Scope: 6 weeks

Categories: UX Research, UX Design

Role: Solo project. Conducted user interviews, created wireframes and prototypes

Tools: Sketch, Princple, Omnigraffle

The Solution

My DESIGN PROCESS

The Problem In Detail

Users tended to seek out information that strengthened their preferred narratives and to reject information that undermined it. Alarmingly, when deliberately false information was introduced into these echo chambers, it was absorbed and viewed as credible as long as it conformed with the primary narrative. - Washington Post

Although confirmation bias is not a new concept, in the age of social media as a source of news, it can be even more augmented. Especially in today's turbulent political climate, it is even more important to read across the aisle and have open discourse with "the other".  My belief is that if we can understand why someone's political views do not match with us, it will be easier to empathize with the other side.

If we unpack this problem into specific user needs, we see that they need to do the following: 

• Need to read literature on both sides of the major political issues

• Need to know what political candidates they align with

• Need convenient access to this data quickly and easily

LITERATURE REVIEW

There is a wide range of papers that analyze the effective of confirmation bias. What was particularly interesting was how this bias was augmented and manipulated by social media feeds. The most blatant example is Facebook, which includes many instances of 'fake news', which are claimed to have swung the election of Donald Trump. 

A 2015 study suggested that more than 60% of Facebook users are entirely unaware of any curation on Facebook at all, believing instead that every single story from their friends and followed pages appeared in their news feed. - The Guardian

After the first iteration of wireframes, I discovered the Pew Research Center's political typology classifications.  The key takeaway from this research was that political affiliation was more nuanced than just conservative and liberal. 

INTERVIEWS

Throughout this project, I conducted interviews with millennial American citizens who were registered to vote. Out of these users, all of them used smart phones, and more than 80% were heavy social media users. Of these interviewees, roughly half of them associated as conservative, while the other half described themselves as liberal. 

COMPETITIVE ANALYSIS

In the competitive analysis, I focused my research on news aggregation applications that are most regularly used. Most of these platforms covered a wide range of articles, not only political news. More interestingly, most of these applications actually curated the news based on what the app's algorithms expected the user to like and agree with. This meant that they were much more likely to be shown information to further affirm their point of view. 

The USER

I focused on a user that was a young and on-the-go young millennial American voter. This user is a heavy social media user who does not have a lot of time to sit down and read the news. She is constantly multitasking, has no single source of aggregate news, and wants to be informed and up to date on the current political topics. 

AFFINITY MAPPING

From an observation clustering exercise, we were able to distill several key learnings: 

1. Efficiency, speed, and convenience are necessary for a good political news app.

2. People tend to look at the news while multitasking (i.e. on the commute to work).

3. People are more inclined to gather news to affirm their existing bias.

4. People prefer visual and graphic content over large blocks of text.

5. People tend to treat those with opposing political views as "the other" and unreasonable.

CARD SORTinG

From our open card sort, I learned that not everyone I interviewed knew what right leaning and left leaning meant, so the labelling and terminology on the app needs to be extremely clear. I'm working under the assumption that my users will have somewhat a working knowledge of the American political system. There was also confusion on where moderate articles might be placed. 

USER FLOW

In the original user flow, the user is prompted by a daily summary of political news. They have the option to drill down into more specific topics, and then consequently read left leaning and right leaning articles which are pertinent to those issues. Based on how they rate the article, the app will assess the political leanings of the user.  

sketchING AND IDEATION

INFORMATION ARCHITECTURE

WIREFRAMES

We created the following wireframes to make our ideas more concrete, and to make sure that the Information Architecture made sense.

USABILITY TESTING

In our usability testing, we came across the following issues. 

ISSUE 1 - TOPIC TAB TOO BINARY

When asked to navigate to a left leaning article, users were often confused. Many asked if I wanted an article that was more moderate or radical. From this I realized that having things classified as only Republican or Democratic was too binary. 

This prompted me to do more user research on political typologies. From this research, I found a classification created by the Pew Research Center which defined the American public into more nuanced political typologies

ISSUE 3  - profile tab

When asked to share their profile with their friends, many users were uncomfortable with having their political affiliations open to their social networks. 

NEXT STEPS & LEARNINGS

In terms of next steps, I would like to focus on the accessibility considerations of my application, as well as the logistics of how to classify articles. Initially, I would like to classify the articles by the publication that they come from. However, this may not be entirely accurate. For example, there are some opinion pieces that may not fall under the right category.

From this project, I was really able to learn how to be mindful about accessibility considerations, as well as be more thoughtful about friction and why I receive certain types of user feedback.