Predicting Social Media Post Popularity Using Image and Non-Image Features

Concepts de base

Incorporating both image and non-image features, such as captions, user information, and temporal data, can significantly improve the accuracy of predicting the popularity of social media posts.

Résumé

The study presents a framework for predicting the popularity of image-based social media content by effectively extracting key image and color information from users' postings and exploring a wide range of prediction models.
Key highlights:

The study utilizes the Google Cloud Vision API to extract image labels and dominant colors, which are then used as covariates in the prediction models.
The authors employ Seeded Latent Dirichlet Allocation (Seeded-LDA) to summarize the image contents into interpretable topic variables.
The study compares the performance of various models, including Linear Mixed Model, Support Vector Regression, Multi-layer Perceptron, Random Forest, and XGBoost, with linear regression as the benchmark.
The results demonstrate that models capable of capturing the underlying nonlinear interactions between covariates, such as Random Forest and XGBoost, outperform other methods.
Incorporating both image and non-image features, such as captions, user information, and temporal data, can significantly improve the accuracy of predicting the popularity of social media posts compared to using only non-image covariates.
The proposed methods are practical and interpretable, providing direct implications for real-world applications.

Stats

The number of images per post is an important factor in predicting the popularity of social media posts.
The time difference between the post upload and data crawling is a key variable that needs to be accounted for when predicting post popularity.
The number of hashtags used in a post's caption is a significant predictor of its popularity.

Citations

"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."
"Experimental results demonstrate that the proposed interpretable variables achieve comparable performance to features constructed by various existing methods, including embedding vectors extracted using deep learning."
"Our considered methods are both practical and interpretable, yielding direct implications for real-world applications."

Idées clés tirées de

Enhancing Social Media Post Popularity Prediction with Visual Content

by Dahyun Jeong... à arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02367.pdf

Questions plus approfondies

How can the proposed framework be extended to incorporate video content in addition to images for predicting social media post popularity?

Incorporating video content into the proposed framework for predicting social media post popularity would require adjustments and enhancements to accommodate the unique characteristics of videos. Here are some ways the framework can be extended to include video content:

Feature Extraction from Videos: Just like images, videos contain valuable information that can influence post popularity. Utilizing video analysis techniques, such as frame-by-frame analysis or deep learning models, can help extract relevant features from videos. These features could include visual elements, audio cues, video length, editing style, and more.

Integration of Video-specific Variables: Introducing variables specific to videos, such as video duration, aspect ratio, resolution, audio quality, and engagement metrics like views, comments, and shares, can provide additional insights into the popularity of video posts.

Model Adaptation for Video Data: The prediction models used in the framework may need to be adapted or expanded to handle the complexities of video data. Models like Long Short-Term Memory (LSTM) networks or Convolutional Neural Networks (CNNs) are commonly used for video analysis and could be integrated into the framework.

Hierarchical Structure for Video Posts: Similar to the hierarchical structure for image and caption data, a hierarchical approach can be developed for video posts. This could involve considering the user's posting history, video content, and user engagement patterns to predict the popularity of video posts accurately.

Validation and Testing: Extending the framework to include video content would require thorough validation and testing to ensure the models can effectively analyze and predict the popularity of video posts. This may involve collecting a new dataset with video posts, refining the feature extraction process, and fine-tuning the models for video data.

By incorporating video content into the framework and adapting the models and variables accordingly, the prediction accuracy for social media post popularity can be enhanced, providing a more comprehensive analysis of user-generated content on social media platforms.