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Enhancing Social Media Post Popularity Prediction with Visual Content
May 7, 2024, 4:41 a.m. | Dahyun Jeong, Hyelim Son, Yunjin Choi, Keunwoo Kim
cs.LG updates on arXiv.org arxiv.org
Abstract: Our study presents a framework for predicting image-based social media content popularity that focuses on addressing complex image information and a hierarchical data structure. We utilize the Google Cloud Vision API to effectively extract key image and color information from users' postings, achieving 6.8\% higher accuracy compared to using non-image covariates alone. For prediction, we explore a wide range of prediction models, including Linear Mixed Model, Support Vector Regression, Multi-layer Perceptron, Random Forest, and XGBoost, …
abstract accuracy api arxiv cloud color cs.cv cs.lg data extract framework google google cloud hierarchical image information key media prediction social social media study type vision visual
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