An iterative refinement approach for social media headline prediction

Chih Chung Hsu, Jun Yi Lee, Jing Wen Lin, Chia Yen Lee, Tsai Yne Hou, Ching Yi Hsueh, Hsiang Chin Chien, Ting Xuan Liao, Ying Chu Kuo, Zhong Xuan Zhang

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

12 Citations (Scopus)


In this study, we propose a novel iterative refinement approach to predict the popularity score of the social media meta-data effectively. With the rapid growth of the social media on the Internet, how to adequately forecast the view count or popularity becomes more important. Conventionally, the ensemble approach such as random forest regression achieves high and stable performance on various prediction tasks. However, most of the regression methods may not precisely predict the extreme high or low values. To address this issue, we first predict the initial popularity score and retrieve their residues. In order to correctly compensate those extreme values, we adopt an ensemble regressor to compensate the residues to further improve the prediction performance. Comprehensive experiments are conducted to demonstrate the proposed iterative refinement approach outperforms the state-of-the-art regression approach.

Original languageEnglish
Title of host publicationMM 2018 - Proceedings of the 2018 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages5
ISBN (Electronic)9781450356657
Publication statusPublished - 2018 Oct 15
Event26th ACM Multimedia conference, MM 2018 - Seoul, Korea, Republic of
Duration: 2018 Oct 222018 Oct 26

Publication series

NameMM 2018 - Proceedings of the 2018 ACM Multimedia Conference


Other26th ACM Multimedia conference, MM 2018
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction


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