DFW-PP: Dynamic Feature Weighting based Popularity Prediction for Social Media Content

TL;DR

This paper introduces DFW-PP, a framework that dynamically weights features over time for more accurate social media content popularity prediction, addressing the challenge of content saturation and feature importance variability.

Contribution

The paper presents a novel dynamic feature weighting approach that adapts to temporal changes, improving popularity prediction accuracy over existing static models.

Findings

Demonstrates improved prediction accuracy on benchmark datasets.

Effectively handles feature skewness through log-log normalization.

Shows promising results compared to traditional methods.

Abstract

The increasing popularity of social media platforms makes it important to study user engagement, which is a crucial aspect of any marketing strategy or business model. The over-saturation of content on social media platforms has persuaded us to identify the important factors that affect content popularity. This comes from the fact that only an iota of the humongous content available online receives the attention of the target audience. Comprehensive research has been done in the area of popularity prediction using several Machine Learning techniques. However, we observe that there is still significant scope for improvement in analyzing the social importance of media content. We propose the DFW-PP framework, to learn the importance of different features that vary over time. Further, the proposed method controls the skewness of the distribution of the features by applying a log-log normalization. The proposed method is experimented with a benchmark dataset, to show promising results. The code will be made publicly available at https://github.com/chaitnayabasava/DFW-PP.