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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia
發行者Association for Computing Machinery, Inc
頁面9451-9455
頁數5
ISBN(電子)9798400701085
DOIs
出版狀態Published - 2023 10月 26
事件31st ACM International Conference on Multimedia, MM 2023 - Ottawa, Canada
持續時間: 2023 10月 292023 11月 3

出版系列

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

Conference

Conference31st ACM International Conference on Multimedia, MM 2023
國家/地區Canada
城市Ottawa
期間23-10-2923-11-03

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦繪圖與電腦輔助設計
  • 人機介面
  • 軟體

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