Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory

Abstract The subject of collective attention is in the center of this era of information explosion. It is thus of great interest to understand the fundamental mechanism underlying attention in large populations within a complex evolving system. Moreover, an ability to predict the dynamic process of...

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Autores principales:	Peng Bao, Xiaoxia Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/91a3548d02ff47229d82ed2de106e0f2
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spelling	oai:doaj.org-article:91a3548d02ff47229d82ed2de106e0f22021-12-02T15:06:15ZUncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory10.1038/s41598-017-02826-62045-2322https://doaj.org/article/91a3548d02ff47229d82ed2de106e0f22017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02826-6https://doaj.org/toc/2045-2322Abstract The subject of collective attention is in the center of this era of information explosion. It is thus of great interest to understand the fundamental mechanism underlying attention in large populations within a complex evolving system. Moreover, an ability to predict the dynamic process of collective attention for individual items has important implications in an array of areas. In this report, we propose a generative probabilistic model using a self-excited Hawkes process with survival theory to model and predict the process through which individual items gain their attentions. This model explicitly captures three key ingredients: the intrinsic attractiveness of an item, characterizing its inherent competitiveness against other items; a reinforcement mechanism based on sum of each previous attention triggers; and a power-law temporal relaxation function, corresponding to the aging in the ability to attract new attentions. Experiments on two population-scale datasets demonstrate that this model consistently outperforms the state-of-the-art methods.Peng BaoXiaoxia ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-8 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peng Bao Xiaoxia Zhang Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory
description	Abstract The subject of collective attention is in the center of this era of information explosion. It is thus of great interest to understand the fundamental mechanism underlying attention in large populations within a complex evolving system. Moreover, an ability to predict the dynamic process of collective attention for individual items has important implications in an array of areas. In this report, we propose a generative probabilistic model using a self-excited Hawkes process with survival theory to model and predict the process through which individual items gain their attentions. This model explicitly captures three key ingredients: the intrinsic attractiveness of an item, characterizing its inherent competitiveness against other items; a reinforcement mechanism based on sum of each previous attention triggers; and a power-law temporal relaxation function, corresponding to the aging in the ability to attract new attentions. Experiments on two population-scale datasets demonstrate that this model consistently outperforms the state-of-the-art methods.
format	article
author	Peng Bao Xiaoxia Zhang
author_facet	Peng Bao Xiaoxia Zhang
author_sort	Peng Bao
title	Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory
title_short	Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory
title_full	Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory
title_fullStr	Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory
title_full_unstemmed	Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory
title_sort	uncovering and predicting the dynamic process of collective attention with survival theory
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/91a3548d02ff47229d82ed2de106e0f2
work_keys_str_mv	AT pengbao uncoveringandpredictingthedynamicprocessofcollectiveattentionwithsurvivaltheory AT xiaoxiazhang uncoveringandpredictingthedynamicprocessofcollectiveattentionwithsurvivaltheory
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Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory

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