Research on the Method of Identifying Opinion Leaders Based on Online Word-of-Mouth

  • Chenglin He
  • Shan LiEmail author
  • Yehui Yao
  • Yu Ding
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


Opinion leaders are attracting increasing attention on practitioners and academics. Opinion leaders’ online Word-of-Mouth (WOM) plays a guiding and decisive role in reducing risks and uncertainty faced by users in online shopping. It is of great significance of businesses and enterprises to effectively identify opinion leaders. This study proposes an integrated method by looking at not only essential indicators of reviewers but also the review characteristics. The RFM model is used to evaluate the activity of reviewers. Four variables L (text length), T (period time), P (with or without a picture) and S (sentiment intensity) are derived to measure review helpfulness from review text. And two effective networks are built using the Artificial Neural Network (ANN). This study utilizes a real-life data set from for analysis and designs three different experiments to verify the identification effect. The results show that this method can scientifically and effectively identify the opinion leaders and analyze the influence of opinion leaders.


Online WOM Opinion leader ANN RFM WOM content 


  1. 1.
    X. Lu, S. Ba, L. Huang, Y. Feng, Promotional marketing or word-of-mouth? Evidence from online restaurant reviews. Inf. Syst. Res. 24(3), 596–612 (2013)CrossRefGoogle Scholar
  2. 2.
    B. Gu, J. Park, P. Konana, The impact of external word-of-mouth sources on retailer sales of high-involvement products. Inf. Syst. Res. 23(1), 182–196 (2012)CrossRefGoogle Scholar
  3. 3.
    A.H. Huang, K. Chen, D.C. Yen, T.P. Tran, A study of factors that contribute to online review helpfulness. Comput. Hum. Behav. 48(C), 17–27 (2015)CrossRefGoogle Scholar
  4. 4.
    X. Yan, J. Wang, M. Chau, Customer revisit intention to restaurants: evidence from online reviews. Inf. Syst. Front. 17(3), 645–657 (2015)CrossRefGoogle Scholar
  5. 5.
    C. Wu, H. Che, T. Chan, X. Lu, The economic value of online reviews. Mark. Sci. 34(5), 739–754 (2015)CrossRefGoogle Scholar
  6. 6.
    H. Choi, S. Kim, J. Lee, Role of network structure and network effects in diffusion of innovations. Ind. Mark. Manage. 39, 170–177 (2010)CrossRefGoogle Scholar
  7. 7.
    S. Mohammadi, A. Andalib, Using the opinion leaders in social networks to improve the cold start challenge in recommender systems, in 2017 3th International Conference on Web Research (ICWR), (IEEE, Tehran, Iran, 2017), pp. 62–66Google Scholar
  8. 8.
    S.B. Yudhoatmojo, I. Budi, F.K. Dewi, Identification of opinion leader on rumor spreading in online social network twitter using edge weighting and centrality measure weighting. in 2017 Twelfth International Conference on Digital Information Management (ICDIM) (IEEE, Fukuoka, Japan 2018), pp. 313–318Google Scholar
  9. 9.
    Y. Ma, X. Shu, S. Shen, J. Song, G. Li, Q. Liu, Study on network public opinion dissemination and coping strategies in large fire disasters. Procedia Eng. 71, 616–621 (2014)CrossRefGoogle Scholar
  10. 10.
    M. Shinde, S. Girase, Identification of topic-specific opinion leader using SPEAR algorithm in online knowledge communities, 2016 International Conference on Computing, Analytics and Security Trends (CAST) (IEEE, Pune, India, 2017), pp. 144–149Google Scholar
  11. 11.
    X. Yu, X. Wei, X. Lin, Algorithms of BBS opinion leader mining based on sentiment analysis. in WISM 2010: Web Information Systems and Mining (Springer, Sanya, China 2010), pp. 360–369CrossRefGoogle Scholar
  12. 12.
    Y. Cho, J. Hwang, D. Lee, Identification of effective opinion leaders in the diffusion of technological innovation: A social network approach. Technol. Forecast. Soc. Chang. 79, 97–106 (2012)CrossRefGoogle Scholar
  13. 13.
    A. Ellero, A. Sorato, G. Fasano, A new model for estimating the probability of information spreading with opinion leaders. Department of Management at Università Ca’ Foscari Venezia working Paper No. 13, Italy (2011)Google Scholar
  14. 14.
    Y. Ma, S. Cai, R. Wang, Study on the method of identifying opinion leaders based on online customer reviews. in 2011 International Conference on Management Science & Engineering (IEEE, Rome, Italy 2011), pp. 10–17Google Scholar
  15. 15.
    J. Weng, E.P. Lim, J. Jiang, Q. He, TwitterRank: finding topic-sensitive influential twitterers. in Proceedings of the third ACM International Conference on Web Search and Data Mining (ACM, New York 2010), pp. 261–270Google Scholar
  16. 16.
    W. Zhang, X. Li, H. He, X. Wang, Identifying network public opinion leaders based on markov logic networks. Sci. World J., 1–8 (2014)Google Scholar
  17. 17.
    Q. Miao, Y. Meng, J. Sun, Identifying the most influential topic-sensitive opinion leaders in online review communities. in 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (IEEE, Chengdu, China 2016), pp. 330–335Google Scholar
  18. 18.
    Y. Liu, J. Jin, P. Ji, A.H. Jenny, Y.K.F. Richard, Identifying helpful online review: a product designer’s perspective. Comput. Aided Des. 45(2), 180–194 (2013)CrossRefGoogle Scholar
  19. 19.
    M.S.I. Malik, A. Hussain, An analysis of review content and reviewer variables that contribute to review helpfulness. Inf. Process. Manage. 54, 88–104 (2018)CrossRefGoogle Scholar
  20. 20.
    H. Hong, D. Xu, G. Wang, W. Fan, Understanding the determinants of online review helpfulness: a meta-analytic investigation. Decis. Support Syst. 102, 1–11 (2017)CrossRefGoogle Scholar
  21. 21.
    S.M. Mudambi, D. Schuff, What makes a helpful review? A study of customer reviews on Amazon. Com. MIS Q. 34(1), 185–200 (2010)CrossRefGoogle Scholar
  22. 22.
    Y. Hu, K. Chen, P. Lee, The effect of user-controllable filters on the prediction of online hotel reviews. Inf. Manag. 54, 728–744 (2017)CrossRefGoogle Scholar
  23. 23.
    A.Y. Chua, S. Banerjee, Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Comput. Hum. Behav. 54, 547–554 (2016)CrossRefGoogle Scholar
  24. 24.
    Q. Yan, Y. Meng, Factors affecting the perceived helpfulness of online reviews—an empirical study based on online film reviews. Chin. J. Manag. Sci. S1, 26–131 (2013). 闫强, 孟跃: 在线评论的感知有用性影响因素——基于在线影评的实证研究.中国管理科学 (S1), 26–131 (2013)Google Scholar
  25. 25.
    Y. Yang, Y. Zhu, No picture, no truth? The effect of pictorial and verbal service online reviews on consumer attitudes. Psychol. Explor. 34(1), 83–89 (2014). 杨颖, 朱毅: 无图无真相?图片和文字网络评论对服务产品消费者态度的影响.心理学探新 34(1), 83–89 (2014)Google Scholar
  26. 26.
    S. Chen, Research on the relationship between picture form word-of-mouth features and adoption effect. Huazhong University of Science and Technology, Hubei, China (2013). 陈珊珊: 图片形式网络口碑信息特性与其采纳效果的关系研究.华中科技大学, 湖北 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Economics and ManagementNanjing University of Aeronautics and AstronauticsNanjingChina

Personalised recommendations