IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i9p220-d621078.html
   My bibliography  Save this article

Trend Prediction of Event Popularity from Microblogs

Author

Listed:
  • Xujian Zhao

    (School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China)

  • Wei Li

    (School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China)

Abstract

Owing to rapid development of the Internet and the rise of the big data era, microblog has become the main means for people to spread and obtain information. If people can accurately predict the development trend of a microblog event, it will be of great significance for the government to carry out public relations activities on network event supervision and guide the development of microblog event reasonably for network crisis. This paper presents effective solutions to deal with trend prediction of microblog events’ popularity. Firstly, by selecting the influence factors and quantifying the weight of each factor with an information entropy algorithm, the microblog event popularity is modeled. Secondly, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of microblog event. Then, the box chart method is used to divide the popularity of microblog event into various trend spaces. In addition, this paper exploits the Bi-LSTM model to deal with trend prediction with a sequence to label model. Finally, the comparative experimental analysis is carried out on two real data sets crawled from Sina Weibo platform. Compared to three comparative methods, the experimental results show that our proposal improves F1-score by up to 39%.

Suggested Citation

  • Xujian Zhao & Wei Li, 2021. "Trend Prediction of Event Popularity from Microblogs," Future Internet, MDPI, vol. 13(9), pages 1-13, August.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:220-:d:621078
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/9/220/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/13/9/220/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yin, Fulian & Pang, Hongyu & Xia, Xinyu & Shao, Xueying & Wu, Jianhong, 2021. "COVID-19 information contact and participation analysis and dynamic prediction in the Chinese Sina-microblog," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    2. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Fang, Siwei & Zhao, Nan & Chen, Nan & Xiong, Fei & Yi, Yunhui, 2019. "Analyzing and predicting network public opinion evolution based on group persuasion force of populism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 809-824.
    4. Liang, Tao & Zhao, Qing & Lv, Qingzhao & Sun, Hexu, 2021. "A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers," Energy, Elsevier, vol. 230(C).
    5. Pan, Jun-Shan & Li, Yuan-Qi & Hu, Han-Ping & Hu, Yong, 2021. "Modeling collective behavior of posting microblogs by stochastic differential equation with jump," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    2. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    4. Fan Zou & Yupeng Li & Jiahuan Huang, 2022. "Group interaction and evolution of customer reviews based on opinion dynamics towards product redesign," Electronic Commerce Research, Springer, vol. 22(4), pages 1131-1151, December.
    5. Jinyuan Liu & Shouxi Wang & Nan Wei & Yi Yang & Yihao Lv & Xu Wang & Fanhua Zeng, 2023. "An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting," Energies, MDPI, vol. 16(3), pages 1-14, January.
    6. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
    7. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
    8. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    9. Zhichao Li, 2022. "Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    10. Geng, Xiulin & Xu, Lingyu & He, Xiaoyu & Yu, Jie, 2021. "Graph optimization neural network with spatio-temporal correlation learning for multi-node offshore wind speed forecasting," Renewable Energy, Elsevier, vol. 180(C), pages 1014-1025.
    11. Wei, Nan & Yin, Chuang & Yin, Lihua & Tan, Jingyi & Liu, Jinyuan & Wang, Shouxi & Qiao, Weibiao & Zeng, Fanhua, 2024. "Short-term load forecasting based on WM algorithm and transfer learning model," Applied Energy, Elsevier, vol. 353(PA).
    12. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update," Applied Energy, Elsevier, vol. 340(C).
    13. Yong-Ju Jang & Min-Seung Kim & Chan-Ho Lee & Ji-Hye Choi & Jeong-Hee Lee & Sun-Hong Lee & Tae-Eung Sung, 2022. "A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 Pandemic," IJERPH, MDPI, vol. 19(11), pages 1-22, June.
    14. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    15. Li, Jiale & Song, Zihao & Wang, Xuefei & Wang, Yanru & Jia, Yaya, 2022. "A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD," Energy, Elsevier, vol. 251(C).
    16. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
    17. Fei Chen & Zhiyang Wang & Yu He, 2023. "A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids," Energies, MDPI, vol. 16(22), pages 1-17, November.
    18. Xiaoxun, Zhu & Zixu, Xu & Yu, Wang & Xiaoxia, Gao & Xinyu, Hang & Hongkun, Lu & Ruizhang, Liu & Yao, Chen & Huaxin, Liu, 2023. "Research on wind speed behavior prediction method based on multi-feature and multi-scale integrated learning," Energy, Elsevier, vol. 263(PA).
    19. Ren, Yuting & Li, Zhuolin & Xu, Lingyu & Yu, Jie, 2023. "The data-based adaptive graph learning network for analysis and prediction of offshore wind speed," Energy, Elsevier, vol. 267(C).
    20. Wenchao Ban & Liangduo Shen, 2022. "PM2.5 Prediction Based on the CEEMDAN Algorithm and a Machine Learning Hybrid Model," Sustainability, MDPI, vol. 14(23), pages 1-15, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:220-:d:621078. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.