February 18, 2018

Political Information Overload and the New Filtering

[We’re pleased to introduce Luis Villa as a guest blogger. Luis is a law student at Columbia Law School, focusing on law and technology, including intellectual property, telecommunications, privacy, and e-commerce. Outside of class he serves as Editor-in-Chief of the Science and Technology Law Review. Before law school, Luis did great work on open source projects, and spent some time as “geek in residence” at the Berkman Center. — Ed]

[A big thanks to Ed, Alex, and Tim for the invitation to participate at Freedom To Tinker, and the gracious introduction. I’m looking forward to my stint here. — Luis]

A couple weeks ago at the Web 2.0 Expo NY, I more-or-less stumbled into a speech by Clay Shirky titled “It’s Not Information Overload, It’s Filter Failure.” Clay argues that there has always been a lot of information, so our modern complaints about information overload are more properly ascribed to a breakdown in the filters – physical, economic, and social- that used to keep information at bay. This isn’t exactly a shockingly new observation, but now that Clay put it in my head I’m seeing filters (or their absence) everywhere.

In particular, I’m seeing lots of great examples in online politics. We’ve probably never been so deluged by political information as we are now, but Clay would argue that this is not because there is more information- after all, virtually everyone has had political opinions for ages. Instead, he’d say that the old filters that kept those opinions private have become less effective. For example, social standards used to say ‘no politics at the dinner table’, and economics used to keep every Luis, Ed, and Alex from starting a newspaper with an editorial page. This has changed- social norms about politics have been relaxed, and ‘net economics have allowed for the blooming of a million blogs and a billion tweets.

Online political filtering dates back at least to Slashdot’s early attempts to moderate commenters, and criticism of them stretches back nearly as far. But the new deluge of political commentary from everyone you know (and everyone you don’t) rarely has filtering mechanisms, norms, or economics baked in yet. To a certain extent, we’re witnessing the birth of those new filters right now. Among the attempts at a ‘new filtering’ that I’ve seen lately:

  • The previously linked election.twitter.com. This is typical of the twitter ‘ambient intimacy‘ approach to filtering- everything is so short and so transient that your brain does the filtering for you (or so it is claimed), giving you a 100,000 foot view of the mumblings and grumblings of a previously unfathomably vast number of people.
  • fivethirtyeight.com: an attempt to filter the noise of the thousands of polls into one or two meaningful numbers by applying mathematical techniques originally developed for analysis of baseball players. The exact algorithms aren’t disclosed, but the general methodologies have been discussed.
  • The C-Span Debate Hub: this has not reached its full potential yet, but it uses some Tufte-ian tricks to pull data out of the debates, and (in theory) their video editing tool could allow for extensive discussion of any one piece of the debate, instead of the debate as a whole- surely a way to get some interesting collection and filtering.
  • Google’s ‘In Quotes’: this takes one first step in filtering (gathering all candidate quotes in one place, from disparate, messy sources) but then doesn’t build on that.

Unfortunately, I have no deep insights to add here. Some shallow observations and questions, instead:

  • All filters have impacts- keeping politics away from the dinner table tended to mute objections to the status quo, the ‘objectivity’ of the modern news media filter may have its own pernicious effects, and arguably information mangled by PowerPoint can blow up Space Shuttles. Have the designers of these new political filters thought about the information they are and are not presenting? What biases are being introduced? How can those be reduced or made more transparent?
  • In at least some of these examples the mechanisms by which the filtering occurs are not a matter of record (538’s math) or are not well understood (twitter’s crowd/minimal attention psychology). Does/should that matter? What if these filters became ‘dominant’ in any sense? Should we demand the source for political filtering algorithms?
  • The more ‘fact-based’ filters (538, inquotes) seem more successful, or at least more coherent and comprehensive. Are opinions still just too hard to filter with software or are there other factors at work here?
  • Slashdot’s nearly ten year old comment moderation system is still quite possibly the least bad filter out there. None of the ‘new’ politics-related filters (that I know of) pulls together reputation, meta-moderation, and filtering like slashdot does. Are there systemic reasons (usability, economics, etc.?) why these new tools seem so (relatively) naive?

We’re entering an interesting time. Our political process is becoming both less and more mediated– more ‘susceptible to software’ in Dave Weinberger’s phrase. Computer scientists, software interaction designers, and policy/process wonks would all do well to think early and often about the filters and values embedded in this software, and how we can (and can’t) ‘tinker’ with them to get the results we’d like to see.