Personal Informatics to Encourage Diversity in News Reading

Today, people can choose from among more news sources than ever, some of which cater to particular ideological niches or which highlight items popular in one’s social network. Scholars and pundits express concerns this technology could reinforce people’s tendency to read predominantly agreeable news, through their own choices and the choices made by system designers. While this might increase short-term engagement and help people feel validated, it may not further either individual or societal goals. Reading broadly can further learning and out-of-the-box thinking. Individuals who are aware of other viewpoints can better communicate and empathize with those who disagree.

Despite theories that predict a preference for reading agreeable political news, many people appear to agree with the norm of reading diverse viewpoints, and at least some people actively prefer it. Colleagues and I were curious whether a personal informatics tool could help people identify when their behavior is inconsistent with this norm and help them take corrective action.

Balancer extension example

The Balancer extension gives readers feedback on the political lean of their online newsreading.

To test this, we built Balancer, an extension for the Chrome web browser. Balancer enables users to see patterns in their behavior and also reminds them of the norm of balance, the form a character on a tightrope. When the user’s newsreading is balanced, the character is happy; when the user’s newsreading is not, the character is in peril of falling.

In a one-month, open-enrollment controlled field experiment, this extension encouraged participants with unbalanced reading habits to make small but measurable changes in the balance of their newsreading. Compared to a control group, users receiving feedback from Balancer made 1-2 more weekly visits to a website with predominantly opposing views or 5-10 more weekly visits to a site with more neutral views.

We are working to improve its capabilities, and others are also making progress in this space. There are now many browser extensions and other tools that give people feedback about the news they read and sources they follow. ManyAngles recommends articles that cover different aspects of the topic about which a user is currently reading. Slimformation reveals topical diversity in one’s online news-reading. Scoopinion givers users feedback on their top authors, sources, and genres. Follow Bias shows people the gender (im)balance of their Twitter network. This is an exciting time for tools that help readers reflect on the news they read!

For more, see our full paper, Encouraging Reading of Diverse Political Viewpoints with a Browser Widget.

Sean A Munson, Human Centered Design & EngineeringDUB Group, University of Washington
Stephanie Y. Lee, Sociology, University of Washington
Paul Resnick, School of Information, University of Michigan

This entry was posted in ICWSM 2013 by Sean Munson. Bookmark the permalink.

About Sean Munson

Sean is an Assistant Professor at the University of Washington's Department of Human Centered Design and Engineering and a member of the dub group. He studies the use of software to support positive behavior changes. His work focuses on the domains of political news access and health and wellness. Sean completed a BS in Engineering with a concentration in Systems Design at Olin College in 2006 and his PhD at the University of Michigan's School of Information in 2012. He has been a political blogger and, while working at Boeing, designed concepts for future passenger airplane interiors.

3 thoughts on “Personal Informatics to Encourage Diversity in News Reading

  1. I haven’t read the paper yet, but I’m very skeptical. Until you have a tool that can actually detect propaganda and separate it from evidence-based writing, you’re merely ‘measuring’ *whose* propaganda your users are consuming and how much.

  2. Very interesting idea with a lot of potential! Do you have a sense for how the switch to the URL approach instead of content analysis impacted the validity of the liberal vs conservative bias estimation?

  3. Hi Ed – yeah, we don’t make any claims to the information quality or credibility. That’s a different problem. The extension just reports on the lean of the sources, not the

    Joel, good question. My sense is that for most users, the errors more or less cancel in aggregate. There are some, however, for whom the errors add up and are considerably worse: for example, someone who reads political in right-leaning sources only but who follows lots of sports news from a left-leaning or centrist local paper would get feedback that they are a lot more balanced than they are. I’d like to get to a point where we use content analysis (or more precise URL classifiers) to at least filter out articles on some topics (sports, celebrities, entertainment) so they do not influence the user’s score one way or the other.

Comments are closed.