Summary of Real-time Stress Detection on Social Network Posts Using Big Data Technology, by Hai-yen Phan Nguyen et al.
Real-time stress detection on social network posts using big data technology
by Hai-Yen Phan Nguyen, Phi-Lan Ly, Duc-Manh Le, Trong-Hop Do
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based system is developed to detect stress in online social media posts, leveraging a large dataset of Reddit posts with both stressful and non-stressful content. The model uses Apache Kafka, PySpark, and AirFlow to process and analyze the data, achieving an accuracy of 69.39% and F1-score of 68.97%. This study has implications for developing more accurate methods for detecting stress in online users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how technology can be used to detect stress in people’s social media posts. A big dataset of Reddit posts was collected, with some having stressful things and others not. The team built a system using special tools like Apache Kafka, PySpark, and AirFlow to analyze the data. The results were quite good, showing that the system could correctly identify stressed posts most of the time. |
Keywords
* Artificial intelligence * F1 score * Machine learning
