Trisha, Ananya SubhraBin Rofi, IshmamEshita, Mashiyat MahjabinBiswas, JoyAhmed, Md. Sabbir2026-08-152026-08-152023-01-01A. S. Trisha, I. Bin Rofi, M. M. Eshita, J. Biswas and M. S. Ahmed, "Content, Consumption, and Productivity: An Empirical Analysis of Compact Streaming and Reel Content's Effects on the Productivity of Today's Emerging Generation," 2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Kuala Lumpur, Malaysia, 2023, pp. 181-186, doi: 10.1109/SKIMA59232.2023.10387365.97983503165512373082X2-s2.0-85184363261https://hdl.handle.net/10361/29117In today's generation, short streaming video content on various platforms has become pervasive. More importantly, it has a significant impact on the productivity of today's generation. This paper delves into the intertwined connection between productivity and short video contents and reels with the help of ML models like Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes Classifier. For our research work, we have assembled 751 data of certain people under the age group of 15 to 35. The individuals within this data set are characterized by their active engagement with short streaming video content. While analyzing, among different ML models, the Naive Bayes Classifier has the highest accuracy of 76.06%. Additionally, to demonstrate our research, we have also used Ensemble Learning which combines the result of many different models to reduce the bias, to increase model stability, and accuracy.en-USfalseBias reductionData analysisEnsemble learningMachine learningNaive bayesProductivityReelsSocial mediaConference Proceeding