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Case-Based Reasoning and Attribute Features Mining for Posting-Popularity Prediction: A Case Study in the Online Automobile Community

Author

Listed:
  • Tingting Zhao

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Jie Lin

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Zhenyu Zhang

    (School of Economics and Management, Tongji University, Shanghai 200092, China
    School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China)

Abstract

Social media is in a dynamic environment of real-time interaction, and users generate overwhelming and high-dimensional information at all times. A new case-based reasoning (CBR) method combined with attribute features mining for posting-popularity prediction in online communities is explored from the perspective of imitating human knowledge reasoning in artificial intelligence. To improve the quality of algorithms for CBR approach retrieval and extraction and describe high-dimensional network information in the form of the CBR case, the idea of intrinsically interpretable attribute features is proposed. Based on the theory and research of the social network combined with computer technology of data analysis and text mining, useful information could be successfully collected from massive network information, from which the simple information features and covered information features are summarized and extracted to explain the popularity of the online automobile community. We convert complex network information into a set of interpretable attribute features of different data types and construct the CBR approach presentation system of network postings. Moreover, this paper constructs the network posting cases database suitable for the social media network environment. To deal with extreme situations caused by network application scenarios, trimming suggestions and methods for similar posting cases of the network community have been provided. The case study shows that the developed posting popularity prediction method is suitable for the complex social network environment and can effectively support decision makers to fully use the experience and knowledge of historical cases and find an excellent solution to forecasting popularity in the network community.

Suggested Citation

  • Tingting Zhao & Jie Lin & Zhenyu Zhang, 2022. "Case-Based Reasoning and Attribute Features Mining for Posting-Popularity Prediction: A Case Study in the Online Automobile Community," Mathematics, MDPI, vol. 10(16), pages 1-28, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2868-:d:885734
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    References listed on IDEAS

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    1. Liao, Shu-Hsien & Widowati, Retno & Hsieh, Yu-Chieh, 2021. "Investigating online social media users’ behaviors for social commerce recommendations," Technology in Society, Elsevier, vol. 66(C).
    2. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    3. Jinhui He & Huirong Zhang & Zhenyu Zhang & Jiaping Zhang & Ching-Feng Wen, 2021. "Probabilistic Linguistic Three-Way Multi-Attibute Decision Making for Hidden Property Evaluation of Judgment Debtor," Journal of Mathematics, Hindawi, vol. 2021, pages 1-16, May.
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    1. Guenwoo Lee & Ayu Pratiwi & Farikhah & Aya Suzuki & Takashi Kurosaki, 2023. "Do online communities of practice complement or substitute conventional agricultural extension services? Evidence from Indonesian shrimp farmers' participation in a Facebook group," Working Papers e183, Tokyo Center for Economic Research.

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