research-article

Predicting the popularity of viral topics based on time series forecasting

Authors:

Bowen NingAuthors Info & Claims

Neurocomputing, Volume 210, Issue C

Pages 55 - 65

https://doi.org/10.1016/j.neucom.2015.10.143

Published: 19 October 2016 Publication History

Abstract

Thanks to social media platforms, topics are diffused online. The ones which diffuse rapidly and widely are known as viral topics. Predicting the popularity of viral topics is important to online recommendation systems and marketing services. However, the task is still challenging due to the fact that the popularity of viral topics is highly dynamic and little research has been focused on how to predict the popularity accurately for viral topics. This paper first uses data collected from the largest BBS in China to find high correlations for the short-term popularity of viral topics. Based on this finding, this paper then utilizes a time series feature space to capture the behaviors of popularity of viral topics and presents a method for predicting the short-term popularity of a given viral topic by using only data of historical popularity of the topic. This paper demonstrates that our method outperforms a baseline model and one of the most sophisticated methods in the terms of MAPE (mean absolute percentage error) and RAE (relative absolute error). Furthermore, this paper gives out how long the minimum observation period is in order to predict promptly. Finally, this paper uses two cases to show the effectiveness and simplicity of our method. HighlightsThis paper finds high correlations for the short-term popularity of viral topics, so it is rational to predict the short-term popularity by using historical popularity for viral topics.This paper defines a time series feature space to capture the behaviors of popularity of viral topics and shows that it can be used to understand how the popularity of viral topics evolves over time.This paper develops a method for predicting the short-term popularity of viral topics based on time series forecasting by using only data of historical popularity of a given viral topic. The experimental results show that it performs much better than a baseline model and one of the most sophisticated methods.

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Cited By

King KWang B(2023)Diffusion of real versus misinformation during a crisis eventInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2021.10239071:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2021.102390
Liu AWang XXu NLiu JSu YZhang QZhang STang YGuo JJin GLi X(2023)SMPC: boosting social media popularity prediction with captionMultimedia Systems10.1007/s00530-022-01030-529:2(577-586)Online publication date: 19-Jan-2023
https://dl.acm.org/doi/10.1007/s00530-022-01030-5
Zhu HMei YWei JShen C(2022)Prediction of online topics’ popularity patternsJournal of Information Science10.1177/016555152096102648:2(141-151)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1177/0165551520961026
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cover image Neurocomputing

Neurocomputing Volume 210, Issue C

October 2016

303 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 19 October 2016

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Cited By

King KWang B(2023)Diffusion of real versus misinformation during a crisis eventInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2021.10239071:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2021.102390
Liu AWang XXu NLiu JSu YZhang QZhang STang YGuo JJin GLi X(2023)SMPC: boosting social media popularity prediction with captionMultimedia Systems10.1007/s00530-022-01030-529:2(577-586)Online publication date: 19-Jan-2023
https://dl.acm.org/doi/10.1007/s00530-022-01030-5
Zhu HMei YWei JShen C(2022)Prediction of online topics’ popularity patternsJournal of Information Science10.1177/016555152096102648:2(141-151)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1177/0165551520961026
Hoang TMothe J(2022)Prediction of brand stories spreading on social networksAdvances in Data Analysis and Classification10.1007/s11634-021-00450-x16:3(559-591)Online publication date: 1-Sep-2022
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Alijani STanha JMohammadkhanli (2022)An ensemble of deep learning algorithms for popularity prediction of flickr imagesMultimedia Tools and Applications10.1007/s11042-021-11517-481:3(3253-3274)Online publication date: 1-Jan-2022
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Wang XFang BZhang HWang X(2020)Predicting the security threats on the spreading of rumor, false information of Facebook content based on the principle of sociologyComputer Communications10.1016/j.comcom.2019.11.042150:C(455-462)Online publication date: 15-Jan-2020
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Chen GKong QXu NMao W(2019)NPPNeurocomputing10.1016/j.neucom.2018.12.039333:C(221-230)Online publication date: 14-Mar-2019
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Kou FDu JYang CShi YLiang MXue ZLi H(2019)A multi-feature probabilistic graphical model for social network semantic searchNeurocomputing10.1016/j.neucom.2018.03.086336:C(67-78)Online publication date: 7-Apr-2019
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Wang XFang BZhang HWang Y(2019)A competitive model of popularity prediction using correlation matrixCluster Computing10.1007/s10586-017-1461-822:2(2747-2759)Online publication date: 1-Mar-2019
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Song YLi AQuan YZhu JLin E(2018)Topics' popularity prediction based on ARMA modelProceedings of 2018 International Conference on Mathematics and Artificial Intelligence10.1145/3208788.3208799(68-72)Online publication date: 20-Apr-2018
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