Analyzing topics in social media for improving digital twinning based product development

Abstract

Digital twinning enables manufacturers to create digital representations of physical entities, thus implementing virtual simulations for product development. Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages, failing to cover the gap between physical and digital spaces. This work mines real-world consumer feedbacks through social media topics, which is significant to product development. We specifically analyze the prevalent time of a product topic, giving an insight into both consumer attention and the widely-discussed time of a product. The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution. Therefore, these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics. To this end, this work combines deep learning and survival analysis to predict the prevalent time of topics. We propose a specialized deep survival model which consists of two modules. The first module enriches input covariates by incorporating latent features of the time-varying text, and the second module fully captures the temporal pattern of a rumor by a recurrent network structure. Moreover, a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction. Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

Keywords

Digital twinning / Product development / Topic analysis / Social media

References

[1] T.H.-J. Uhlemann, C. Lehmann, R. Steinhilper, The digital twin: realizing the cyber-physical production system for industry 4.0, Procedia Cirp 61 (2017) 335-340.

[2] L. Prifti, M. Knigge, H. Kienegger, H. Krcmar,A Competency Model for ”Industrie 40” Employees. Proceedings der 13. Internationalen TagungWirtschaftsinformatik (WI 2017), St. Gallen, 2017, pp. 46-60.

[3] A.A. Olad, O.F. Valilai, Using of social media data analytics for applying digital twins in product development, in: Proceedings of the 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEE, 2020, pp. 319-323.

[4] W. Tang, G. Luo, Y. Wu, L. Tian, X. Zheng, Z. Cai, A second-order diffusion model for influence maximization in social networks, IEEE Transact. Comput. Soc. Syst. 6 (4) (2019) 702-714.

[5] R. Veenstra, J.K. Dijkstra, C. Steglich, M.H. Van Zalk, Network-behavior dynamics, J. Res. Adolesc. 23 (3) (2013) 399-412.

[6] W. Tang, B. Hui, L. Tian, G. Luo, Z. He, Z. Cai, Learning disentangled user representation with multi-view information fusion on social networks, Inf. Fusion 74 (2021) 77-86.

[7] W. Tang, L. Tian, X. Zheng, G. Luo, Z. He, Susceptible user search for defending opinion manipulation, Future Generat. Comput. Syst. 115 (2021) 531-541.

[8] W. Liu, Z.-H. Deng, X. Gong, F. Jiang, I.W. Tsang, Effectively predicting whether and when a topic will become prevalent in a social network, in: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI Press, 2015, pp. 210-216.

[9] S. Wang, Z. Yan, X. Hu, S.Y. Philip, Z. Li,Burst time prediction in cascades, in:Proceedings of the 2015 AAAI Conference on Artificial Intelligence 29, AAAI Press, 2015.

[10] E.L. Kaplan, P. Meier, Nonparametric estimation from incomplete observations, J. Am. Stat. Assoc. 53 (282) (1958) 457-481.

[11] D.R. Cox, Regression models and life-tables, J. Roy. Stat. Soc. B 34 (2) (1972) 187-202.

[12] C. Lee, W.R. Zame, J. Yoon, M. van der Schaar, Deephit: a deep learning approach to survival analysis with competing risks,in:Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, AAAI Press, 2018, pp. 2314-2321.

[13] K. Ren, J. Qin, L. Zheng, Z. Yang, W. Zhang, L. Qiu, Y. Yu, Deep recurrent survival analysis, in: Proceedings of the 2019 AAAI Conference on Artificial Intelligence, AAAI Press, 2019, pp. 1-8.

[14] M. Zimmer, N.J. Proferes, A topology of twitter research: disciplines, methods, and ethics, Aslib J. Inform. Manag. 66 (3) (2014) 250-261.

[15] Y. Lin, Z. Cai, X. Wang, F. Hao, Incentive mechanisms for crowdblocking rumors in mobile social networks, IEEE Trans. Veh. Technol. 68 (9) (2019) 9220-9232.

[16] X. Zheng, G. Luo, Z. Cai, A fair mechanism for private data publication in online social networks, IEEE Transact. Network Sci. Eng. 7 (2) (2018) 880-891.

[17] Z. Cai, Z. He, X. Guan, Y. Li, Collective data-sanitization for preventing sensitive information inference attacks in social networks, IEEE Trans. Dependable Secure Comput. 15 (4) (2016) 577-590.

[18] Z. He, Z. Cai, J. Yu, Latent-data privacy preserving with customized data utility for social network data, IEEE Trans. Veh. Technol. 67 (1) (2017) 665-673.

[19] M. Siddula, Y. Li, X. Cheng, Z. Tian, Z. Cai, Anonymization in online social networks based on enhanced equi-cardinal clustering, IEEE Transact. Comput. Soc. Syst. 6 (4)(2019) 809-820.

[20] F. Figueiredo, J.M. Almeida, M.A. Gonçalves, F. Benevenuto, Trendlearner: early prediction of popularity trends of user generated content, Inf. Sci. 349 (2016) 172-187.

[21] Y. Hu, C. Hu, S. Fu, P. Shi, B. Ning, Predicting the popularity of viral topics based on time series forecasting, Neurocomputing 210 (2016) 55-65.

[22] Q. Kong, W. Mao, G. Chen, D. Zeng, Exploring trends and patterns of popularity stage evolution in social media, IEEE Trans. Syst. Man Cybern. Syst. 50 (10) (2020) 3817-3827.

[23] S. Wang, C. Li, Z. Wang, H. Chen, K. Zheng, Bpfþþ: a unified factorization model for predicting retweet behaviors, Inf. Sci. 515 (2020) 218-232.

[24] D. Ding, M. Zhang, X. Pan, M. Yang, X. He, Modeling extreme events in time series prediction, in: Proceedings of the 25th ACM International Conference on Knowledge Discovery & Data Mining, ACM, 2019, pp. 1114-1122.

[25] Z. Liu, Y. Yang, Z. Huang, F. Shen, D. Zhang, H.T. Shen, Event early embedding: predicting event volume dynamics at early stage,in:Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2017, pp. 997-1000.

[26] X. Ma, X. Gao, G. Chen, Beep: a bayesian perspective early stage event prediction model for online social networks,in:Proceedings of the 2017 IEEE International Conference on Data Mining, IEEE, 2017, pp. 973-978.

[27] K. Subbian, B.A. Prakash, L. Adamic, Detecting large reshare cascades in social networks, in: Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, 2017, pp. 597-605.

[28] M. Cheung, J. She, A. Junus, L. Cao, Prediction of virality timing using cascades in social media, ACM Trans. Multimed Comput. Commun. Appl 13 (1) (2016) 1-23.

[29] J. Huang, Y. Tang, Y. Hu, J. Li, C. Hu, Predicting the active period of popularity evolution: a case study on twitter hashtags, Inf. Sci. 512 (2020) 315-326.

[30] Y. Li, J. Wang, J. Ye, C.K. Reddy, A multi-task learning formulation for survival analysis, in: Proceedings of the 22nd ACM International Conference on Knowledge Discovery and Data Mining, ACM, 2016, pp. 1715-1724.

[31] G. Yang, Y. Cai, C.K. Reddy, Spatio-temporal check-in time prediction with recurrent neural network based survival analysis, in: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, ACM, 2018, pp. 2976-2983.

[32] P. Zheng, S. Yuan, X. Wu, Safe: a neural survival analysis model for fraud early detection,in: Proceedings of the 2019 AAAI Conference on Artificial Intelligence 33, AAAI Press, 2019, pp. 1278-1285.

[33] P.K. Andersen, O. Borgan, R.D. Gill, N. Keiding, Statistical Models Based on Counting Processes, Springer Science & Business Media, 2012.

[34] R. Tibshirani, The lasso method for variable selection in the cox model, Stat. Med. 16 (4) (1997) 385-395.

[35] E.T. Lee, J. Wang, Statistical Methods for Survival Data Analysis, 476, John Wiley & Sons, 2003.

[36] R. Ranganath, A. Perotte, N. Elhadad, D. Blei, Deep survival analysis, in: Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR, 2016, pp. 101-114.

[37] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 (8)(1997) 1735-1780.

[38] J. Zhang, B. Liu, J. Tang, T. Chen, J. Li, Social influence locality for modeling retweeting behaviors, in: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, AAAI Press, 2013, pp. 2761-2767.