Summary of Enhancing Social Media Post Popularity Prediction with Visual Content, by Dahyun Jeong et al.
Enhancing Social Media Post Popularity Prediction with Visual Content
by Dahyun Jeong, Hyelim Son, Yunjin Choi, Keunwoo Kim
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 The paper proposes a framework for predicting image-based social media content popularity by leveraging complex image information and hierarchical data structures. The authors utilize the Google Cloud Vision API to extract key image and color features from user postings, which improves accuracy by 6.8% compared to using non-image covariates alone. To predict popularity, the study explores a range of models including Linear Mixed Model, Support Vector Regression, Multi-layer Perceptron, Random Forest, and XGBoost, with linear regression serving as a benchmark. The results demonstrate that models capable of capturing underlying nonlinear interactions between covariates outperform other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to predict how popular an image will be on social media based on what’s in the picture. It uses special software to look at things like colors and shapes, which helps make better predictions. The researchers tested different ways to do this, including some that are good at finding patterns in data. They found that these pattern-finding methods were usually better than simpler ones. |
Keywords
» Artificial intelligence » Linear regression » Random forest » Regression » Xgboost
