CNN Learn
A new way to consume news that fights polarization and promotes trust in CNN
Category
Research, Strategy, Social Innovation
Sponsor
CNN Digital
Role
Design Research, Strategy, Client Management, Product Design
Team
Adhiraj Singh, Annie Chen, Hakyung Yeom, Sarah Young
Overview
We partnered with CNN to design a solution for a real business challenge that also had social implications. The prompt was to understand and design for CNN's "Hate-readers".
After a 2 months of a rigorous, end-to-end design process that included weekly share outs and co-design sessions, we presented a working prototype for feedback that addressed needs of skeptical news consumption.
My Contributions
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Design Research Lead - designed research strategy, recruited all participants, scripted and performed interviews, conducted user testing, led synthesis for product strategy
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Product Management - defined project scope and timeline, directed client relationship
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Prototyping - sketched wireframes
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UX Writing - created copy for prototype and collaborated on information architecture flow
Solution
CNN Learn is new tab within CNN's existing news app for skeptical news consumers that provides rich context around trending news topics of the user's choice. It provides curated content about each topic such as timelines, overviews, relevant breaking news, content from regional affiliates, and past stories.
By providing a fuller picture around a news topic, consumers can have a richer, more educational, transparent and tone-neutral news experience. The tab also provides ways for users to test, compare and analyze the information they are presented - true learning opportunities.
CNN's Prompt
CNN tasked us to understand and design for their "hate-readers" - a prominent user group of their app about which they understood very little. CNN defined "hate-readers" as people who explicitly dislike CNN, but use the app anyways. They wanted us to investigate around these questions:
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Who are they?
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How should CNN deal with them?
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What are their implications?
The Big Picture
What external forces are shaping this phenomenon? What will affect our design direction? I charted key influences on this phenomenon by doing desk research, pulling insights from my past interviews with 10+ journalists on challenges in the media environment, and by talking to the CNN Digital team.
Growing Public Mistrust of Big Media
Growing public belief that media outlets are biased and unreliable
CNN Business Challenges
An aging user base, changing revenue models and greater media competition
Rampant Misinformation
Infinite content creators, quick media sharing and clickbait
Establishing Constraints
We were lost on where to start; it was a vague and unique challenge. So, we mapped constraints, explored goals and guidelines, and listed our assumptions.
My contributions:
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Designed project timeline
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Explored CNN's strategic objectives, branding assets and user archetypes
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Performed assumption mapping on what we think hate-reading is and why hate-reading could be significant for CNN's strategic model
"To read (a blog, newspaper, etc.) that one professes to dislike, often with the intention to mock or criticize."
- Dictionary.com's definition of hate-read
Exploratory Research
We needed to get a grasp on on the basics of hate-reading. What is it, what does it look like, who does it, and why? We dove into the topic with our highest-risk assumptions guiding the research.
My responsibilities:
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Created a research and interview guide
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Designed consent forms
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Performed desk-research on hate reading behaviors and motivations
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Designed and disseminated 1 screening survey
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Held 4 interviews with self-professed news consumers
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Synthesized interviews against CNN's user archetypes to find behaviors that have not been charted
Challenges + Key Moments: Exploratory Research
Initially we thought we wanted to focus on trolls and those who intentionally hate-read, but, it was very difficult to find these people, especially without telling them upfront. Knowing that people can hate-read unintentionally granted me access to a wider pool of initial interviewees while staying true to our target demographic.
Initial Findings on Hate-Reading and News Consumption
I used various methods to synthesize the information I gathered, and found some key common behaviors around hate-reading and news consumption:
Readers are aware of news to inform vs. for entertainment, and its effect on them
"I can't deal with the [CNN] commentators!" - Cl
Intentions around hate-reading may be positive as well as negative
"When he was more prevalent, I followed Donald Trump to see what the 'F' he was saying..." - La
Readers acknowledge the bias in their news, consume it anyways
"I wish there was a more objective news outlet, but I think it's a sign of our times. That's just what's available." - Pe
Synthesizing Further
We needed to define the user and the parameters of their hate-reading behaviors. I helped us see that the common denominator among hate-reading is actually skepticism. From there, we decided to do more primary research.
My contributions included:
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Performing desk research on skepticism
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Charting new behaviors against personas
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Mapping assumptions on skeptical news consumption
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Creating new interview guide
"An attitude of doubt or a disposition to incredulity either in general or toward a particular object"
- One of Merriam Webster's definitions of skepticism
Investigating Skepticism
I held another round of interviews to understand the behaviors and motivations around consuming news with a skeptical mindset.
What I did:
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Drafted new interview questions
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Designed and disseminated recruitment adverts
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Held 8 interviews with people who identify as "skeptical news consumers"
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Designed and led a ranking activity on media outlet trustworthiness
Synthesizing Skepticism
But what does skepticism mean for news consumption? What causes it, and what does it look like? Through affinity mapping and mapping, we
I helped derive that:
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Skepticism and trust in news and content are products of both long and short term factors
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Skepticism is healthy but also maladaptive
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The pain points are mostly around bias and external influences in objectivity
Mindsets + Pain Points
Regarding trustworthiness in news and media, I derived these mindsets and behaviors around skeptical news consumers:
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Trusting local and foreign sources more
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Acknowledging news bias yet consuming it to paint own truth
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Trusting a news outlet based on how their peers trust it
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Frustration from lack of transparency upfront
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Avoiding political news consumption due to hyper-partisan tone fatigue
And the following pain points:
Too much commercial bias in news reporting
"I can't go on my phone without getting apple news everywhere. It's been totally commercialized, bought out by billionaires." - J
Mixing facts with opinion, perceived lack of proof
"Why would I trust them [news outlets] when they have been wrong on so many things?" - L
Too much "clickbait" and content to persuade
"Are you trying to get me to vote a certain way or just trying to report? For me, that's where my skepticism comes from, ultimately." - CZ
Creating a New Archetype
Having mapped the users' behaviors and mindsets against CNN's existing archetypes, we saw that they were consistent with a new type of user. To be valued by CNN, this newly-identified user needed to be charted as a new archetype.
My contributions:
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Synthesized behaviors from interviews to determine who is and who isn't "skeptical"
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Created new metrics for behaviors and mindsets
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Mapped preferences on existing dimensions to create a psychographic summary
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Identified key attitudes, mindsets, pain points and barriers to entry
How Might We...?
We then looked at our behavioral insights on skepticism, and framed them against CNN's business strategy, social forces, and our constraints to derive our design direction:
How might we transform CNN's existing assets into an experience that is transparent and provides users diverse news perspectives?
Brainstorming Directions
With our "How Might We" statement and our constraints, we could now begin developing concepts. We first came up with three general directions, all within CNN's existing ecosystem. With these directions we then mapped out their hypothetical business, user and societal impact.
We also understood that capturing Gen Z could be an important part of achieving CNN's business strategy, and remembered that people have higher trust in local news. With that in mind, we needed to research Gen Z media consumption and their attitudes towards local news.
My contributions:
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Collaborated on concept direction and impact mapping
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Performed desk research on the value of local news for users and Gen Z trends in media consumption
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Interviewed 4 Gen Z news consumers, and obtained data from 8 more people through a survey on their needs and views on local news
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Synthesized data to create insights for Gen Z-specific features
Concept Sketching
With feedback from CNN, we decided to pursue a concept leveraging their local news affiliates and the Gen Z audience. We then produced three different concepts around that direction.
Concept #1 - A news aggregator that's based on Gen Z interests and style, is image heavy, can be personalized, and has social engagement
Concept #2 - Wikipedia-like function within app that provides deeper context around news
Concept #3 - Improved heuristics of existing CNN app, while including greater personalization options
My contributions:
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Drew comps on media and feature strategies for engaging Gen Z and local news affiliates
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Created wireframes for concept #1 (Gen Z social)
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Brainstormed main features of other concepts
Feature Mapping
With the feedback from CNN we decided to focus on the "Learn tab" (concept #2) because of its uniqueness among CNN products and its production feasibility.
My contributions:
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Brainstormed functional, social and business "jobs to be done" within the concept
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Synthesized the "jobs to be done" into feature themes
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Mapped hypothetical features on an impact x effort scale to determine which were feasible
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Drew comps of features from relevant apps
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Sketched wireframes for hypothetical features
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Collaborated on a user journey map to determine touch points and entry points
Features
The features centered around 3 themes: user agency, empowerment through knowledge, and inclusion. Through a mapping exercise, we identified 6 tasks or "jobs to be done" that the user would aim to do with the product:
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I need to know who this news affects and who is involved
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I want to share what I learn with my friends
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I want to be updated effortlessly and quickly
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I want to be informed via multiple perspectives
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I want to feel confident in my new knowledge
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I want to see things relevant to me
Validation Testing
With only one week remaining on the project, we needed to at least validate the concept and main features of the prototype.
My contributions:
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Designed a user testing experiment guide around assumptions, hypotheses and success metrics
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Created a functional prototype for validating user understanding of the concept
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Conducted 4 user tests in person and online
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Synthesized data using quantitative methods
Validation Insights
Through synthesis of user feedback, I gathered the following insights on the overall direction of the concept:
General Understanding
They understand what "Learn" is aiming to do and are excited to dive deep into the topics. But, they were unsure how this is different from an aggregator.
Favorite Features
How the tab presents different layers and complexities of information in a zoom-in, zoom-out style
Pain Points
The flow between different categories, information and screens was confusing.
Prototype Iteration
It was clear from the user testing that the concept became unclear once inside the tab. First, as a team we needed to align on our own understanding of the concept, then from there we decided to streamline the information architecture and make the UX copy consistent with the intended flow. We also created entry points for the tab from within CNN's existing assets.
My contributions:
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Suggested user flow and information architecture improvements
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Drafted UX copy that is clear and aligned with CNN's actual content
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Drafted wireframes for new features
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Researched entry points within CNN's existing ecosystem
