Graphic Sociology

World's water resources by type of water, via The Atlantic data from the USGS

Is Minnesota like Saudi Arabia?

Minnesota is the land of 10.000 lakes and thus holds far more than its representative share of precious fresh water. Is this synonymous with the naturally granted wealth of oil in countries like Saudi Arabia? Maybe. But does that mean Minnesota is going to become a state with a similar level of political economic power? No…not so much. It is silly to compare nation states like Saudi Arabia to states in a federation like Minnesota; it is silly to think that a state with an existing economy relatively unreliant on water is going to suddenly transform itself into an economy with a single primary commodity; it is silly to think that a democratic governance system will respond like a dictatorship did to a valuable commodity. As an aside, Tim Mitchell’s latest book, Carbon Democracy makes a historically grounded argument about the relationship between the material qualities of oil and coal and the technics of the political economy that developed in concert with carbon-based wealth.

How are information graphics like propaganda?

This infographic is more than half graphic (and less than half ‘info’). Normally, that’s not the best balance for displaying social science data. Usually, social science data is multi-faceted, requires a contextual framework for adequate understanding, and the sheer amount of information necessary to tell the story makes it harder to include graphic elements that do not represent information. However, this is not social science data. Technically, it is geological data, but I think it would be more accurate to describe it as data that is being mobilized for political reasons. Hence, the title of this post makes a blatant comparison between water (blue gold) and oil (black gold) to emphasize the implicit political valence of the message in this graphic.

In short, information graphics are being mobilized for what are essentially purposes quite similar to propaganda. This particular graphic is not the best example. It is the example I happened to see yesterday, and it does a good job of demonstrating what is at stake in the current infoscape with respect to information graphics. These graphics are generally considered to be intellectual and political lightweights compared to communication that is based on the production of critical texts. Overlooking the work that these graphics do is both dangerous and foolish. For one, many critical voices from within the academy *have* critical messages they have trouble communicating with broader audiences because many audiences are unlikely to read academic writing, even if that writing is posted to blogs. If these academics can create their own graphics, they add another tool for communicating clearly just what their perspective is. Yet pretending that information graphics are either merely ‘pretty’ or that they are straightforward representations of empirical data avoids engaging with the way that political messaging is built into graphic design.

One reason this blog exists is to help people start to sharpen their critical visual analysis tools. As educators, we spend a lot of time in the classroom teaching students how to write and how to stop believing everything they read by becoming aware of rhetorical moves, selective mobilization of facts, and reliance on carefully chosen narratives that initiate particular kinds of human perceptual biases and emotional responses. Art historians teach the same kinds of critical skills for interrogating paintings and photographs. Media studies folks teach the same kinds of skills for interrogating popular culture products like television shows, films, and magazines. Social scientists would serve the discipline well if they begin to teach students how to critically consume information presented in infographics.

References

Thinking Big Series. (2012) The World’s Water Supply. The Atlantic. This series is sponsored by Fidelity Investments, LLC.

Mitchell, Tim. (2009) Carbon Democracy: Political power in the age of oil. Verso.

The Rich Get Even Richer research by Thomas Piketty and Emmanuel Saez. Graphic by the New York Times, Stephen Rattner author.

What works

Quoting Justin Wolfers who I happen to follow on twitter, it’s generally not good practice to look at a single year’s worth of data, especially when it would be easy to get comparable data going back for years. Still, in this particular economic news climate, many of the people who are likely to see this graphic have some sense of the relevant contextual data in their heads already, thanks in part to the Occupy Wall Street movement but also to the often thankless work of social scientists and labor statisticians who have been working on issues like income distribution since long before OWS congealed. That’s a long-winded preamble to summarize two fairy simple achievements in this graphic:

1. This graphic demonstrates that it is possible to make it appear as though there was income growth for everyone in 2010 – even that bottom 99% saw an INCREASE in income, albeit a tiny one – despite the fact that the economy was rather slack in 2010.*
2. The graphic amply demonstrates that the post-2008 world is quite similar to the pre-2008 world in the sense that income distribution is dramatically skewed. The rich do get richer.

One thing that the article draws readers’ attention to is that the study, which looked at tax returns, and the graphic are about income. Thus, we are not talking about the distribution of wealth (ie the accumulated capital that results from single year uneven distributions of income and a lack of attendant unequal distributions of spending). The rich folks in 2010 got most of this income from labor, not from returns to investments.

What needs work

*One thing I fear is that this graphic obscures an important truth by comparing only the top 1% to the bottom 99%: many people had declining income in 2010. This graphic makes it seem like everyone got *something* but really, the folks at the bottom of this distribution got no increase or a decrease, for the most part. From a statistical leverage point of view, the 99% is just too big of a group to be all that revealing. The spotlight is on the 1% in both this graphic and the current political economic discourse in a way that curiously contributes to the inaccurate notion that America is a classless society. One of the things that makes the 1% vs. the 99% a clever rhetorical frame in America is that we all thought we were in the middle class before OWS and we can now continue to think of ourselves as one giant middle class with this troublesome small pimple of a distribution problem to sort through represented by a mere 1%. The whole 99% sounds so comfortably inclusive and that pesky 1% must, in the end, be a manageable problem because it sounds kind of small-ish. It’s only 1%.

Of course, the rhetorical move of splitting the American population into the 1% and the 99% sets up for all these fantastic (as in remarkable, not as in laudable) statements, like the one made by the graphic, that go something like: “The top 1% of the population got 93% of the income in 2011 while the bottom 99% only got 7%.” Being able to make comparisons like that is a more straightforward, empirically sound reason for the 99% vs. the 1% framing than one that seems to make an effort to avoid noting that America has a lower class.

Relative frame: US vs the world

If you are not in the 1% – and most of you are not – I imagine you might be feeling righteously indignant right now. But think of it this way. All of you have computers and internet connections, most of you are American or English according to the google analytics for this blog, and are therefore in the global 1%. It’s a golden rule problem not in the sense that you should do unto your less fortunate global neighbors what you would have your more fortunate doctors/bankers/lawyers/businesspeople do unto you – though I suppose that might apply, too – but more along the lines of, ‘those who have the gold, rule’. Revisit C. Wright Mills The Power Elite, skim a bit of Marx, and maybe look at something a little more recent like Tim Mitchell’s Rule of Experts and this graphic and the entire OWS narrative is analytically similar to a snapshot of a sports event: different in its particulars but so predictable it’s almost trite. It would be trite if there weren’t so much at stake.

References

Rattner, Stephen. (25 March 2012) The Rich Get Even Richer. In the New York Times, The Opinion Pages.
Note: The author, Mr. Rattner, is himself a member of the 1%. Sometimes when I see graphics like this, I wonder if people who know that they are in the 1% are secretly congratulating themselves for having done so well compared to the rest of us.

What a man wanted, what a man wants | Boxer, Noonan, and Whelan

What Men and Women Want in a Mate, 1939-2008 | Boxer, Noonan, and Whelan

Update on references

As you can see in the comments, Christie Boxer, the lead author of the journal article behind the Coontz Opinionator piece has contacted me to let us all know that the article is currently in revise and resubmit phase but will be published in Journal of Family Issues shortly.

What works

The graphic is more legible than the chart from which the data originated. I’m guessing the Journal of Family Issues would not allow such a “fancy” series of graphics in the final published piece so I don’t mean this as a critique of the article’s authors. Just pointing out that there is good reason for journals and other publishers to reconsider their policies about how data can most usefully be presented.
I happen to have created a few graphics in this style myself and tend to favor it over the chart (e.g. this one about agricultural subsidies) in the past and think they work well for displaying changes in attitudes over time.

What needs work

Illustrations to Accompany "The M.R.S. and the PhD" by Stephanie Coontz, New York Times

The article from which this news story is drawn clearly provides information on both what women want and what men want in greater detail than what’s seen here. Why did the news story choose to run with less than half the data?
The chart clearly contains information on what men want in a mate AS WELL AS what women want in a mate. I see no reason for going (less than) halfway on this story. In fact, what I find most interesting is the convergence on some things – nobody cares much about chastity in a mate any more – and divergence on other traits – women rank men’s desire for home and children much higher than men rates women’s desire for home and children. That’s a puzzler worthy of thought in a way that a story that reflects only what men want is…well…just not all that interesting. Pair bonding takes two, as I’m sure Coontz knows because she’s been researching marriage for years. It’s unclear if the Times pressured her to come up with a more attention grabbing headline “The M.R.S. and the PhD” or if she chose that on her own or if it was a combination of factors.

I’m glad to see that, at least as far as I can tell from what is available to scholars other than Coontz (who might have an early full-length, unreleased draft of the Boxer, Noonan, Whelan paper), the scholars whose data led to the graphic were not so singly concerned with what men want in a mate. They were looking at how mate selection characteristics have been adjusted over time for both men and women and I hope that their article looks at the consonance and dissonance between the two genders’ mate selection ideals.

I would have preferred more attention paid to the graphic – like, say, the inclusion of what women want or an integrated graphic that displayed the overlaps and distances between what men and women want – and less time put into the accompanying illustrations which I have included to the left. I welcome regular readers of Sociological Images (and others) to comment on the messages coming out of the illustrations.

References

Coontz, Stephanie. (2012) “The M. R. S. and the PhD”. The New York Times, Sunday Review, Opinionator. [Information graphic by Bill Marsh/The New York Times]

Boxer, Christie; Noonan, Mary; and Whelan, Christine. (forthcoming) “Measuring Mate Preferences: A Replication and Extension” Journal of Family Issues. [Table drawn from Christine Whelan's research webpage]

Partnership-driven infrastructure project financing | Creating a formula for success from existing projects

Visualizing Finance

For almost a year I have been working at the Center on Law and Public Finance, a center based at New York University’s Institute for Public Knowledge, which is currently dedicated to research on American infrastructure. Infrastructure has a halo of geeky coolness about it that is a combination of the tinkerers desire to figure out “How Things Work” ala David MacCaulay and the awe of beholding massive public works projects like the Hoover Dam, the Tappan Zee bridge, and New York City’s monumental water delivery system.

Tappan Zee Bridge | ABC Local

Right now, though, the debate in DC and in various states is about how we can pay for the upgrades and extensions of our infrastructure that are badly needed. It’s also about just what counts as infrastructure. We define infrastructure as physical, regulatory, bureaucratic, and behavioral assemblages that are durable over time. This is a fairly academic definition, but it allows for the inclusion of not only of bridges, roads, ports, and mass transit, but also of things like suicide prevention hotlines, manufacturing plants, and educational institutions. Once we broaden the definition to include a more realistic, inclusive set of infrastructures that underpin civic, commercial, and social life, the challenge of explaining how we might pay for these projects gets even harder.

For our most recent report, “Partnership-driven Growth: A bipartisan way forward”, I tried to develop a flexible strategy for demonstrating the reliance on partnerships of monetary and non-monetary support that come together to meet the specific needs of particular projects, while following a loose template adopted by many infrastructure projects. Since infrastructure generally benefits many constituencies, including civic society, the most common successful infrastructure funding is like a collage. Often, successful projects draw on a modest amount of federal support, either in the form of loans, loan guarantees, or (matching) grants. These federal dollars are good at acting as funding anchors (and votes of confidence) which tend to smooth the way for states, local governments, and private investors to commit their own funds and support to the projects.

One of the things I wanted to emphasize with the graphic was that though each project presents a unique ‘flower’, there is a general formula for success. Nobody is out there re-inventing the wheel with respect to financing vehicles even though it might sometimes feel like that for local governments, states, and private investors who haven’t built many financing vehicles. I was also trying to find a way to indicate that not all support for infrastructure projects is monetary support. Sometimes support comes from a willingness to change a zoning law or to create a partnership with a local university where the business, design, or engineering school dedicates time and effort to overcoming challenges within the infrastructure or business plan.

The page you see at the top of the post was the frontis-page for a section in the report that looked at a number of case studies. Each case study contained the same “flower” from the frontispiece with a lengthier description of just how much of which kinds of funding were involved. I’ve included the relevant page of the Tesla case study below, just to demonstrate how the design was developed within the report. I wanted the frontis-page to this section to give readers pause – they had just made it through about 10 pages of prose – and to help them connect individual projects back to a general ‘formula for success’. Hence, I repeated the flower form from the frontis-page in each of the case studies, hoping that a little repetition would help to cement key concepts.

Tesla Fremont partnership project case study

Infrastructure banks

Politically, the reason it is important to understand how infrastructure financing works when it is successful, is that both at the national level and within particular states, lawmakers are considering establishing infrastructure financing authorities (hereafter referred to as infrastructure banks). The exact dimensions of these banks are still being hashed out. Will they fund only certain sectors of infrastructure like transit, energy, and manufacturing or should they include social infrastructure, too? Will they use revenues generated by some of the stronger infrastructure sectors to help support those sectors that are less likely to be self-sufficient? Or, should each project be responsible only for its own bottom line? Since infrastructure has a long time horizon, what is the best way to set up lifecycle-aware financing structures?

Electric Vehicle Charging infrastructure schematic | Schneider Electric

Our current work tries to build a baseline of understanding so that decision makers, including voters, will have a framework within which to advocate properly for their own interests while keeping an open mind about the visionary possibilities of infrastructure banks. This discussion needs to be much bigger than one that only responds to the “we’re in a recession, let’s find a rapid cash infusion from the private sector” frame. A new bank could do much more than that. It could be time to reconsider agency structures and break down silos; it could be time to reexamine the way infrastructure necessary for commerce relates to private sector revenues; it could be time to recognize synergies between sectors that make more sense now than they did in the past (the energy sector and private automotive transportation have something different to say to each other as more cars are electric, for instance. Social infrastructure and broadband supporters have a different conversation now that so many people turn to the internet for social services and broader social support).

There will be more to come in this series. The conversation is just getting started.

Criticism welcome

As always when I present my own work, I invite criticism. Readers of this blog have been generous (and civil) with their comments in the past and I am quite grateful to have such a thoughtful readership.

References

Likosky, Michael and Norén, Laura. (January 2012) “Partnership Driven Growth: A bipartisan way forward” [Report] Center on Law and Public Finance, Institute for Public Knowledge, New York University.

Living Alone by Gender, Age Cohort in the US since 1850

What works

This post is an update to an earlier post about the increasing rate of Americans living alone. The first graph does an excellent job of visualizing the change in Americans’ tendencies to live alone, by age and gender. It’s clear that living alone is on the rise, especially for Americans over 45. It’s interesting that there seems to be a collective slow down in this trend in the decade between 35 and 45 when I suppose some of the late-to-marry people finally settle down and before the marital dissolution rate starts to fire up.

The graphics in this post accompanied an article by Eric Klinenberg in the New York Times Sunday Review that laid out the basic findings in his latest book, “Going Solo” that was based on 300 interviews with people living alone. He finds that while for some, living alone is an unwanted, unpleasant experience, most people who live alone are satisfied with their personal lives more often than not. In fact, they are more social, at least in some ways, than are their counter-parts who live with others. Singletons (his word, not mine. I prefer ‘solos’ in part because it’s an anagram), go to restaurants and other social spaces more often than do those who live with others.

Living alone in Minneapolis

In a number of cities, including Minneapolis, more than 40% of households are single-people households. The article included an interactive map down to the census tract level that shows what percentage of households in that tract were single-person households in 2010. I took a look at Minneapolis and St. Paul and found that the map supported Klinenberg’s qualitative findings. The highest concentration of solos is in the center city areas where opportunities to get out and be social in the community are the highest. The suburbs and rural areas have fewer solos.

I encourage others to use the map and see if their local cities replicate this pattern, that more solos live in ‘happening’ areas than in quieter areas. Of course, this could be caused by a third variable, the presence of households that are affordable for single-earner households…but there isn’t enough analytical power in the map tool to be able to sort out the dependencies.

What needs work

The information about who lives alone by age, marital status, and race that is displayed in the following long skinny stack of datapoints is the right kind of detailed information to use as an entrance into a deeper discussion about living alone, now that we’ve gotten a sense of the view from 30.000 feet. The problem is that this graphic is hard to read, too long for a single computer screen (but in order to make sense of it, one needs to see the whole thing at once), and too optimistic about what color differences are able to do than is reasonable.

The article does a better job of subtly navigating the movement from historical and international context into a detailed, robust analysis. By awkwardly pinning all the data points onto the stalk at once, viewers lose the ability to see patterns within data subsets. Here’s a test. Look at the following data and try to explain to yourself how race and living alone go together. Or how age and living alone go together. The graphic designer was hoping color would be able to do more than it has been able to accomplish here. The color is supposed to tunnel your vision down to a particular color-coded subset so that you can start to understand well just what it is about race or age or marital status that produces particular patterns in living alone. But I had a lot of trouble with the color frame because, quite literally, I had to keep shifting the frame around this graphic – it didn’t fit on my laptop screen. [Graphic designers often work on nice, roomy screens where they end up seeing more at once than their eventual audience who is probably peering at this thing from a web browser on a laptop or occupying half of a monitor somewhere.]

All the clustering around the mean is another problem that could have been avoided had the graphic been organized differently. As it is, all sorts of groups lump on top of one another down around 14%.

I also kind of hate that I can’t add categories together in any meaningful way here. I can tell that being a widow would put someone at high risk for living alone, but that’s kind of a no-brainer, isn’t it? I would have gotten more mileage out of visualizing the absolute numbers of people living alone by marital status, age, and race. Maybe over half of all widows live alone, but I haven’t the faintest idea how many widows there are in America so I don’t know if half of all widows is half a million people? Or 3 million people? Or whether it’s more or less than the 38% of separated people who are living alone. 19% of never married’s live alone, but because these people are likely to be young, maybe that is actually a larger absolute group than the 58% of widows living alone.

Final verdict: There was both a data fail and a graphic design fail.

Who lives alone? A demographic breakdown

References

Going Solo Cover

Klinenberg, Eric. (2012) One’s a Crowd. New York Times Sunday Review.

Weber, Susan and Beveridge, Andrew. (2012) [infographics]
Solo in America graphic Line graph looking of the changing percentage of singleton households in America, 1850-2000
More on their own here…and even more abroad American and International singleton households.
Mapping the US Census: Percentage of Households with only one occupant Interactive graphic of US singleton households by census tract.

Figures 1 and 2 from "Who Gives a Tweet?" by André, Bernstein, and Luther CSCW paper

What works

A new study will be presented in a couple weeks at CSCW by researchers in Human-Computer Interaction and Social Computing that used 43,000 ratings of tweets to explain what content twitter readers find useful.

In short, worthwhile tweets:
1. Are informative NOT boring
2. Are funny
3. Are concise (even shorter than 140 characters!)
4. Are hyper-timely
5. Avoid whining and navel gazing (Tweets about meals past, present, or future are ‘boring’)
6. Avoid using too much twitter mark-up like @ replies, hashtags, multiple links)

The graphics do a good job of providing a visual overview of the study’s findings. With my brief textual synopsis and the two graphics here I bet many of you reading this will feel like there is no need to go read the study itself. Just in case that’s true, you should know that in the author’s discussion section, they note that their raters were volunteers who were not randomly chosen and skewed towards the tech crowd. Perhaps there’s reason to believe that tech people would be more likely to appreciate informative tweets? Not sure. But I can say from my own research that there is a noticeable portion of the twitterverse that appreciates food-related tweets. Even within that sub-group, people tend to appreciate tweets about recipes or with pictures over tweets that just say, “I had a great #sandwich at lunch! Fresh mozzarella rocks.” A recipe is informative. A recounting of lunch or a whiny tweet about missing lunch is boring at best and annoying at worst.

The thing I like best about this piece is that many of the findings apply to communication in general, not just tweets. Folks, it’s probably true that whether you are tweeting or talking, nobody wants to know what you had for lunch unless they want to have what you’re having. And if they do, they’ll probably ask. No need to volunteer. Also: brevity is the soul of wit; and wit is wonderful.

As an aesthetic point, I think they got the colors about right. Red represents the not-worthy or bad votes that ought to stop; blue represents the neutral position; and green represents the good tweets tweeps should go for.

What needs work

This graphic came without a title and I added “Which tweets are worth reading?” because it was really hard to interpret the graphs at first glance without a title. There is enough information for interpretation in the caption, but I think a caption should not stand in for a title.

The title is the first thing we see.
The graph is the second thing we see.
The caption is the third thing we see.
In order to understand the graph, then, it’s logical to have a title first so that readers’ don’t get frustrated that they have no idea what these colorful bars represent (the axes only get us halfway there in this case).

The title follows their own recommendations: questions work well as tweets. I figured I would try it here as a title, see what happens.

References

P. André, M. Bernstein, and K. Luther. (In press). “Who Gives A Tweet: Evaluating Microblog Content Value.” To appear in CSCW ’12: Proceedings of the 2012 ACM Conference on Computer Supported Cooperative Work. (Best Paper Award honorable mention; top 5% of submissions)

Spring Counter | John Maeda

What works

John Maeda (now head honcho at RISD, formerly of MIT’s Media Lab) designed this simple interactive graphic in 2006 while contemplating the cyclical nature of life during the still grey days of a New England winter. His visualization shows the number of springs men can expect to have if they live an average life span for men in their country. Users input their age and select their country. The flowers in color are those in the user’s future; the ones in grey represent the past. Simple. Elegant. An infographic haiku.

What needs work

I have a slightly sunnier view of the past than does Maeda, perhaps. I think I would have colored both the past and present flowers, just used different schemes. Maybe it’s the social scientist in me, but I believe our past and future both provide the context for our present. Perhaps some past years have been grey, but the territory of the past is not generally a cemetery.

References

Maeda, John. (2006) Life Counter. Interactive web-based graphic.

See also:
Maeda, John. (2006) The Laws of Simplicity. Cambridge, MA: MIT Press.

Crony Capitalism | Original by Stephanie Herman posted to lewrockwell.com/blog

What works

There is a lot of information here, that’s one of the best things about these Venn diagrams. People often stick a single word or a phrase in one circle, another in the next, and that’s it. But this graphic proves Venn diagrams can help organize much more detailed, drilled-down information fairly well.

What needs work

For the sake of legibility and small font sizes, I probably would have made one of the circles white instead of black, then left the colored one a color, and had the middle oval shape have a much lighter background. That might have helped make some of the text easier to read. In particular, I think it’s important to read the names themselves, so I would have worked to make sure they stood out.

I might have snugged the titles up to the curve. Their spacing is a little haphazard. Clearly, in a circular format, one cannot use a vertical margin line, but then that leaves a question about whether to mirror the shape of the circles on the outside or the ovaloid shape on the inside. I would have tried it both ways and then picked one. Not sure what happened here.

References

Herman, Stephanie. (2011) Venn diagram of Corporate Cronyism in America on geke.us

SOPA Opera in Congress

Graphic Sociology opposes PIPA/SOPA

The proposed PIPA and SOPA legislation has been roundly criticized with solid, logical arguments here by danah boyd, here by Tarleton Gillespie who links to 16 other authors who have argued against SOPA/PIPA, and here in a letter Harvard Law Professor Laurence Tribe sent to members of Congress.

As someone who is a passionate scholar of collaboration (both in its cooperative and competitive forms), I worry about the legal and economic repercussions of SOPA/PIPA that have been brought up by the authors mentioned above as well as the negative impact the threat of discretionary censorship would have on the kinds of sharing and borrowing that have made the internet and digital files such a rich source of remixing, incremental improving, and all around innovation. There is no way I could put a dollar figure or other empirical metric on what might happen to remix culture (what the cool kids call it) or innovation (what the business schools call it) under a legal regime in which just about anyone can censor just about anyone else. I can say that the internet as we know it would cease to exist. I post things here on Graphic Sociology that I have designed and created without even mentioning Creative Commons or standard copyright or anything else. If people take my work and get something out of it, that’s fantastic. I don’t even care if they give me credit, though many creative people do, and for good reason. I’m afraid if PIPA and SOPA were to pass, fewer people would re-mix my work and that’s the best kind of use, as far as I am concerned. Reposting is fine, remixing is divine.

I also post the work of others and critique them as an academic, something that is legal under the fair use doctrine. I’m not sure how SOPA and PIPA would mesh with the particular provision of the fair use doctrine that I am exercising. Presumably, they can co-exist, but I certainly don’t have the resources to hire a lawyer and defend myself against anyone who might claim that I’m violating SOPA/PIPA. And as just one of a family of bloggers, any infringement claim against any blog post on the society pages could darken the entire site. So if someone got upset with me, that would mean lights out for Sociological Images, Thick Culture, and all the rest of the blogs here.

The rest of this post is written by guest blogger Alec Campbell of Reed College in Oregon.

Guest post by Alec Campbell, Reed College

What works

This graphic clearly shows that something caused a change between January 18 and January 19. That something was almost certainly the focused attention on SOPA and PIPA resulting from shutdowns, blackouts and other actions taken or led by a number of popular Internet sites (wikipdedia, reddit, the Social Media Collective, and even here at thesocietypages there was a blackout of sorts).

What needs work

The most important flaw in this graph is that it excludes members of congress who are undecided or whose opinions are unknown. Looking at the graph it appears that the distribution of opinion moves from 72% in favor before the Internet shut down to 39% in favor after. In reality the distribution is 15% in favor, 6% opposed and 79% unknown/undecided before the shutdown and 12% in favor, 19% opposed and 69% unknown /undecided after the shutdown. The two graphs aren’t comparable because they don’t include the same total number of observations. When comparing populations of different sizes one has to compare percentages which this graphic does not do.

In fairness, the article accompanying this graphic has links to much more detailed data on SOPA
and PIPA
that does include a full accounting of all members of congress. However, those data don’t allow for comparison over time, which is the central point of this graphic.

What I can’t figure out

It’s clear that there are fewer supporters on January 19 but it isn’t clear if the people who no longer support PIPA/SOPA now oppose it or if they are now undecided. Did the Internet action make converts or agnostics? Arstechnica is keeping a running tally of Senators who are now opposing PIPA, including many of the former co-sponsors of the bill.

The graphic could have used arrows to show movement from one of the three camps (supporters, opposers, undecideds) to help illustrate where the movement happened.

Why it Matters

None of this matters much if our interest is in the fate of SOPA/PIPA. It matters a great deal if we are interested in the power of Internet protest. This graphic is about the power of protest because the prominently displayed time dimension is only relevant to this issue. This graphic overstates the power of Internet protests by omitting the unknown/undecided category making it appear that people changed their minds overnight. Clearly, some did. The number of supporters dropped in absolute terms. However, the larger effect is in convincing people to publicly state their opinion or to finally make up their minds. It is entirely possible that the major effect of the Internet protest was to get congresspersons that were leaning towards opposition to publicly state their opposition and force some supporters to claim an undecided status. That is certainly something but it isn’t the same thing as changing supporters into opponents, which is what the graphic implies.

References

propublic.org following House support of SOPA

propublic.org following Senators support of PIPA

Lee, Timothy. (19 January 2012) PIPA support collapses, with 13 new Senators opposed. [blog post] arstechnica