A Comprehensive Study of Spatiotemporal Feature Learning for Social Medial Popularity Prediction

Chih Chung Hsu, Pi Ju Tsai, Ting Chun Yeh, Xiu Yu Hou

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

For accurately predicting the popularity of social media, the multi-modal approach was usually adopted to have promising performance. However, the popularity is highly correlated to its identity (i.e., user ID). Inappropriate data splitting could result in lower generalizability in real-world applications. Specifically, we observed that the training and testing datasets are partitioned on a specific timestamp, whereas some users were registered after the timestamp, implying that the partial identities could be missing in the testing phase. It turns the social media prediction (SMP) tasks temporally irrelevant, making the temporal-related feature useless due to missing identities. Therefore, we form the SMP task as an identity-preserving time-series task to observe the popularity scores for the specific identity. In addition, more valuable and essential features could be explored. In this paper, by reformulating the SMP tasks and integrating the multi-modal feature aggregation to the base learner for better performance, how identity-preserving is an essential property for SMP tasks is discussed. We firstly explore the impact of the temporal features with/without identity information in the conventional SMP tasks. Moreover, we reformulate the SMP task in the time-series data-splitting and evaluate the temporal features' importance. Comprehensive experiments are conducted to deliver the suggestions for the SMP tasks and offer the corresponding solutions for effectively predicting the popularity scores.

原文English
主出版物標題MM 2022 - Proceedings of the 30th ACM International Conference on Multimedia
發行者Association for Computing Machinery, Inc
頁面7130-7134
頁數5
ISBN(電子)9781450392037
DOIs
出版狀態Published - 2022 10月 10
事件30th ACM International Conference on Multimedia, MM 2022 - Lisboa, Portugal
持續時間: 2022 10月 102022 10月 14

出版系列

名字MM 2022 - Proceedings of the 30th ACM International Conference on Multimedia

Conference

Conference30th ACM International Conference on Multimedia, MM 2022
國家/地區Portugal
城市Lisboa
期間22-10-1022-10-14

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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