Early Detection Of Brain Rot Via Social Media Usage Patterns Using Machine Learning Algorithms
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2026
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| _version_ | 1866901505857028096 |
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| author | Kolhapure, Gayatri Gurav, Shradha Kulkarni, Madhuri Jadhav, Samruddhi |
| author_facet | Kolhapure, Gayatri Gurav, Shradha Kulkarni, Madhuri Jadhav, Samruddhi |
| contents | <p>This research paper presents a novel approach to detecting the early signs of “brain rot” caused by excessive consumption of short-form video content. A mobile application was developed to passively track YouTube Shorts usage and collect real-time behavioral data such as watch duration, skip rate, repetition, and content type. Using machine learning, the system classifies video content and generates a composite Brain Rot Index (BRI) to quantify cognitive risk patterns. The study moves beyond self-reported data by providing an objective and data-driven evaluation of digital media habits. The proposed framework aims to understand the impact of modern media consumption on attention, executive function, and cognitive fatigue. This work offers a scalable and practical solution for promoting healthier digital behavior</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18204491 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Early Detection Of Brain Rot Via Social Media Usage Patterns Using Machine Learning Algorithms Kolhapure, Gayatri Gurav, Shradha Kulkarni, Madhuri Jadhav, Samruddhi Brain Rot Index (BRI) Short-Form Video Addiction Social Media Usage Analysis Attention Monitoring Machine Learning in Mental Health Digital Media Consumption Dopamine-Driven Content Cognitive Fatigue Detection <p>This research paper presents a novel approach to detecting the early signs of “brain rot” caused by excessive consumption of short-form video content. A mobile application was developed to passively track YouTube Shorts usage and collect real-time behavioral data such as watch duration, skip rate, repetition, and content type. Using machine learning, the system classifies video content and generates a composite Brain Rot Index (BRI) to quantify cognitive risk patterns. The study moves beyond self-reported data by providing an objective and data-driven evaluation of digital media habits. The proposed framework aims to understand the impact of modern media consumption on attention, executive function, and cognitive fatigue. This work offers a scalable and practical solution for promoting healthier digital behavior</p> |
| title | Early Detection Of Brain Rot Via Social Media Usage Patterns Using Machine Learning Algorithms |
| topic | Brain Rot Index (BRI) Short-Form Video Addiction Social Media Usage Analysis Attention Monitoring Machine Learning in Mental Health Digital Media Consumption Dopamine-Driven Content Cognitive Fatigue Detection |
| url | https://doi.org/10.5281/zenodo.18204491 |