Center for Design | Twitter

Research on the role of User Interface design in misinformation

The Center for Design is Northeastern University’s platform for interdisciplinary design research. This project is an earlier subset of X’s Community Notes (formerly known as Twitter’s Birdwatch)

  • 2022

  • Apple iOS, Web

  • Figma, User Zoom, Apple Xcode, Microsoft Teams, Microsoft Word, Excel, Powerpoint, Google Docs, Google Sheets, Google Slides

  • Product Experience Design, UX Research, Prototyping, User Testing, Research Writing

Responsibility

As a Product Designer and HCI Researcher, my key responsibilities included:

  • 1. Literature Review & Research: Conducted extensive research on news consumption, interface design, and industry approaches to mitigate misinformation, identified gaps & opportunities for design interventions.

  • 2. Design & Prototyping: Developed 5 prototypes incorporating visual cues (colors, symbols, percentages) to highlight misinformation in tweets using alerts, community groups, and more.

  • 3. User Testing & Data Analysis: Recruited participants, conducted user tests, collected qualitative and quantitative evaluations. Used thematic analysis to assess critical thinking patterns & content preferences.

80%

11

Success Rate

Participants

5

Prototypes

Literature Review & Research

Goal

Design and evaluate interventions on Twitter that foster critical reflection through visual cues and community-based reporting. The project aimed to understand the impact of visual elements on users’ news consumption and the prevention of engagement with fake news.

  • Abstract

    Social media’s role in information dissemination has grown exponentially, often leading to the rapid spread of misinformation. This project aimed to investigate how interface design influences misinformation spread by affecting users’ consumption behavior.

  • Visual Design Principles

    By reviewing existing studies, I analyzed the role of various visual attributes—size, symbols, colors, etc.—and their impact on user behavior, concluding with design implications and recommendations for the social media’s interface interventions.

  • Research Material

    The literature review references spanned academic studies on social media behavior, misinformation impact, cognitive reflection, public trust, algorithm monitoring, design influence. Key themes included user interaction psychology, content labeling, and verification practices.

Ideation

Building on research that showed how visualizing filter bubbles can increase transparency and influence consumption behavior, we explored design directions focusing on transparency, third-party fact-checking, collaboration, and article tracking. We tested 5 prototypes to evaluate Twitter users’ reactions to visual prompts and the potential impact of community-based reporting and third-party fact-checkers.

Concept Map

The literature review covered news consumption, interface design, and current approaches to reducing misinformation spread. Key design directions emerged, including third-party apps, labels, truth meters, source verification, and user reflection.

Design & Prototyping

Variable 0

Baseline test with the current Twitter interface, including user-verification badges and retweet/quote retweet ratios.

Variable 1.1

Misinformation alerts submitted by individual Twitter users, displayed dynamically. Included traffic light colored alerts for fact (green), not entirely accurate (yellow), and misleading claims (red).

Variable 1.2

Similar to Variable 1.1 but with alerts in a fixed order. Aimed at testing ease of navigation and information consumption.

Variable 2

Misinformation alerts submitted by group lists on Twitter, with variations in alert size, percentages, and visual prominence.

Variable 3

Misinformation alerts assessed by third-party fact-checkers. Compared community-based reporting with third-party verification.

User Testing & Data Analysis

User Tests

To assess the effectiveness of these variables, the Project Manger and I conducted user tests with participants familiar with Twitter, aged 20-30, and regular consumers of social media news. The testing involved: interviews, think-aloud sessions, clickthrough of prototypes, surveys.

With a success rate of 80%, there was a strong preference for Variable 1 and its traffic light colored icons that helped reflect on content accuracy. Percentages and numbers attracted user attention and sparked curiosity. There was also a preference for bold text and straightforward explanations.

Testing Analysis

Overall, there was a huge desire for transparency and credibility that Variable 1.1 and 1.2 offered using visual, traffic light colored icons/alerts. People highly prefer friction-less content consumption without disruption but willingness to trade off speed for accuracy if additional information aids reflection. This bottom-up approach would empower people to collaborate and add context to potentially misleading posts. People found concerns about credibility, neutrality, and transparency of Third-Party Fact-Checking in Variable 3. There was also confusion about group interactions; and skepticism about community groups’ credibility in Variable 2.