Gradient Boost Tree Network based on Extensive Feature Analysis for Popularity Prediction of Social Posts

Chih Chung Hsu, Chia Ming Lee, Xiu Yu Hou, Chi Han Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social media popularity (SMP) prediction is a complex task, affected by various features such as text, images, and spatial-temporal information. One major challenge in SMP is integrating features from multiple modalities without overemphasizing user-specific details while efficiently capturing relevant user information. This study introduces a robust multi-modality feature mining framework for predicting SMP scores by incorporating additional identity-related features sourced from the official SMP dataset when a user's path alias is accessible. Our preliminary analyses suggest these supplemental features significantly enrich the user-related context, contributing to a substantial improvement in performance and proving that non-identity features are relatively unimportant. This implies that we should focus more on discovering the identity-related features than other meta-data. To further validate our findings, we perform comprehensive experiments investigating the relationship between those identity-related features and scores. Finally, the LightGBM and TabNet are employed within our framework to effectively capture intricate semantic relationships among different modality features and user-specific data. Our experimental results confirm that these identity-related features, especially external ones, significantly improve the prediction performance of SMP tasks.

Original languageEnglish
Title of host publicationMM 2023 - Proceedings of the 31st ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages9451-9455
Number of pages5
ISBN (Electronic)9798400701085
DOIs
Publication statusPublished - 2023 Oct 26
Event31st ACM International Conference on Multimedia, MM 2023 - Ottawa, Canada
Duration: 2023 Oct 292023 Nov 3

Publication series

NameMM 2023 - Proceedings of the 31st ACM International Conference on Multimedia

Conference

Conference31st ACM International Conference on Multimedia, MM 2023
Country/TerritoryCanada
CityOttawa
Period23-10-2923-11-03

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

Fingerprint

Dive into the research topics of 'Gradient Boost Tree Network based on Extensive Feature Analysis for Popularity Prediction of Social Posts'. Together they form a unique fingerprint.

Cite this