Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics
A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, publ...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/5551718 |
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| _version_ | 1850165763424387072 |
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| author | Xiaoliang Chen Xiang Lan Jihong Wan Peng Lu Ming Yang |
| author_facet | Xiaoliang Chen Xiang Lan Jihong Wan Peng Lu Ming Yang |
| author_sort | Xiaoliang Chen |
| collection | DOAJ |
| description | A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N-SEP2M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N-SEP2M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic. |
| format | Article |
| id | doaj-art-edbeb66cd96d4161bbd179e043fa37d6 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-edbeb66cd96d4161bbd179e043fa37d62025-08-20T02:21:39ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/55517185551718Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series CharacteristicsXiaoliang Chen0Xiang Lan1Jihong Wan2Peng Lu3Ming Yang4School of Computer and Software Engineering, Xihua University, Chengdu 610039, ChinaInstitute of Statistical Science, Sichuan Provincial Bureau of Statistics, Chengdu 610041, ChinaInstitute of Artificial Intelligence, School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, ChinaDepartment of Computer Science and Operations Research, University of Montreal, Montreal QC H3C3J7, CanadaSchool of Computer and Software Engineering, Xihua University, Chengdu 610039, ChinaA growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N-SEP2M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N-SEP2M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.http://dx.doi.org/10.1155/2021/5551718 |
| spellingShingle | Xiaoliang Chen Xiang Lan Jihong Wan Peng Lu Ming Yang Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics Discrete Dynamics in Nature and Society |
| title | Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics |
| title_full | Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics |
| title_fullStr | Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics |
| title_full_unstemmed | Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics |
| title_short | Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics |
| title_sort | evolutionary prediction of nonstationary event popularity dynamics of weibo social network using time series characteristics |
| url | http://dx.doi.org/10.1155/2021/5551718 |
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