Entity Matters in News: An Association Network-Enhanced Method for News Reprint Prediction
文献类型：期刊论文
| 作者 | Li, Qiudan3 ; Liu, Hejing2,3; Yao, Riheng2,3; Xu, David Jingjun1; Zeng, Daniel D.2,3
|
| 刊名 | IEEE INTELLIGENT SYSTEMS
 |
| 出版日期 | 2022 |
| 卷号 | 37期号:1页码:99-107 |
| ISSN号 | 1541-1672 |
| DOI | 10.1109/MIS.2021.3121274 |
| 通讯作者 | Li, Qiudan(qiudan.li@ia.ac.cn) |
| 英文摘要 | Reprint is a fast and efficient way for news media to spread information and plays an increasingly important role in evaluating the influence of media and building a brand image. News reprint prediction is a novel research question in the news diffusion field, which predicts whether a news media will reprint a piece of news in the future. During reprinting, an association network among media and entities in the news is formed that reflects their multidimensional dynamic interaction. Existing research primarily focuses on integrating reprint historical records and news content while reprint prediction considering the associations among media and entities in the news remains under-researched. This work develops an entity association network-enhanced method for news reprint prediction, which adopts HIN2Vec to model the dynamic interaction and incorporates the learned embedding and news content through attention mechanism to generate entity-specific content representation. The efficacy of the proposed method is validated on real-world news reprint data. Experimental results show that the fusion of entity association network helps improve the performance of reprint prediction. This research contributes to media communication literature and has significant practical implications. |
| 资助项目 | National Key Research and Development Program of China[2020AAA0103405] ; National Natural Science Foundation of China[62071467] ; National Natural Science Foundation of China[71902179] ; National Natural Science Foundation of China[71621002] ; City University of Hong Kong[7005380] ; City University of Hong Kong[7005193] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27030100] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE COMPUTER SOC |
| WOS记录号 | WOS:000792916700012 |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; City University of Hong Kong ; Strategic Priority Research Program of Chinese Academy of Sciences |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49443]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 通讯作者 | Li, Qiudan |
| 作者单位 | 1.City Univ Hong Kong, Coll Business, Dept Informat Syst, Hong Kong, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Qiudan,Liu, Hejing,Yao, Riheng,et al. Entity Matters in News: An Association Network-Enhanced Method for News Reprint Prediction[J]. IEEE INTELLIGENT SYSTEMS,2022,37(1):99-107. |
| APA | Li, Qiudan,Liu, Hejing,Yao, Riheng,Xu, David Jingjun,&Zeng, Daniel D..(2022).Entity Matters in News: An Association Network-Enhanced Method for News Reprint Prediction.IEEE INTELLIGENT SYSTEMS,37(1),99-107. |
| MLA | Li, Qiudan,et al."Entity Matters in News: An Association Network-Enhanced Method for News Reprint Prediction".IEEE INTELLIGENT SYSTEMS 37.1(2022):99-107. |
入库方式： OAI收割
来源：自动化研究所
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