Designing Misinformation Interventions for X

The Key Insight

Every intervention strong enough to meaningfully reduce misinformation on a social platform is also strong enough to be called censorship. Designing moderation features for an audience primed to see moderation as an attack was the central problem this project was built around.

Through values-centered design and think-aloud testing with five X users, we found that how agency is distributed determines whether an intervention is received as helpful or hostile. Community-driven corrections and journalist-curated context were welcomed; platform-imposed containment triggered the exact free-speech backlash it was designed to prevent. Each of our three concepts maps to a distinct theory of change: correct retroactively, contain at the moment of spread, or contextualize before exposure.

My Role

On a three-person team in HCDE 598: Designing for Trust, I contributed to every stage of the project. The table below maps my specific contributions.

Area	My contribution
Literature review	Synthesized academic and policy sources on content moderation, misinformation campaigns, and platform trust to inform design values and success criteria.
Ideation	Contributed to brainstorming, concept scoring against success criteria, and selection of the three concepts for prototyping.
Prototyping	Co-designed medium-fidelity Figma mockups for all three intervention concepts.
User testing	Ran think-aloud sessions, asked value-based and concept-specific questions, and took notes on participant reactions.
Analysis & writing	Analyzed findings against design values, co-authored the final report, and contributed to design iterations based on test results.

Context

Misinformation on X is structurally entrenched. Algorithmic feeds reward engagement, and inflammatory or false content reliably generates it. X's "free speech" brand positioning attracts audiences that are particularly critical of moderation, and likely to frame interventions as censorship. Meanwhile, platform trust among the public is low and declining, and legacy media trust is eroding in parallel.

Top-down removals (like the Trump account ban after January 6th) generate significant backlash. Community-led approaches like Community Notes exist but have limited reach.

We scoped our work to high-profile national events, where misinformation is both high-stakes and fast-moving. For user testing, we used a specific false claim: that National Guard soldiers were called to the University of Texas Austin during pro-Palestinian campus protests in spring 2024. This claim had circulated on X and was definitively debunked by the Associated Press, giving us a testable, verifiable, politically charged but not partisan scenario.

Method

We used a six-stage iterative design process informed by a values-centered framework.

Stage	Scope	Output
Literature review	Academic + policy sources	Design values and success criteria for moderation interventions
Ideation	6 concepts scored	3 concepts selected for prototyping
Prototyping	Figma, medium-fidelity	Non-interactive mockups for all three concepts
User testing	n = 5, think-aloud	Value-based evaluation of each concept with structured + open questions
Analysis	5 design values	Findings mapped to values; design iterations identified
Synthesis	Final report	Revised mockups, policy recommendations, and design principles

Design values

The literature review surfaced three initial success criteria: User Experience and Affect, Trust and Facts, and Adaptability and Scalability. We translated these into five specific, single-issue values that would be easier to discuss with participants and evaluate across concepts:

1. Free Speech: Does this enable or prohibit free expression? Could it be seen as censorship?
2. Reducing Bias: What biases does this feature reduce or exacerbate?
3. Transparency: Does the user understand how the platform made this decision?
4. Societal Impact: Where could this go wrong or have unintended consequences?
5. User Effort: How much effort does this require? Is that a reasonable expectation?

These five values became the lens through which we evaluated every concept in user testing.

Ideation and concept selection

We brainstormed against the success criteria, producing a field of ideas that we scored and discussed as a team. Six were developed in greater detail through sketching:

1. Community Notes extension if Community Notes change
2. More descriptive labels and options in Community Notes
3. Interstitial cover warning users of potential manipulation
4. Preventing sharing of posts under fact-checking review
5. Algorithmically rewarding nuanced posts with badges
6. Tracking developing stories with journalistic curation

After scoring and discussion, we combined ideas 1 and 2 and moved forward with three concepts for prototyping: Community Notes Notifications, a Quarantine Feature, and a Developing Story Tracker. Each targeted a different point in the misinformation lifecycle: correction after the fact, containment at the moment of spread, and context-setting before exposure.

User testing

We recruited five participants with varied backgrounds, X usage patterns, and demographics to evaluate all three concepts through think-aloud sessions.

Age	Gender	Background	X Usage
28	Male	Software engineer, from the South	Low (lurker)
25	Male	Latino, from Texas	Medium to high
28	Male	Urban planner in Washington, DC	Medium to high
25	Female	Medical researcher in Seattle	Low
20s	Nonbinary	PhD student in Seattle	Medium to high

Each session followed the same protocol: baseline questions about X usage and misinformation attitudes, walkthroughs of each prototype with value-based and concept-specific questions, and concluding questions about platform responsibility.

Three Concepts

Concept 1: Community Notes Notifications

Community Notes is X's existing community-based correction system, but it has a critical gap: if a user reads a post before a Community Note is added, they never see the correction. This concept addresses that gap by notifying users when context is added to a post they previously viewed.

The standard user journey: a user views a post with unlabeled misinformation. A day later, they receive a notification that a post they interacted with has received additional context from community members. They read the note and update their interpretation of the original post. Secondary features included a category label on the contextualized post (e.g., "Misleading: Factual Error") and a history view for posts that receive multiple corrections.

Concept 2: Quarantine Feature

Some content needs to be slowed before moderation teams can review it, particularly when it has the potential to cause immediate harm. The Quarantine Feature withholds potentially misleading posts from the algorithmic "For You" feed and search results while review is underway, though they remain visible in the "Following" feed. Users cannot like, share, or comment on quarantined posts, but they can subscribe to be notified when a decision is made.

Three review outcomes are possible: the post is removed for violating guidelines, context is added for being potentially misleading, or the post is cleared entirely. The feature was designed to be transparent about this process: users see that a post is under review and understand that the platform is making an active determination, not silently suppressing content.

Concept 3: Developing Story Tracker

Rather than addressing false content directly, this concept focuses on surfacing credible information and providing a frame for users to interpret events as they unfold. The Developing Story Tracker layers a journalist-curated "Analysis" tab on top of trending topics. The journalist curates credible posts and adds commentary; the "Latest" tab remains for users who want unfiltered real-time posts. The goal, drawing on Kate Starbird's framing, was not to create better facts but better frames through which to interpret events.

This approach is notably lower-risk for the platform: posts are not removed, the "free speech" objection is harder to invoke, and the curator burden falls on journalists rather than platform moderators. X benefits from keeping news-seeking behavior on-platform rather than losing users to other news sites for breaking updates.

Findings and Iterations

Community Notes Notifications

The notification concept was the most uniformly well-received. Users saw it as an augmentation of how they already use X: keeping up with evolving stories and not spreading things they later learn are wrong.

"I would appreciate being notified if something I read is factually incorrect or has other errors because oftentimes I bring this up in discussion and it would be embarrassing to be wrong."

The primary concern was notification overload: if the trigger was simply viewing a post, users feared being flooded. A secondary finding was that participants had varied mental models of how Community Notes actually works. Users who understood the system were more confident in its corrections; those who didn't were skeptical about contributor bias. In actuality, Community Notes has safeguards to protect against coordinated manipulation and reflect agreements from diverse perspectives, but these aren't communicated well to users.

Quarantine Feature

Users understood the appeal of quarantine (it targets the infrastructure of spread rather than requiring active user behavior) but had significant concerns about free speech, bias, and adversarial use.

"I think this could be good but yea...screenshots. I feel like it's almost a challenge for them like 'oh you don't want me to share this I'm gonna prove you wrong and share it'...they really think the radical Left is out to get them."

Users also raised concerns about harmful applications: quarantine-by-reporting could be weaponized against queer communities, artists, activists, and small businesses. The reporting mechanism itself was seen as a vector for bias.

"I've heard of people complaining about being shadowbanned, of not being able to get any engagement [...] of artists getting shadowbanned, and it affecting their ability to make income."

Developing Story Tracker

This concept fit most naturally into users' existing behaviors and received the warmest reception overall. Users already use X to follow breaking news; the Story Tracker streamlines this without forcing behavior change.

"I would use this as a reference when debating my family during stuff in group texts. My parents are smart but sometimes they share and believe crazy sh*t. This could automate it for me to send them here."

The main concern was the "Analysis" tab being shown by default, which felt like the platform asserting an editorial perspective.

"Having [the 'Analysis' tab] on by default makes it seem like [the platform is] pushing an agenda...like the platform is trying to narrate rather than having me research for myself." (However, the participant still said they would read the Analysis tab.)

Takeaways

Three design principles emerged from the process:

Agency distribution determines reception. The Quarantine Feature was the most technically aggressive intervention and generated the most resistance, not because users disagreed with the goal, but because it concentrated authority in the platform. Community Notes Notifications and the Story Tracker both distribute agency across communities, journalists, and users. Interventions that augment user agency are better received than those that replace it.

Notification design is misinformation design. Even for users who valued staying corrected, the prospect of notification overload was enough to undermine trust in the system. An effective correction that users learn to ignore is no better than no correction. Any notification-based intervention must budget carefully for users' attention.

Mental models shape trust as much as the intervention itself. Users who understood how Community Notes worked trusted the corrections it produced. Users who didn't were skeptical regardless of the content. Designing effective moderation features also means designing for comprehension of the moderation system; these can't be separated.

Outcome

The three intervention concepts, along with revised mockups, policy recommendations, and design principles, were delivered as a final report for HCDE 598: Designing for Trust. This was a course project; the concepts were not implemented on X. The value of the work is in the framework: a values-centered approach to designing moderation features for a platform whose audience is primed to resist moderation, and evidence from real users about which design moves trigger that resistance and which avoid it.

Limitations

The most significant limitation is sample diversity. Five participants, all educated and urbanite, all tested on a politically complex but not explicitly partisan topic. This leaves significant uncertainty about how any of these interventions would land with users who have strong partisan identities or an ideological commitment to "free speech" as a value above accuracy. That population is a meaningful proportion of X's user base and the one most likely to push back on moderation features. Future work should prioritize recruiting across political identity.

The medium-fidelity, non-interactive prototypes also limit ecological validity. Encountering a Quarantine notification while mindlessly scrolling is meaningfully different from observing it attentively in a research session. Higher-fidelity interactive prototypes, ideally tested within a simulated real X session, would surface different reactions.

Reflection

What we got right: the values-centered framework was genuinely useful for structuring both ideation and testing. Using a real, debunked claim as the test stimulus grounded the sessions and gave participants a concrete reference point. And finding that each concept maps to a distinct theory of change (correct retroactively, contain at spread, contextualize before exposure) gave us a useful vocabulary for recommending when to deploy which tool.

Appendix

Project artifacts from the HCDE 598 course deliverables:

Artifact	Description
Final Report	Literature review, design process, mockups, user testing findings, iterations, and design recommendations