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

arXiv: https://arxiv.org/abs/2110.08510 · HTML: https://mlsanity.com/paper/2110.08510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks](https://mlsanity.com/paper/1712.04443/) (2017)
- [Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media](https://mlsanity.com/paper/2105.08809/) (2021)
- [Intrinsic Image Popularity Assessment](https://mlsanity.com/paper/1907.01985/) (2019)
- [A Latent Social Approach to YouTube Popularity Prediction](https://mlsanity.com/paper/1308.1418/) (2013)
- [Can Self Reported Symptoms Predict Daily COVID-19 Cases?](https://mlsanity.com/paper/2105.08321/) (2021)

## Built on by (most cited first, 1 of 1)

- [Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks](https://mlsanity.com/paper/2206.12258/) (2022)

## Abstract (arXiv)

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.