A Partition and Interaction Combined Model for Social Event Popularity Prediction

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	A Partition and Interaction Combined Model for Social Event Popularity Prediction
	Guandan Chen; Qingchao Kong; Wenji Mao; Daniel Zeng
	2018
会议名称	IEEE Intelligence and Security Informatics (ISI) 2018
会议日期	2018.11.08-2018.11.10
会议地点	Miami, FL, USA
摘要	Social media platforms make the spread of social event information quicker and more convenient. Some of these social events may become hot topics, which highlights the importance of event popularity prediction in public management， decision making and other security related applications. Due to the complexity of social event itself, it has two unique characteristics which most previous popularity prediction work has ignored: (1) the discussion of an event itself may consist of several components, e.g. different sub-events, different stances or different user communities; (2) the popularity of an event can be influenced by other related events. To address its unique characteristics, we propose an event popularity prediction model combining partition and interaction. We employ reinforcement learning to automatically partition an event into components and recognize related events. Then we predict event popularity by modeling component information and interactions between related events. Experimental results on a real world dataset show that our proposed model can outperform the competitive baseline methods.
关键词	Popularity Prediction Information Cascade Reinforcement Learning
收录类别	EI
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21798
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
作者单位	1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China 2.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China 3.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China 4.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China
推荐引用方式 GB/T 7714	Guandan Chen,Qingchao Kong,Wenji Mao,et al. A Partition and Interaction Combined Model for Social Event Popularity Prediction[C],2018.

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