Even if just a few people are incredibly active, there are still thousands of people engaging at some level. That’s exciting.

Tweets per user


 

* I’m using gender-neutral pronouns since I don’t know who these accounts belong to, whether they are owned by a person or a group, and since it makes sense to use gender-neutral pronouns when talking about harassment and safety.

 


Summary Stats about #StoptheNSA Twitter Activity

I gave a talk at Social Media Week Chicago with Prof. Ed Lee from IIT Chicago-Kent College of Law this week. We are studying a number of online political protests including the February 11, 2014 #StoptheNSA protest spearheaded by the Day We Fight Back. Here are the summary statistics about that day on Twitter:

Total Tweets: 98,515 (2.8x as many tweets about NSA as usual)
Original tweets N: 48,374 (49%)
Retweets N: 50,141 (51%)

We collected over 2M tweets with the hashtags #StoptheNSA, #NSA, and #daywefightback from February 7, 2014 to April 8, 2014 and found that the protest did

  • create high volume activity spikes
  • involve many and diverse users
  • reach huge audiences
  • generate attention

The graph below plots the number of tweets with any of those hashtags by day. You can see a great spike on February 11, the actual day of the protest, and another spike on March 25, the day President Obama gave public remarks about the NSA at the Hague. You can also see that on the day of the protest, the #StoptheNSA hashtag was quite popular, but it mostly disappeared by the time President Obama spoke at the Hague. The general #NSA hashtag, though, received continued attention throughout this time period. Even though the protest’s own hashtag died out, the protest was likely able to generate additional, lasting interest in the NSA.

#StoptheNSA Tweets by Day

The number of tweets posted using the #StoptheNSA, #NSA, or #daywefightback hashtags.

You can learn more about our findings from the slides for our talk. If you’d like to monitor and understand your own Twitter campaign, please contact me.


Who said it first – Congress or the press?

Sometimes Congress, sometimes the press, it turns out. Matt Shapiro and I wrote a paper for this month’s Midwest Political Science Association meeting in which we analyzed the timing of tweets with hashtags and New York Times articles with keywords and found

… news coverage and Twitter activity from the previous day are good predictors of news coverage and Twitter attention on any given day.

We wondered whether political issues popular on Twitter were popular in the press as well and whether issues cropped up among politicians on Twitter or in the press first. So, we retrieved all the articles available from the New York Times Article API for 2013 and all of the tweets Twitter would let us have for members of Congress (see links to code for collecting data below). We focused on hashtags and article keywords for six policy areas: budget, immigration, environment, energy, the Affordable Care Act (ACA), and marginalized groups (e.g., LGBT, military veterans, Latinos, etc.) and compared the timelines of when those issues were referenced in tweets and in articles.

The tables below show the results of our regressions. For most of the issues, they were similarly popular in the press and on Twitter on the same day. However, for immigration, Twitter activity in the past is a better predictor of news coverage than prior news coverage. For marginalized groups, neither prior news nor prior tweets are good predictors of a day’s news, suggesting that attention both in the news and on Twitter is spotty (or bursty) for marginalized groups.

The strong correlations between issues’ Twitter activity and news coverage on the same day (see models labeled “b” in the tables below) suggest, at least, that the press and Congress are giving attention to similar issues.

  Budget Immigration Environment
  (1a) (1b) (2a) (2b) (3a) (3b)
Previous day’s news .377*** .361*** .202*** .176*** .169*** .169***
Previous day’s tweets .366*** .114* .287*** .224*** .111** .115**
Same day’s tweets .351*** .184*** -.012
F-statistic 145.03 119.71 34.29 27.75 7.97 5.31
R2 0.45 0.50 0.16 0.19 0.04 0.04
N 364 364 364 364 364 364

 

  Energy ACA Marginalized
  (4a) (4b) (5a) (5b) (6a) (6b)
Previous day’s news .171*** .171*** .382*** .296*** .074 .078
Previous day’s tweets .129** .115** .201*** .073 .007 .035
Same day’s tweets .042 .313*** -.081
F-statistic 9.45 6.50 64.02 57.87 1.00 1.39
R2 0.05 0.05 0.26 0.33 0.01 0.01
N 364 364 364 364 364 364

Note: Each count of articles and tweets is a standard score, and beta coefficients for each predictor are reported. Predictors’ significance are indicated with asterisk where *, **, *** represent p<0.1, p<0.05, p<0.001, respectively.

Python Code for Collecting the Data


Collecting and Connecting On- and Offline Political Network Data

I gave a talk at the DIMACS Workshop on Building Communities for Transforming Social Media Research Through New Approaches for Collecting, Analyzing, and Exploring Social Media Data at Rutgers University last week. Here are my slides and roughly what I said:

Many of today’s talks are about gathering big social data or automating its analysis. I’m going to focus instead on connecting disparate data sources to increase the impacts of social media research.

Most of my work is about public policy, civic action, and how social media plays a role in each. Today, I’ll talk mainly about a study of how Congress uses Twitter and how that use influences public discussion of policy.

I’m in a department of humanities but have degrees in information and work experience in web development. My position allows me to witness divides in how different disciplines think about data and research, and I’ve included a few comments in my talk about why those divides matter.

Often when we study social media we’re looking at trends or trying to generalize about or understand whole populations, but I’m interested in a specific subset of people, what they do online, and how that online activity influences the offline world. This focus allows me to connect what we know about people offline with what they do online quite reliably. For instance, I can connect data such as party affiliation, geolocation, gender, tenure in Congress, chamber, voting record, campaign contributions, the list goes on, because these values are known for members of Congress. Getting their data from Twitter is trickier. Govtrack does a nice job keeping track of official accounts, but politicians often have 2 or 3 accounts – for campaign messaging, for personal use – and there are many bogus accounts out there. Once you find their accounts, you can use a variety of tools to capture their Twitter data. I wrote my own in Python and MySQL because most other free tools focus on hashtags and the Search API, and those don’t return the Twitter data I need.

So, back to the study. There’s plenty of hype in the popular press about how politicians wield social media influence to impact policy. Look at how Obama used the internet to become President! I wondered whether these claims are true – was social media providing a new route to influence for members of Congress?

Turns out, not so much. The people positioned to exert the most influence online occupy similar positions of power offline.

How can I be sure? Network theory, especially social capital theory, provides tools for making judgments about the relative power of people as a function of their positions in a network. And luckily, I’m not the only one who thinks network theory is a good way to interrogate power in Congress. Other researchers have used network theory to analyze relationships among bill co-sponsors, roll call votes, congressional committees, and press events. I’ll focus just on cosponsorship because it’s the most widely used measure of legislative influence. I compared legislative influence with a person’s ability to control the spread of information.

To do so, I used Jim Fowler’s cosponsorship network data and his measure of influence – connectedness – which is a weighted closeness centrality measure for the network analysts among us.

My data is a network of mentions among members of Congress. I have a few hundred thousands tweets from 2008 – present in which Congress mention one another about 75,000 times. Every mention creates an explicit, directed link between two members, and these links form the network I’m interested in.

Fowler’s data is an undirected, implicit network in that members are connected through their affiliation with legislation. To use Fowler’s data, I needed a way to connect members of Congress on Twitter to members in his data, a sort of key, if you will. Keith Poole’s ICPSR ID is a widely used unique identifier for individual members of Congress (that Fowler also uses) so, I developed a mapping of Twitter ID to ICPSR ID.

So Fowler used bill cosponsorship networks to figure out who wields influence over what legislation gets addressed and eventually passed. Turns out members with high connectedness are more effective at convincing their peers to vote with them. I used the same algorithm to measure who wields influence over the spread of information online. Being able to spread information quickly allows politicians to control the conversation. We know from studies of framing, for instance, that the first frame is likely to get traction and essentially constrain future discussions of an issue. What I found was that people with legislative influence also control information. While this correlation isn’t necessarily causal in either direction, what matters is that the same people wield influence both online and off. I can tell you a little more about those people too, because I was able to connect online behavior data with off-line demographic and political data.

Members of Congress who control the conversation, or at least are in a position to, are male House Republicans. If you’re a female, Democrat, feminist scholar like me, that’s scary. What are some of the implications of that information control? Well, for starters, male House Republicans nearly never talk about issues facing marginalized groups such as pay inequality, discrimination, and poverty. Instead, the online conversation is about the ACA, gun control, and the debt. Whether you think we should be talking more about poverty or the ACA, I expect you’d agree that some diversity in both topics and talkers would be welcome. But that’s not what I’m seeing. Instead, I’m seeing male House Republicans controlling the spread of information and attention through the Congressional network and to all of its followers.

We’ve now taken a whirlwind tour through one of my studies of political communication on Twitter. What did we find that matters? First, social media data is most useful when we connect it with other data. Second, social media is not providing an alternate route to power for members of Congress. Third, maleness and Republicanness are the most reliable routes to influence online. From my view, these results paint a pretty bleak picture were social media doesn’t actually challenge the status quo, and groups that wield disproportionate, and often oppressive, influence offline do so online as well. This isn’t quite the democratizer or equalizer I was hoping for, but as I work to understand what’s happening among citizens, maybe I’ll see something different.

I’d rather not end on a depressing note, so let me end on a call to action instead. The technical expertise required to do this study – to collect Fowler’s data, to collect Twitter data, to do the statistical and network analyses – may seem second nature to many of us here, but they are not to the people best equipped to interrogate this data. Let’s not measure of impact by the size of our dataset or the lines of code we had to write to get it. For instance, I have a colleague in political science at IIT who knows much more than I do about legislative influence and political communication. He shouldn’t also be asked to learn R and Python to contribute to the discussion about social media’s role in influencing public policy discussions. I hope we can remove, or at least diminish, the technical barriers for subject matter experts and scientists of other stripes to use [big] [open] [social] data and that we can change graduate education to train students in both social theory and technical tools.

See tweets from the conference: http://seen.co/event/dimacs-workshop-on-building-communities-for-social-media-research-core-building-rutgers-university-new-brunswick-nj-2014-4203

Access Fowler’s papers and data: http://jhfowler.ucsd.edu/cosponsorship.htm

Access Poole’s ICPSR ID data and information: http://www.voteview.com/icpsr.htm


Two Python scripts for gathering Twitter data

Anyone who has talked to me about my research in the last year and a half knows I’m constantly frustrated by the challenges of capturing and storing Twitter data (not to mention sharing – that’s another blog post). I hired a couple of undergrads to help me write scripts to automatically collect data and store it in a relational MySQL database where I can actually use it. We chose to use the streaming API because we limit data by person rather than by content. The Twitter Search API can handle only about 10 names at a time in the “from” or “mentions” query parameters. Since we’re studying over 1500 people, we’d have to run 150 different searches to get data for everyone. Using the Streaming API has its problems too – most notably that any time the script fails, we miss some data.

Below, I provide some info and links to two different scripts for collecting data from Twitter. Both are written in Python. One uses the Streaming API and one uses the Search API. Depending on your needs, one will be better than the other. The two store data slightly differently as well. They both parse tweets into relational MySQL databases, but the structure of those databases differs. You’ll have to decide which API gets you the data you need and how you want your data stored.

Both options come with all the caveats of open-source software developed within academia. We can’t provide much support, and the software will probably have bugs. Both scripts are still in development though, so chances are your issue will get addressed (or at least noticed) if you add it to the Issues on GitHub. If you know Python and MySQL and are comfortable setting and managing cron jobs and maybe shell scripts, you should be able to get one or both of them to work for you.

Option 1: pyTwitterCollector and the Streaming API

When to use this option:

  • You want to collect data from Twitter Lists (e.g., Senators in the 113th Congress)
  • You want data from large groups of specific users
  • You want data in real-time and aren’t worried about the past
  • You need to run Python 2.7
  • You want to cache the raw JSON to re-parse later

What to watch out for:

  • Twitter allows only one standing connection per IP so running multiple collectors is complicated
  • You need to anticipate events since the script doesn’t look back in time

Originally written in my lab, pyTwitterCollector uses the streaming API to capture tweets in real time. You can get the pyTwitterCollector code from GitHub.

Option 2: TwitterGoggles and the Search API

When to use this option:

  • You want data about specific terms (e.g., Obamacare)
  • You want data from before the script starts (how far back you can go changes on Twitter’s whim)
  • You can run Python 3.3

What to watch out for

  • Complex queries may need to be broken into more than one job (what counts at complicated is up to Twitter – if it’s too complicated, the search just fails with no feedback)

Originally written by Phil Maconi and Sean Goggins, TwitterGoggles uses the search API to gather previously posted tweets. You can get the TwitterGoggles code from GitHub.

 


Who in Congress talks to Each Other?

On Twitter, at least, most of the communication is between members of the same party. That’s not all that surprising given the polarized Congress and a slew of recent social science findings about homogeneous connections among users. I still think it’s interesting though.

A couple months ago I blogged about using geometric mean instead of simple edge weight and reciprocation measures, and I put that to use recently on data from Congress’s mentioning on Twitter between March 2012 and October 2012. The images below show the resulting graph using various geometric mean thresholds to determine whether or not an edge should display.

Geometric Mean 1 or Greater

Geometric Mean 1 or Greater

The image above includes all reciprocal relationships, regardless of how one-sided those relationships were. The yellow edges mean that there were mentions across party. We see more here than Adamic and Glance did among political bloggers, but the red (Republican to Republican) and blue (Democrat to Democrat) mentions clearly occur much more frequently.

Geometric mean 10 or greater

Geometric Mean 10 or Greater

In this image, the threshold for display was 10. That means these people are mentioning each other pretty often. A couple things jump out right away – first that the network is quite fragmented. The reciprocal network looks like a single component (I’ll have to check to be sure), but this one clearly has multiple components. Second, there are very few between-party links. Near the bottom, we can see Senators McCain, Graham, Lieberman, and Ayotte. I’d love to hear how Sen. Ayotte ended up in a conversation with those guys. Near the top, there’s another bipartisan conversation between Representatives Yoder and Cleaver from neighboring states; off to the right there’s another between Senators Moran and Warner. I’ll also look into why those guys are chatty.

Those two groups in the middle, where names are overlapping too much to read, have just within-party mentions. One group of Republicans talk amongst themselves, and one group of Democrats do as well. Then, just to the left of center, we see Sen. Grassley talking to himself to/from multiple accounts. That makes me think there’s a problem with the data. But, that’s why I put stuff here first – I can blog while I clean data and before I write the paper. The other groups are mostly representing the same state or from the same party. This exercise definitely presents a whole slew of new interesting questions to ask and answer.

UPDATE: Those two accounts for Sen. Grassley are actually his accounts – ChuckGrassley and GrassleyOffice.


UPDATED: Why didn’t the isolates go away?

I’m giving in. I’m finally learning how to do social network analysis R. What made me switch (from only UCINet and NodeXL)? Well, all my data lives in a MySQL database, and I have networks with millions of edges. R makes it really easy to connect to MySQL and create a data frame from data found there. That saves me about 20 minutes every time I want to do some analysis. No more selecting and downloading data and crashing UCINet and Excel, just

con <- dbConnect(MySQL(), user="user", password="pass", dbname="TwitterCollector", host="localhost")

mentions <- dbGetQuery(con, "SELECT * FROM tweet_mentions WHERE source_user_id IN (SELECT user_id FROM congress_attributes) AND target_user_id IN (SELECT user_id FROM congress_attributes)")

And I have all of Congress’s mentions of one another ready to go. Phew!

All I did today was get those connections setup, get some data in data frames for R to use, and then draw some rudimentary graphs like this one:

Mentions - Full network

I’m glad to see output, but I’m confused about why my isolate deleting functions didn’t work. Here’s how I tried to delete isolates:

mention_graph_no_iso <- delete.vertices(mention_graph, V(mention_graph)[degree(mention_graph)==0])

But I still see isolates in my graph. In fact, this one is even messier:

Mentions - no isolatesUPDATE:

The isolates weren’t in the graph object, but I forgot to rerun the layout after removing them. So, once I did that, I got a less messy graph (see below). I also cleaned up my code by moving the deleting isolates code to a function. I got the original function online but can’t find the page. Will post the URL here when I do. I made a small change to the function, and here it is:

delete.isolates <- function(graph, mode = 'all') {
isolates <- which(degree(graph, mode = mode) == 0)
delete.vertices(graph, isolates)
}

mentions_no_iso


I’m so ready for the gay #gayzzoli #Rizzles #RizzoliOnIsles

The title from this post is actually a tweet posted by Twitter user GayzzoliForever just before Tuesday night’s Rizzoli and Isles episode aired on the East Coast. I’ve been studying Twitter and watching Rizzoli and Isles for a few years, and this week I decided to give myself some time to work on a fun side project. It’s kind of like Google’s 20% time. Anyway, I decided to do some analysis about  the hashtags #gayzzoli and #rizzles.

I’m interested in how viewers engage with the text and subtext, and I’m especially curious about a subtext like #gayzzoli. What behaviors do #gayzzoli viewers label as “gay”? How do they respond when characters act more or less in line with the subtext? Why do we sexualize a friendship between two straight women, and what happens when we do? GLBT subtexts are certainly not new – e.g., for a really interesting study of slash fiction, for instance, see Rhiannon Bury‘s “Cyberspaces of their Own” – but this real-time, public discussion of a GLBT subtext seems remarkably bold. Even Sasha Alexander, who plays Dr. Maura Isles, talks about the subtext (she’s done it before, but that link goes to a recent clip from CONAN on TBS). She joked that season 4 would be called “Rizzoli on Isles,” hence the bonus hashtag in GayzzoliForever’s tweet in the title. Tess Gerritsen, the author of the books on which the show is based, and Janet Tamaro, the creator of the show, have talked about the #gayzzoli phenomenon too. SPOILER ALERT: Some of the tweets may contain spoilers, so watch the episode before reading if you care about that stuff. I don’t even watch the “next week on” snippets after the show, so I totally understand. Continue Reading


CSCW paper and poster about Congress on Twitter

My colleagues and I will present a paper and a poster at CSCW 2013 in San Antonio in February. Both submissions are based on data we collected from Twitter around politicians and their use of social media.

What’s Congress Doing on Twitter? (paper)

With Jahna Otterbacher and Matt Shapiro, this paper reports our first summary stats about who’s using Twitter and what they’re accomplishing. Using data from 380 members of Congress’ Twitter activity during the winter of 2012, we found that officials frequently use Twitter to advertise their political positions and to provide information but rarely to request political action from their constituents or to recognize the good work of others. We highlight a number of differences in communication frequency between men and women, Senators and Representatives, Republicans and Democrats. We provide groundwork for future research examining the behavior of public officials online and testing the predictive power of officials’ social media behavior.

Read the paper

Read my guest post at Follow the Crowd about the paper

“I’d Have to Vote Against You”: Issue Campaigning via Twitter (poster)

With Andrew Roback, one of my great graduate students, this poster focuses on the citizen side of the Twitter conversation. Specifically, using tweets posted with #SOPA and #PIPA hashtags and directed at members of Congress, we identify six strategies constituents employ when using Twitter to lobby their elected officials. In contrast to earlier research, we found that constituents do use Twitter to try to engage their officials and not just as a “soapbox” to express their opinions.

Read the Extended Abstract


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