Exploring the world of social media through the lens of political research.


#WikipediaProblems: How Do You Classify Everything?

How do you organize all the world’s information?

A decision made by the editors in charge of Wikipedia’s newest, biggest project reveals the difficulty of such a task.

Wikidata is the newest project from the Wikimedia Foundation, the organization which runs Wikipedia. As Becca wrote here last year, Wikidata promises a single, shared infrastructure of knowledge beneath Wikipedia in every language. This underlying data layer, which is Wikidata, can be read by both humans and machine, and it propagates changes from one language’s version of Wikipedia to other languages. If Canada, for instance, gets a new finance minister, and someone edits Wikipedia in English to reflect that change, then Wikidata will propagate that information to Wikipedia in other languages.

That’s a relatively straightforward use of Wikidata, though. Its promise is machine analysis of the Wikipedia body of knowledge in a complex, ongoing, holistic way — and for computation like that, its macro-organizational system matters.

Imagine a live visualization of the entire Wikipedia system, organized partly by the subject matter of article. What system needs to exist to make that possible?

Read more. [Image: Glyn Lowe]

Gender stereotypes mean that voters look for more information on women candidates’ competence than they do for men. →

Loved reading this article from the London School of Economics on research coming out of Iowa State University and Rutgers University on how gender stereotypes influence voters’ perceptions of women candidate. 

The researchers’ focus on how gender stereotypes impact voters’ approach to information gathering is fascinating, and may help explain why previous research has been so conflicted:

We argue that in order to better understand the relationship between candidate gender, voters’ attitudes toward women political candidates, and electoral outcomes, we have to consider the importance of information. In other words, before they can form evaluations and decide whom to vote for, voters have to search for and encounter information about the candidates in a particular race. Only after gathering and processing a sufficient amount of information, can they reach any sort of conclusions about a candidate. At the same time, if voters come to the table with assumptions about women candidates in general, those stereotypes will likely influence the type and amount of information they seek out about the particular women running in a given race. These differences in search patterns could then ultimately influence vote choice.  Rather than a direct relationship between candidate gender and vote choice, then, we argue that gender has an indirect effect on voting through information search.

From my own research (years ago now, for an honors thesis project), I’d be curious to see how these search patterns play out beyond candidates’ websites. What do these patterns look like in other online realms? Facebook? Twitter? And how do candidates anticipate or respond to these voter behaviors and biases through communications, website design, branding, etc.?


The 11 Most Fascinating Charts From Mary Meeker’s Epic Slideshow of Internet Trends

See more. [Images: KPCB]

Honestly, the biggest surprise continues to be that people are still on MySpace… #why


We Promise Not to Screw

Quick, someone teach the Yahoo social team how to use the Tumblr Twitter box. STAT.

Image: Automated tweet from Yahoo’s Tumblr to Yahoo CEO Marissa Mayer’s Twitter account.


13% of internet users ages 18-29 use Tumblr.

Compare Tumblr user demographics to other social networking sites: http://pewrsr.ch/VBAYby

Interesting to see the buzz around the web (mostly negative) about Yahoo’s acquisition of Tumblr. Most of the commentary centers around something that seems pretty obvious: if Yahoo hasn’t gotten the core of its services perfected (i.e. mail, news, etc.), will buying a shiny new platform really make a difference?