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

Hsin Yu Chen, Cheng-Te Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-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
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2710-2713
Number of pages4
ISBN (Electronic)9781538627143
DOIs
Publication statusPublished - 2017 Jul 1
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: 2017 Dec 112017 Dec 14

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period17-12-1117-12-14

Fingerprint

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

Cite this

Chen, H. Y., & Li, C-T. (2017). PSEISMIC: A personalized self-exciting point process model for predicting tweet popularity. In 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 (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 2710-2713). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 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. editor / 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. pp. 2710-2713 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017).
@inproceedings{1647c026e68a4319be6b08c01d22cf5a,
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.",
author = "Chen, {Hsin Yu} and Cheng-Te Li",
year = "2017",
month = "7",
day = "1",
doi = "10.1109/BigData.2017.8258234",
language = "English",
series = "Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2710--2713",
editor = "Jian-Yun Nie and Zoran Obradovic and Toyotaro Suzumura and Rumi Ghosh and Raghunath Nambiar and Chonggang Wang and Hui Zang and Ricardo Baeza-Yates and Ricardo Baeza-Yates and Xiaohua Hu and Jeremy Kepner and Alfredo Cuzzocrea and Jian Tang and Masashi Toyoda",
booktitle = "Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017",
address = "United States",

}

Chen, HY & Li, C-T 2017, PSEISMIC: A personalized self-exciting point process model for predicting tweet popularity. in 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 (eds), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 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. ed. / 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; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceedingConference 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.

UR - http://www.scopus.com/inward/record.url?scp=85047747783&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047747783&partnerID=8YFLogxK

U2 - 10.1109/BigData.2017.8258234

DO - 10.1109/BigData.2017.8258234

M3 - Conference contribution

AN - SCOPUS:85047747783

T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

SP - 2710

EP - 2713

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. In 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, editors, 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