PSEISMIC: A personalized self-exciting point process model for predicting tweet popularity

Hsin Yu Chen, Cheng-Te Li

研究成果: Conference contribution

1 引文 (Scopus)

摘要

Social networking websites allow users to create and share a variety of items. Big information cascades of post resharing can be generated because users of these sites reshare each other's posts with their friends and followers. In this work, we aim at predicting the final number of reshares for any given post. We build on the theory of self-exciting point processes to develop a statistical model, PSEISMIC, which leads to accurate predictions of popularity. Moreover, we perform cluster analysis to group all tweets so that the coefficient of memory kernel in PSEISMIC can be estimated for every cluster, rather than using the same memory kernel. Experiments conducted on a large-scale retweet dataset show that the proposed PSEISMIC model outperforms the state-of-the-art approach, SEISMIC in predicting the popularity of a given post.

原文English
主出版物標題Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
編輯Jian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2710-2713
頁數4
ISBN(電子)9781538627143
DOIs
出版狀態Published - 2017 七月 1
事件5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
持續時間: 2017 十二月 112017 十二月 14

出版系列

名字Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
國家United States
城市Boston
期間17-12-1117-12-14

指紋

Memory Kernel
Point Process
Process Model
Data storage equipment
Social Networking
Cluster analysis
Cluster Analysis
Statistical Model
Cascade
Websites
Prediction
Coefficient
Experiment
Experiments
Process model
Kernel
Point process
Model
Statistical Models
Coefficients

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

引用此文

Chen, H. Y., & Li, C-T. (2017). PSEISMIC: A personalized self-exciting point process model for predicting tweet popularity. 於 J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, ... M. Toyoda (編輯), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (頁 2710-2713). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; 卷 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258234
Chen, Hsin Yu ; Li, Cheng-Te. / PSEISMIC : A personalized self-exciting point process model for predicting tweet popularity. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. 編輯 / Jian-Yun Nie ; Zoran Obradovic ; Toyotaro Suzumura ; Rumi Ghosh ; Raghunath Nambiar ; Chonggang Wang ; Hui Zang ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Xiaohua Hu ; Jeremy Kepner ; Alfredo Cuzzocrea ; Jian Tang ; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. 頁 2710-2713 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017).
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title = "PSEISMIC: A personalized self-exciting point process model for predicting tweet popularity",
abstract = "Social networking websites allow users to create and share a variety of items. Big information cascades of post resharing can be generated because users of these sites reshare each other's posts with their friends and followers. In this work, we aim at predicting the final number of reshares for any given post. We build on the theory of self-exciting point processes to develop a statistical model, PSEISMIC, which leads to accurate predictions of popularity. Moreover, we perform cluster analysis to group all tweets so that the coefficient of memory kernel in PSEISMIC can be estimated for every cluster, rather than using the same memory kernel. Experiments conducted on a large-scale retweet dataset show that the proposed PSEISMIC model outperforms the state-of-the-art approach, SEISMIC in predicting the popularity of a given post.",
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Chen, HY & Li, C-T 2017, PSEISMIC: A personalized self-exciting point process model for predicting tweet popularity. 於 J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (編輯), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 卷 2018-January, Institute of Electrical and Electronics Engineers Inc., 頁 2710-2713, 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 17-12-11. https://doi.org/10.1109/BigData.2017.8258234

PSEISMIC : A personalized self-exciting point process model for predicting tweet popularity. / Chen, Hsin Yu; Li, Cheng-Te.

Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. 編輯 / Jian-Yun Nie; Zoran Obradovic; Toyotaro Suzumura; Rumi Ghosh; Raghunath Nambiar; Chonggang Wang; Hui Zang; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Xiaohua Hu; Jeremy Kepner; Alfredo Cuzzocrea; Jian Tang; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2710-2713 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; 卷 2018-January).

研究成果: Conference contribution

TY - GEN

T1 - PSEISMIC

T2 - A personalized self-exciting point process model for predicting tweet popularity

AU - Chen, Hsin Yu

AU - Li, Cheng-Te

PY - 2017/7/1

Y1 - 2017/7/1

N2 - Social networking websites allow users to create and share a variety of items. Big information cascades of post resharing can be generated because users of these sites reshare each other's posts with their friends and followers. In this work, we aim at predicting the final number of reshares for any given post. We build on the theory of self-exciting point processes to develop a statistical model, PSEISMIC, which leads to accurate predictions of popularity. Moreover, we perform cluster analysis to group all tweets so that the coefficient of memory kernel in PSEISMIC can be estimated for every cluster, rather than using the same memory kernel. Experiments conducted on a large-scale retweet dataset show that the proposed PSEISMIC model outperforms the state-of-the-art approach, SEISMIC in predicting the popularity of a given post.

AB - Social networking websites allow users to create and share a variety of items. Big information cascades of post resharing can be generated because users of these sites reshare each other's posts with their friends and followers. In this work, we aim at predicting the final number of reshares for any given post. We build on the theory of self-exciting point processes to develop a statistical model, PSEISMIC, which leads to accurate predictions of popularity. Moreover, we perform cluster analysis to group all tweets so that the coefficient of memory kernel in PSEISMIC can be estimated for every cluster, rather than using the same memory kernel. Experiments conducted on a large-scale retweet dataset show that the proposed PSEISMIC model outperforms the state-of-the-art approach, SEISMIC in predicting the popularity of a given post.

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BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

A2 - Nie, Jian-Yun

A2 - Obradovic, Zoran

A2 - Suzumura, Toyotaro

A2 - Ghosh, Rumi

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Zang, Hui

A2 - Baeza-Yates, Ricardo

A2 - Baeza-Yates, Ricardo

A2 - Hu, Xiaohua

A2 - Kepner, Jeremy

A2 - Cuzzocrea, Alfredo

A2 - Tang, Jian

A2 - Toyoda, Masashi

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Chen HY, Li C-T. PSEISMIC: A personalized self-exciting point process model for predicting tweet popularity. 於 Nie J-Y, Obradovic Z, Suzumura T, Ghosh R, Nambiar R, Wang C, Zang H, Baeza-Yates R, Baeza-Yates R, Hu X, Kepner J, Cuzzocrea A, Tang J, Toyoda M, 編輯, Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2710-2713. (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017). https://doi.org/10.1109/BigData.2017.8258234