I’m happy to announce my newest productivity software: Anti-Social. Anti-Social is a neat little productivity application for Macs that turns off the social parts of the internet. When Anti-Social is running, you’re locked away from hundreds of distracting social media sites, including Facebook, Twitter and other sites you specify.
I developed Anti-Social because of a problem I ran into consistently with Freedom – I loved being offline, but found myself frustrated when I needed to look up a citation or a new article when Freedom was running. Anti-Social allows you to tune out the social parts of the web – Twitter, Facebook, etc. – while allowing you access to research materials, Google, and other invaluable resources. I’ve been using it for the past few weeks while working on an R&R – Anti-Social allowed me to remain in focused writing mode, while allowing me to research as I revised the manuscript.
Together, Freedom and Anti-Social represent an emergent computing phenomena I’ve been calling “80% computing.” By taking problems that are socially or computationally hard (e.g. changing habits, reducing compulsive surfing), and providing imperfect solutions, I’ve found there’s an interesting spot in the market. I wonder what other highly complex problems (e.g. productivity) we could solve with 80% solutions? If we move away from perfection as a computational standard, and allow individuals to adapt their practice to imperfect technologies, we may be able to develop some very simple solutions to very challenging problems.
Along those lines, the Economist recently profiled my software in a wonderful article. I’ll quote at length:
“CLEAR your screen and clear your mind.” That is the philosophy behind a new wave of dedicated software utilities, and special modes in word-processing packages and other applications, that do away with distractions to enable you to get on with your work. The problem with working on a computer, after all, is that computers provide so many appealing alternatives to doing anything useful: you can procrastinate for hours, checking e-mail, browsing social-networking sites or keeping up with Twitter.
But in its severity and simplicity, Freedom (for Macintosh and Windows) may be the ultimate tool to ward off distractions: the virtual equivalent of retiring to a remote getaway, or going on a writers’ retreat, to get things done.
But fans of Freedom are not concerned by such philosophical niceties; they use it because it makes them more productive. Peter Sagal, the host of the American public radio show “Wait Wait…Don’t Tell Me!”, is one such fan. He has no trouble writing to a strict deadline at work. But outside work, “I simply can’t resist the call of a website or an RSS feeder or now my Twitter feed. I simply can’t do it,” he says. Before he started using Freedom he managed to write a book, but only by unplugging his cable modem to cut off his internet access. “But that was too easy to plug back in,” he says. The internet, he grumbles, has “murdered” his ability to do extracurricular creative work, such as writing books, plays and screenplays.
Hardware and software are usually sold on the basis that they can do more, do things faster or have whizzy new features. There is clearly a place for products that are simple to use and hide complexity—a hallmark of Apple’s products. It is perhaps more surprising that there also seems to be demand for products that disable features. But for people trying to get things done, a hobbled computer may in fact be more useful than a fully functional one, for an hour or two at least. Temporarily worse can, in some ways, be better.
Of note, the New York Post also ran an article that prominently featured Freedom and Anti-Social. The title of the article was a classic Post headline: Fatal Distraction.
I should close with the following. First, I am aware that spending time writing anti-procrastination software is actually meta-procrastination. Second, Anti-Social really is great. Check it out. It is a revelation to be on the un-social Internet. Finally, I’m waiting for Peter Sagal to come and ask me for a percentage of my sales. He is simply too kind with his advocacy of Freedom!
Last week, I had the pleasure of attending the 2010 Political Networks Conference. The first day of the conference included workshop sessions led by Matthew Jackson and Carter Butts, two eminent networks researchers. Both are now online.
The lecture by Carter Butts will be of particular interest to individuals looking to use R for social network analysis. Butts is the author of a number of network analysis packages for R (many of which come bundled in the amazing statnet package).
Network Analysis with statnet for Individual, Organizational, and International Relations Applications by Carter Butts, University of California-Irvine
Advanced Network Analysis by Matthew O. Jackson, Stanford University
If you find these materials useful, you might also wish to check out Steve Goodreau and David Hunter’s tutorial Advanced Social Network Analysis Using R and statnet available at the Complexity and Social Networks blog.
Last month, Facebook announced a number of new features, including “personalization” (which generated significant controversy) and “social plugins.” The plugins are described as follows:
Social plugins let you see what your friends have liked, commented on or shared on sites across the web. All social plugins are extensions of Facebook and are specifically designed so none of your data is shared with the sites on which they appear.
According to Mashable, over 50,000 plugins have been installed since the rollout. Seeing one’s Facebook friends suddenly start showing up on third party sites has raised privacy concerns, which Facebook quickly addressed in a blog post, stating “Because [third party sites] have given Facebook this “real estate” on their sites, they do not receive or interact with the information that is contained or transmitted there.”
Here’s the rub. By giving “real estate” to Facebook, third party sites have created a one-way mirror, allowing Facebook to peer in on what we’re doing. If you’re logged in to Facebook, and you visit a third party page with a social plugin, Facebook knows where you’ve been. The mechanism is simple – cookies and referrals – and it will allow Facebook to create personalized behavioral profiles that, combined with the information we articulate in Facebook, will be tremendously valuable.
To explore the privacy implications of Facebook’s social plugins, I visited the websites of the top 15 U.S. online news destinations (based on some 2009 Nielsen data), and a few honorable mentions. I then selected a news story from the front page, and loaded the page. I checked to see if social plugins were enabled, if the Facebook cookie was called, and if the referring page was sent to Facebook (basically, did the site identify you to Facebook, and share the page you were on).
I found that of the top 15 online news destinations, 9 were sharing information with Facebook (MSNBC, CNN, CBS, ABC, Fox News, Washington Post, and the Tribune, McClatchy and Gannett Companies). Notably, The New York Times, BBC, Yahoo News, AOL News, and Google News did not share information. I then checked a few favorites of mine: NPR (yes), Drudge (no), Huffington Post (yes), and Politico (no). I’ve included all of the details on a spreadsheet, embedded below or html version.
According to Nielsen, the 9 news organizations sharing information with Facebook account for over 177,161,000 monthly unique visitors. Granted, not all of these views will go to social plugin enabled pages, and not all visitors will be logged-in Facebook users. But with 400 million users, it is safe to assume that a substantial proportion of that information will go to Facebook. If you stay logged in to Facebook, it is increasingly likely that Facebook will know what news you read.
My beef here isn’t necessarily with Facebook; Google and other behavioral-targeting firms have very similar SOP’s. Rather, I’m uncomfortable that so many news organizations felt comfortable sharing the news-reading behaviors of their customers that just so happen to be logged in to Facebook. And really, what do they get for trading this tremendously valuable asset? I get to see that a random friend liked an article?
I think it is time that someone wrote a Firefox plugin that specifically manages the Facebook cookie, only allowing it to be accessed when someone is on Facebook proper. Clearly, we can’t trust third parties – even reputable news organizations – to protect our data. Here’s the spreadsheet from my analysis:
Note: For media conglomerates (Tribune, McClatchy, Gannett) I visited the flagship outlet (Chicago Trib, Sac Bee, and USA Today, respectively).
A few days ago, I stumbled upon a post from the blog Business Insider that asked “Why Is Twitter More Popular With Black People Than White People?” Drawing on data from Edison Research, the writer proposed a number of explanations for why “black people represent 25% of Twitter users, roughly twice their share of the population in general.” This factoid has now been reported by the New York Times, the San Francisco Chronicle, The Atlantic, as well as a number of prominent blogs. It’s also going viral in the Twittersphere.
I’m loathe to trust bloggers getting survey data right, so I requested a copy of the report from Edison Research (available here). At first glance, the data looks good – the research was conducted by Arbitron, it employs a landline/mobile random digit dialing (RDD) frame, with about 1,750 people age 12 and older interviewed. “National probability” studies of this sort are generally considered valid for population estimates.
Without getting into too much detail, a study’s validity is dependent on the sampling method and sample size (among many other things). In terms of method, RDD is not a true equal-probability of selection method, but both industry and academia consider it “good enough” when the sample is weighted to known totals. As for size, a sample of 1750 people allows us to make claims about a large population at an error rate of about plus or minus 3 percent.
Let’s cut to the chase: Where did the Edison Research interpretation go wrong? In the report, Tom Webster states:
The percentage of Twitter users who are African-American currently stands at roughly 25%, which is approximately double the percentage of African-Americans in the current U.S. population. Indeed, many of the “trending topics” on Twitter on a typical day are reflective of African-American culture, memes and topics.
From this, we are to believe that of all Twitter users, 25% are African-American. Not only is this surprising considering current population estimates, but also because Twitter is a global service. Let’s explore how Edison got to this 25 percent number (conveniently rounded up from 24 percent).
In the phone interview, Edison asked all respondents 12+ (n=1750) if they “currently ever use[d] Twitter.” 7% of respondents said yes, approximately 123 people. Of those 123, Edison then asked how often they used Twitter. 85% of those respondents (105 people) indicated they used Twitter at least once a month, and were thus recoded as “Monthly Twitter Users.” Herein lies the problem: It was from these 105 individuals (not the 1750 total respondents) that Edison based its estimates of Twitter use.
Let’s return to sampling error. Because random samples are asymptotically efficient, a sample of 1750 can speak to a population of hundreds of millions almost as well as a sample of 2000, 3000, or even 5000. But a sample of 105 people speaking to the very large userbase (self reported at 100 million) of Twitter? Not so efficient. The margins of error are approximately +/- 10% at an alpha of .05, +/- 12.5 at an alpha of .01. And these margins assume true equal probability of selection, and no nonresponse bias. With weighting for proportionality, it is almost certain these margins increase substantially (1).
Let’s explore what this means practically. First, Edison Research can’t speak to all Twitter users, because all Twitter users weren’t potentially included in the sample. Edison can, however, speak to USA Twitter use, from its sample of 105 monthly users. If we assume that only 5 million Twitter users in the USA use the service every month, Edison is still using 105 people to speak about these 5 million people (the margins of error don’t change). Unfortunately, this is highly unreliable.
The American Community Survey finds that approximately 13.1% of the US population self identifies as Black or African American. At an alpha of .05, the range of potentially true estimates of African-American Twitter use in the US is actually anywhere from 14% to 34%. At an alpha of .01, this estimate ranges anywhere from 11% to almost 38%, causing us to reject the hypothesis that the estimate is not attributable to sampling error or random effects. If we then include weights in our estimates of error (likely the case because Edison’s sample over-represents people under 24), the growth in error causes us to fail to reject the null hypothesis at the .05 level as well. We just can’t trust that the demographics of Twitter actually do vary from current population estimates.
Is Twitter “disproportionately” African American, White, Hispanic, or Green? The simple fact is that from this data, we can’t say so with confidence. If Edison had been a little more forthcoming with their sample sizes, it might be more likely that the blogger/journalist who reported these data would have sensed something wrong. But I wouldn’t bank on it, because it seems like Edison Research was pushing this spin from the get-go.
A final note: as I was researching/considering this piece, it was interesting to see the “spin” being placed on this “fact” around the blogosphere. Of course, you had your standard racist comments/tweets of the “there goes the neighborhood” variety, but there also appeared to be a large swath of users who were heralding this as a point of pride. Before you examine my subconscious racist motives for examining this question, please just know I like getting surveys right. And if Edison wanted to get this right, they could start by giving us a topline cross-tab of ethnicity, Twitter use, and the respective margins of error.
Ugh, footnotes on a blog!
1. Research consistently demonstrates a negatively correlated relationship between age and nonresponse; young users are more likely to under-respond, increasing their odds of being weighted in a population (and increasing their margins of error). Research is mixed on the relationship between ethnicity and nonresponse.