Popularity prediction of social media based on multi-modal feature mining

Chih Chung Hsu, Jun Yi Lee, Li Wei Kang, Zhong Xuan Zhang, Chia Yen Lee, Shao Min Wu

研究成果: 書貢獻/報告類型會議論文篇章

5 引文 斯高帕斯(Scopus)

摘要

Popularity prediction of social media becomes a more attractive issue in recent years. It consists of multi-type data sources such as image, meta-data, and text information. In order to effectively predict the popularity of a specified post in the social network, fusing multi-feature from heterogeneous data is required. In this paper, a popularity prediction framework for social media based on multi-modal feature mining is presented. First, we discover image semantic features by extracting their image descriptions generated by image captioning. Second, an effective text-based feature engineering is used to construct an effective word-to-vector model. The trained word-to-vector model is used to encode the text information and the semantic image features. Finally, an ensemble regression approach is proposed to aggregate these encoded features and learn the final regressor. Extensive experiments show that the proposed method significantly outperforms other state-of-the-art regression models. We also show that the multi-modal approach could effectively improve the performance in the social media prediction challenge.

原文英語
主出版物標題MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
發行者Association for Computing Machinery, Inc
頁面2687-2691
頁數5
ISBN(電子)9781450368896
DOIs
出版狀態已發佈 - 2019 十月 15
事件27th ACM International Conference on Multimedia, MM 2019 - Nice, 法国
持續時間: 2019 十月 212019 十月 25

出版系列

名字MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia

會議

會議27th ACM International Conference on Multimedia, MM 2019
國家/地區法国
城市Nice
期間2019/10/212019/10/25

ASJC Scopus subject areas

  • 電腦科學(全部)
  • 媒體技術

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