Google News is an aggregator of news from various outlets. Its goal is to simplistically and holistically provide news to readers on current events. But the app lacks crucial features that help alleviate the cognitive load of readers and reduce their detective work when faced with potentially biased news.


Course Project


4 Weeks


Research | UX | UI | Interaction

Gauging national perspective on media

According to a survey done by the Knight Foundation, 69% of American adults have lost trust in the media over the past decade. 
“Asked to describe in their own words why they trust or do not trust certain news organizations, Americans’ responses largely center on matters of accuracy or bias.”
Lack of transparency was another top factor for why Americans have lost their trust. Articles are long and complex⁠—it’s difficult to sort through and understand ideas when readers are not current on the topic or under a time crunch.


The general problems

After extensive research on news consumption trends and reporting contents— I uncovered a few important points that influence our understanding of the news.


Forming and testing the hypothsis

I talked to 7 newsreaders to understand how they consume news and what they think about the news they intake. Conversing with them helped me understand the nuance of why readers want unbiased news yet find it hard to achieve. There is just too much content and too many steps to go through to get to the bottom of a story.

To test this hypothesis, I provided a story from 3 different outlets with different party affiliations to a reader to learn about her real-life interactions. The goal of this test was to see if there were any behavioral tendencies that were not mentioned in the survey or interviews.


Moving forward from target audience POV

I've summarized my target audience to be those that want to know if articles are biased. As well as have access to source transparency, accurate facts, and article summaries. I visualized my target audience in form of a persona and then worked out the empathy map to uncover points for HMW. The goal of these exercises is to understand all the facets of the news consumption experience from the target user's perspective.


Breaking down the problem

I’ve broken down the problem into four target areas —

Bias | Accuracy | Transparency | Simplicity

1.  Bias

The media use sensational and exaggerated headlines to increase readership and readers want (even if they don’t admit this) articles that are aligned with their pre-existing views.

According to Fair, there are a couple of ways to detect bias in reporting, which I've referred to during ideation.

To curb biases in reporting, I’ve incorporated the bias rating feature. The bias rating focuses on 3 main factors of bias evaluation. These main factors (see below) were finalized based on the groupings of the aforementioned bias detection methods.


2.  Accuracy

It’s incredibly hard to differentiate falsehoods and doctored facts within a seemingly credible article. Readers often don’t finish articles but rather are directly drawn to statements that contain hard data.

So I’ve underlined false/one-sided statements to allow readers to immediately differentiate. They can also learn more information by tapping on the statement to show a more holistic view. Some challenges were met throughout ideation but I ultimately decided on the version below to meet the design criteria.


3.  Transparency

I asked readers to provide their interpretations of media transparency and they deemed below as indicators:

Who the reporter is and his/her personal biases
Who the cited experts are and their hidden interests and affiliation
The original sources for the provided facts and statements (typically hyperlinked)
To find out what information is relevant to include, I looked over the different areas of an individual’s background that are susceptible to biases. Readers found party affiliation and religion to be the two most debated points in biased news. 


4.  Simplicity

Readers are easily susceptible to interruptions and they’re also consuming news in the morning, during break and some time before bedtime— all short spans of time. For that reason, long and convoluted articles are just too inefficient.

Combining the story background with the 5W’s from basic information gathering techniques, I’ve put together a simple, categorical breakdown for readers to access if they’re short on time or simply confused. This feature was widely popular with my users and hence confirmed their need to have bulleted and to-the-point facts.


Testing the prototype

After the initial round of fast prototyping, I conducted 5 sessions of usability testing to uncover any usability problems. The participants were asked to perform various tasks as well as give any general feedback on the app. 

The overall consensus was that the features were nice additions that facilitated users to understand the story more objectively. The in-app features helped to take the load off of having to actively research outside of the reading sphere.


What's new and updated

Initially, I had only included bias ratings and definitions without going into details on the basis of the ratings. The decision was based on my assumption that readers prefer to know only the outcome and not necessarily the process. But this exclusion ultimately backfired in terms of helping readers truly understand the biases. Highlighting actual biased statements from the article helped mitigate this issue.


Final design


Bias Meter

Story Breakdown

Fact Checker

Next steps

Although the features have no fundamental issues, I do want to make it easier for users to use. To draw a stronger connection between the biased statements and the article it originates from, I’ve drafted a concept design shown below.



Readers can now read news with efficiency and accuracy. Story breakdown helps make news consumption faster. And having fact-checkers and bias rating features really help to promote the truth of the story.

Selected Works

LAT AppProject type

Enpower LA - 311 DataProject type

DINNEProject type

SportsyapClient Work

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