Modeling Online Collective Emotions Through Knowledge Transfer
Saike He1; Xiaolong Zheng1; Daniel Zeng1,2
2017-08-18
Conference NameIEEE Intelligence and Security Informatics 2017 Conference (ISI 2017)
Source PublicationIntelligence and Security Informatics (ISI), 2017 IEEE International Conference on
Conference Date22-24 July 2017
Conference PlaceBeijing, China
AbstractOnline emotion diffusion is a compound process that involves interactions with multiple modalities. For instance, different behaviors influence the velocity and scale of emotion diffusion in online communities. Depicting and predicting massive online emotions helps to guide the trend of emotion evolution, thus avoiding unprecedented damages in crises. However, most existing work tries to depict and predict online emotions based on models not considering related modalities. There still lacks an efficient modeling framework that promotes performance by leveraging multi-modality knowledge, and quantifies the interactions among different modalities. In this paper, we elaborate a computational model to jointly depict online emotions and behaviors. By introducing a common structure, we can quantify how user emotions interact with the corresponding behaviors. To scale up to large dataset, we propose a hierarchical optimization algorithm to accelerate the convergence of the model. Evaluation on Sina Weibo dataset suggests that prediction error rate is lowered by 69 percent with the proposed model. In addition, the proposed model helps to explain how user emotions influence consequent behaviors in extreme situations.
KeywordOnline Emotions Knowledge Transfer Social Crises
DOI10.1109/ISI.2017.8004909
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15356
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorXiaolong Zheng
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Saike He,Xiaolong Zheng,Daniel Zeng. Modeling Online Collective Emotions Through Knowledge Transfer[C],2017.
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