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Title:

X User Data Ethics Case Study

Case Study

Overview

As part of a case study, new usability features and changes are proposed in an interactive Figma prototype for the application X (formerly known as Twitter). These changes aim to empower the user in their experience using the platform. The hypothetical new features cover sentiments expressed through primary and secondary research conducted; revealing insights into user concerns regarding AI training, data collection, and feed algorithms.

The final prototype proposes the following changes:

Increased visibility of Privacy/TOS settings so users are more informed and empowered with their data.
Clear TOS and app update notifications to be transparent and clear with users.
Improved feed preference customization and filtering to encourage users to customize their experience.

Adjustments/tweaks:

Added specific context on suggested posts and ads to inform users on why they are receiving certain posts or ads, allowing them to disengage or engage if they please.
Reordering of side menu to prioritize important user settings first before anything else.

Research

Primary research was conducted regarding X in the form of a user survey to determine user’s views and perspectives on the platform. The information gathered would go on to inform the direction of the project as common concerns and trends were addressed through the survey results. The questions cover a variety of topics regarding user behaviours and opinions with information privacy and overall feelings. Secondary research was also conducted to create relevant and informed questions that could validate sentiments online while also spawning new insights on the matter as the app has changed drastically in just the past few years since its acquisition from Elon Musk.

The survey was distributed to fellow students as well as a select group of people online, ranging from ages 18-25 from a variety of backgrounds with their personal info anonymized. To cover a wide range of participants, two streams of the survey was created: users and non-users, determined by the participants answer to how often they use X.

After analyzing the survey data, six key problems were discovered throughout the results. These problems included concerns about AI training of user content, controversial/upsetting feeds pushed by the algorithm, hidden TOS, and more. With these problems, defined, solutions and goals were created to address throughout the project as the through line.

Development

To start I took screenshots of the app and documented the current user flow of completing “problem area” tasks defined by the survey results. Such as finding the TOS, or removing unwanted posts and topics from their feed. I also took some time to look into other similar platforms like Bluesky to get some inspiration. Then, I began production of low fidelity, mid fidelity, and high fidelity interactive prototypes on Figma; testing with a variety of methods and receiving feedback from peers and survey participants.

Testing

Different user testing methods were practiced when testing the different stages of the prototype (alongside general user testing). These methods include: Five second test and Emoticon scale. These methods helped debug and test usability, feasibility, flow, and clarity allowing me to iterate and improve the design over the course of the four week project in order to deliver a refined project reflective of user wants and needs.