Get Off Your Twitter

August 25th, 2017

Web Science, more so than many other disciplines of Computer Science, has a special focus on its humanist qualities – no surprise in that the Web is ultimately an instrument for human expression and cooperation. Naturally, lots of current research in Web Science centers on people and their patterns of behavior, making social media a potent source of data for this line of work.

 

Accordingly, much time has been devoted to analyzing social networks – perhaps to a fault. Much of the ACM’s Web Science ‘17 conference centered on social media; more specifically, Twitter. While it may sound harsh, the reality is that many of the papers presented at WebSci’17 could be reduced to the following pattern:

  1. There’s Lots of Political Polarization
  2. We Want to Explore the Political Landscape
  3. We Scraped Twitter
  4. We Ran (Sentiment Analysis/Mention Extraction/etc.)
  5. and We Found Out Something Interesting About the Political Landscape

Of the 57 submissions included in the WebSci’17 proceedings, 17 mention ‘Twitter’ or ‘tweet’ in the abstract or title; that’s about 3 out of every 10 submissions, including posters. By comparison, only seven mention Facebook, with some submissions mentioning both.

 

This isn’t to demean the quality or importance of such work; there’s a lot to be gained from using Twitter to understand the current political climate, as well as loosely quantifying cultural dynamics and understanding social networks. However, this isn’t the only topic in Web Science worth exploring, and Twitter certainly shouldn’t be the ultimate arbitrator of that discussion. While Twitter provides a potent means for understanding popular sentiment via a well-controlled dataset, it is still only a single service that attracts a certain type of user and is better for pithy sloganeering than it is for deep critical analysis, or any other form of expression that can’t be captured in 140 characters.

 

One of my fellow conference-goers also noticed this trend. During a talk on his submission to WebSci’17, Holge Holtzmann, a researcher from Germany working with Web archives, offered a truism that succinctly captures what I’m saying here: that Twitter ought not to be the only data source researchers are using when doing Web Science.

 

In fact, I would argue that Mr. Holtzmann’s focus, Web archives, could provide a much richer basis for testing our cultural hypotheses. While more old school, Web archives capture a much, much larger and more representative span of the Web from it’s inception to the dawn of social media than Twitter could ever hope to.

 

The winner for Best Paper speaks directly to the new possibilities offered by working with more diverse datasets. Applying a deep learning approach to Web archives, the authors examined the evolution of front-end Web design over the past two decades. Admittedly, I wasn’t blown away by their results; they claimed that their model had generated new Web pages in the style of different eras, but didn’t show an example, which was underwhelming. But that’s beside the point; the point is that this is a unique task which couldn’t be accomplished by leaning exclusively on Twitter or any other social media platform.

 

While I remain critical of the hyper-focus of the Web Science community on social media sites – and especially Twitter – as a seed for its work, I do admire the willingness to wade into cultural and other human-centric issues. This is a rare trait in technological disciplines in general, but especially fields of Computer Science; you’re far more likely to read about gains in deep reinforcement learning than you are to read about accommodating cultural differences in Web use (though these don’t necessarily exclude each other). To that point, the need to provide greater accessibility to the Web for disadvantaged groups and to preserve rapidly-disappearing Web content were widely noted, leaving me optimistic for the future of the field as a way of empowering everyone on the Web.

 

Now time to just wean ourselves off Twitter a bit…

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