In today’s technology-driven era, many people find themselves deeply immersed in digital devices. Social media applications are often carefully designed to prolong user time. Some users are even aware of the problem, but struggle with self-control.
Studies have shown that prolonged use of social media can increase anxiety and lead to addiction. However, most existing control tools only focus on limiting screen time without exploring the underlying reasons behind user addiction.
The project aims to develop an intelligent social media management application that integrates artificial intelligence driven behavior analysis, personalized intervention, emotion tracking to help users establish healthier usage habits. The app will provide smart alerts, social interaction features, data analytics, and other features to encourage moderate use of technology, reduce the risk of addiction, and promote real-life social engagement.
Through this system, we hope users will be able to regain control of their time and foster a more balanced and healthy digital lifestyle.
Name： Chenglin Li
Student ID： 49210611
The problem I am trying to solve is to develop an intelligent social media usage management app that will help users build healthy digital habits. Unlike traditional “forced intermission” tools, the application uses artificial intelligence to improve the scientific and user-friendly nature of the intervention strategy. It helps users to break from the over-dependence on social media without losing the fun of social connection.
Core Features
1. AI Smart Analysis
An AI system observes trends and behavior of social media users and identifies certain high-risk patterns, such as prolonged use. In addition, it offers personalized interventions (like reminders or alternative activity suggestions) according to users’ emotional state and habits of using the application. In addition, the system is adaptive, and thereby changes intervention strategies according to usage data, for example, setting reminder frequency on the fly or suggesting alternative activities more suited for the user.
2. Social Interaction
The fun and social motivation is that users can compete against friends to see who uses social media the least. We have a points-based reward system where users get focus points if they use the product less, and that can be redeemed for personalized rewards, like theme skins, to motivate them to continue having healthy usage.
3. Digital Well-being & Emotional Analysis
It also asks them to enter their emotional states daily and stores it for analysis. It finds correlations between emotional changes and the use of social media (i.e. whether more usage leads to more anxiety). Upon this understanding, the system proposes personalized suggestions offered to the users, for example, breathing exercises, walking, or reading to assist the users to control their emotions and further use the system more efficiently.
4. Focus Mode
Focus Mode shuts off all alerts and social media interruptions, an attempt to bore users into getting things done. Users can choose a focus time (e.g., 45 minutes) and the app helps with countdown to visualize focus goals. Moreover, the app comes with white noise options (such as rain sounds or meditation music) meant to tranquilize the atmosphere and therefore enhance concentration and productivity.
AI Behavior Detection
Tracks users’ social media usage duration and frequency, identifying high-risk patterns (e.g., scrolling short videos continuously for 30 minutes).
Records user activity data and provides a summary report of recent usage.
Smart Intervention
When a user spends an extended period on social media, the app sends gentle reminders (e.g., “You’ve been using social media for 30 minutes. Consider taking a break.”).
Users can choose to accept or ignore the reminder. If reminders are ignored multiple times, the intervention strategy will automatically adjust.
__Focus Mode __
Users can manually activate “Focus Mode,” which blocks all social media notifications.
Focus time can be set, and a countdown timer is provided.
During Focus Mode, users can enable white noise playback (e.g., rain sounds or calming music) to enhance concentration.
__Social Interaction __
A leaderboard is displayed on the homepage, showing the user’s social media usage ranking among friends. Users can compete with friends to reduce screen time.
A simple “Focus Points” system is implemented—points are linked to social media usage and can be redeemed for homepage badges and other rewards.
__Mood Logging __
Users can manually log their daily mood. The app records and analyzes emotional trends.
Basic data analysis is provided, with weekly or monthly emotional reports generated.
1. Availability
Availability refers to the system’s ability to operate reliably across different scenarios, ensuring that users can access its core functions at any time. Since the app needs to continuously monitor user behavior, any crashes or downtime would severely impact the user experience. Long-term availability is crucial to truly help users change their habits, rather than merely offering a short-term solution.
2. Scalability
Scalability refers to the system’s capacity to handle a growing number of users and integrate new features without degrading performance. Since the AI behavior analysis would be based on large-scale data processing, the system must support a large number of concurrent users without performance loss. Given the expectation for widespread adoption, the app must be usable by users around the globe who own different devices with various demographics without faltering during peak usage.
3. Maintainability
Maintainability is how easily the system can be maintained, upgraded, or optimized. It should be possible for developers to debug and add new features quickly without breaking the stability of the current system. Good maintainability will reduce development costs, since the project will be improved continuously, for example, by refining AI detection and optimizing different modes. The tuning of algorithms, optimization of data, and feedback-driven updates must be smooth in the future. High time and labor costs for every bug fix or feature addition could make the project impossible to sustain in the long run if the system is hard to maintain.
For the evaluation of quality attributes of the Intelligent Social Media Management App, we consider Availability, Scalability, and Maintainability to keep the system stable, efficient, and easy to maintain even under huge usage.
Availability
The evaluation emphasizes system stability, response time of core features, and offline availability. The system should maintain smooth performance under high concurrent usage, with a crash rate below 1%, and core feature response times (such as entering Focus Mode or triggering AI interventions) not exceeding 1 second.
Testing Methods:
Automated UI testing
Long-duration stress testing
Performance testing across various devices
To enhance user experience, the app should also support offline mode, allowing users to access critical functions without an internet connection. Once the device reconnects, the app should synchronize data automatically.
Scalability
Scalability evaluates the system’s ability to support a rapidly growing user base while maintaining high performance under heavy load.
Validation Approach:
Use K6 load testing to simulate 10,000 concurrent users, ensuring that API response times stay under 200ms.
Optimize database performance to ensure query times remain below 100ms, even under large-scale data storage.
The system architecture must support auto-scaling, dynamically increasing server instances during peak traffic and scaling down during off-peak periods to maintain efficiency.
Maintainability
Maintainability assesses code modularity, bug resolution speed, and system upgradability.
Implementation Strategy:
Adopt a modular architecture to ensure high decoupling between functional components. This allows features like AI rule updates or enhancements to the social interaction module to be added without major codebase modifications.
Use SonarQube for static code analysis and enforce a minimum 80% automated test coverage to prevent regressions.
Define clear bug resolution timelines: high-priority issues must be fixed within 24 hours to ensure stability.
Integrate CI/CD pipelines with automated regression testing, allowing the system to detect any disruptions to existing features after each update and significantly improve maintenance efficiency.