International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Doomscrolling is an emerging behavioural pattern characterized by prolonged, repetitive, and low-attention smartphone usage, which often leads to reduced productivity and cognitive fatigue, particularly among students. Existing digital well-being solutions largely rely on static screen-time limits and manual restrictions, offering limited adaptability to individual user behaviour. This paper presents a prototype AI-based system for detecting and mitigating doomscrolling through behavioural analysis of smartphone usage metadata.

The proposed approach passively monitors user interaction patterns during an initial learning phase and constructs a personalized behavioural profile using lightweight machine learning techniques. Due to the lack of publicly available labeled datasets for doomscrolling behaviour, a proxy-labeled dataset is generated using behaviour-inspired heuristics to evaluate model feasibility. A lightweight neural network model is trained to predict the likelihood of a user entering a doomscrolling state based on features such as session duration, scrolling intensity, application switching behaviour, and time-based usage patterns.

To preserve user privacy and usability, the system operates without content inspection and deploys the trained model using on-device inference. Conservative decision thresholds and non-intrusive interventions are employed to minimize false-positive alerts and support user awareness rather than enforce restrictive controls. Experimental evaluation demonstrates effective behavioural separability under controlled conditions, validating the feasibility of the proposed approach. The results indicate that predictive, behaviour-aware digital well-being systems can provide a promising foundation for addressing doomscrolling in real-world mobile environments.

Keywords: doomscrolling, screen addiction, mobile usage analysis, machine learning, digital well-being.

Downloads: | DOI: 10.17148/IJARCCE.2026.15233