OpenNews is an app that provides opposing news sources to help users better understand and empathize with those who have opposing views.

Scope: 4 weeks
Methods: UX research, UX design
Role: Solo project. Conducted user interviews, created wireframes and prototypes.
Tools: Sketch, Princple, Omnigraffle


The 2016 U.S. presidential election was one of the most polarizing elections the country has ever seen. Even though the American public is more connected than ever, thanks to social media, much of the populace exists in sharply contrasting echo chambers. These silos further separate people with differing views, and make it difficult for people to hear other perspectives. This reality leads me to ask, “How can Americans communicate better with those who have differing political views?”

“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, it can be even more augmented in the age of social media. Especially in today's turbulent political climate, it is even more important to read across the aisle and have open discourse with people with opposing viewpoints  My belief is that if we can understand why someone's political views do not match with ours, it will be easier to communicate with the other side in a targeted, empathetic way.


To approach this issue, I designed OpenNews, a news aggregation app that displays news articles on major political issues. Based on the user's regular reading habits, the app provides opposing news sources to help the user better understand and empathize with those who have opposing views.

Process Overview

OpenNews Process


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

  • Read literature on both sides of major political issues

  • Know what political candidates they align with

  • Have convenient access to this data quickly and easily


There is a wide range of papers that analyze the effectiveness of confirmation bias. What is particularly interesting is how this bias is augmented and manipulated by social media feeds. The most blatant example is Facebook, which includes many instances of “fake news,” which many claim to have influenced the 2016 U.S. presidential election.

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

“Everything’s just so biased these days, so I just kind of get my news from social media because I don’t know how to pick my own news. I get that my friends all have the same perspectives, but it’s just convenient.
—Anonymous interviewee


Throughout this project, I conducted interviews with American citizens from ages 23–58 who were registered to vote. Out of these users, all of them used smartphones, and more than 80% described their social media use as “very frequent.” Of these interviewees, roughly half of them associated as conservative, while the other half described themselves as liberal. 

Key takeaways from these interviews were that these people felt there was no reliable source that they could comfortably go to when they read the news. They did not have much time to read the news, and often relied on their social media feeds to get breaking news. Most were not surprised to hear that there were specific Facebook algorithms which influenced their news feeds, however most did not do anything actively to combat this. 

Competitive Analysis


For the competitive analysis, I focused my research on popular news aggregation apps. Most of these platforms cover a wide range of articles, beyond political news. More interestingly, most of these applications curate the news based on what the app's algorithms expect the user to like and agree with. This means that they are much more likely to be shown information that further affirms their points of view. 



Affinity Mapping

From an observation clustering exercise, I was able to gain several key learnings: 

  1. Efficiency, speed, and convenience are critical for an effective political news app.

  2. People tend to look at the news while multitasking, such as while commuting to work.

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

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

  5. People tend to view those with opposing political views as unreasonable.

User Persona

With my new learnings, I was able to create a user persona to help me consistently remain user-focused. I centered on Jessa, a user that is a moderately politically involved American voter. Jessa 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 consistently up-to-date on current political topics.

Persona Template-6.png

User Workflow

Based on my research and synthesis, I mapped out a proposed ideal user workflow for Jessa. The workflow illustrates how she would move from the initial notification to reviewing a read article’s stats.



Information Architecture

For apps where content discoverability is key, a clear information architecture is critical. Here, I mapped out the basic organization of the app, including top-level screens and where users will land if they click into them. I sought to ensure that the process of finding content remains simple and straightforward, no matter where the user is in the app.



I created the following wireframes to make my ideas more concrete, and to make sure that the information architecture made sense.


I tested these initial designs with 22 users, asking them to complete a variety of tasks related to content discovery. During usability testing, I observed the following issues. Then I revised the wireframes based on user feedback.


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 articles classified as only “conservative” or “liberal” was too binary. 

This prompted me to do more user research on political typologies. From this research, I found the Pew Research Center's political typology classifications, which define the American public into more nuanced political typologies.

Solution: Add a sliding toggle

In order to address this issue, I replaced the swiping left and right for a sliding toggle bar instead. That way, the user can more accurately define the level at which an article is leaning left or right. In addition, this solution gives room for moderate and more bipartisan articles to live within the app. 

This toggle's design also made its way into the rating scale of the articles. Originally, the user could only indicate if they strongly agreed or disagreed with an article. 

Before and after:

Issue 2: No Consensus on Home Screen Content

There was no consensus on what content should be displayed on the home screen. It was difficult to ensure that there was mirroring content on both sides. Additionally, some interviewees who associated as conservative commented about the placement of the summaries. It would be difficult to put one summary over the other while keeping the application unbiased.

Solution: Remove daily digest

I decided to remove the digest function altogether. Instead of a summary, the user is just recommended the top trending news articles, ensuring that there is a healthy mix of articles. This way, there are fewer interactions and clicks necessary to get to the desired content.

Before and after:

Issue 3: User Discomfort with Public Political Preferences

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

Solution: Remove comparison to social networks

I removed the functionality to compare users’ profiles with their social networks. Instead, users can compare to the general American public or to their state or district. 

Before/before and after/after:

Issue 4: Limited use of colors

Because so much of the application is reliant on colors to indicate the political leaning of the content being shown, there was a limit in the colors that could be used in the app. The majority of users provided negative feedback about the colors used, stating that they looked “boring” and “drab.”

Solution: Introduce neutral colors while ensuring red and blue still stand out

To provide a more visually interesting experience, I decided upon a minimalistic interface that uses mostly neutral colors. That way the red and blue colors would stand out, indicating if the content is right- or left-leaning. However, taking accessibility into mind, I am still working on a solution that does not rely so heavily on color.

Before and after:


Final Prototypes

With my new insights, I created high-fidelity wireframes to bring the concepts to life.

Upon entering the application, users view feed of recommended articles. Unlike other social media feeds, which use algorithms to show users what they want to see, the application will give users a mix of articles from a variety of different sources across the political spectrum.

Users can filter content based on political issue. Many of the articles cover topics that are hotly debated in politics, such as healthcare and gun control. For users who are not knowledgable about a topic, there will be a quick bipartisan summary of the issue.


By toggling a dial left and right, users can filter the articles based on the political affiliation of the publication or author. If users pull the dial left, more left-leaning, more liberal articles will rise to the top of the list. As users pull the dial toward the right, more conservative articles will show up.

Originally, the interaction was to swipe right or left. However, this approach was too binary. Since the spectrum of political beliefs is more fluid, and there are many more moderate news sources, I accounted for this spectrum by using a sliding toggle.


Through my research, I found that the biggest complaint of my demographic was that they often didn't have time to read articles, especially when in transit. So I added functionality to allow users to bookmark articles to read later.

Users will read articles from different news sources through the app. At the end of every article, the user will be able to determine how much they agree or disagree with the sentiment of the content. This will help the app to understand which perspectives the user aligns with, and which would best represent their values.


In the profile section, users will be able to see user details and how they stack up with others based on their app activity. Since the political spectrum can be very fluid, the user will be given a political typology that they fit in the most. These classifications are from a Pew Research study done with 5,000 American voters.


During my research, one issue that many of my users faced was being well informed about the stances of the political candidates that were running. This was especially difficult for local candidates. To be truly informed voters, users can see how congressional and local candidates stand more specifically on the issues users care about.

Additionally, the app will remind users of upcoming elections so that the user will remember to vote.


Users can see a detailed breakdown of how they fall on the specific political topics. This gives a more comprehensive analysis of their stances on specific issues.


Users will also be able to compare how their political views compare to the general American public. Originally, users were given the ability to compare with their social networks. However, many users were uncomfortable with sharing their political leanings with their social networks.

Next Steps and 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 envisioned classifying articles by the publication that they come from. However, this may not be an entirely accurate approach. For example, there are likely to be some opinion pieces that may not fall under the right category.

From this project, I was 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. I was initially hesitant to receive feedback on my designs since I put a lot of work into them, but I received really helpful feedback that helped me improve my designs. For me, this helped reinforce the idea that I shouldn’t ever feel so devoted to a design to be hesitant to receive feedback. Successful UX design is typically iterative, and that requires a willingness to be flexible on the part of the designer.