Abstract
Predicting the scale and quantity of reposting in micro-blog network have significances to the future network marketing, hot topic detection and public opinion monitor. This study proposed a novel two-stage method to predict the popularity of a micro-blog prior to its release. By focusing on the text content of the specific micro-blog as well as its source of publication (user’s attributes), a special classification method—Labeled Latent Dirichlet allocation (LLDA) was trained to predict the volume range of future reposts for a new message. To the authors’ knowledge, this paper is the first research to utilize this multi-label text classifier to investigate the influence of one micro-blog’s topic on its reposting scale. The experiment was conducted on a large scale dataset, and the results show that it’s possible to estimate ranges of popularity with an overall accuracy of 72.56%.
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References
Bandari, R., Asur, S., Huberman, B.A.: The pulse of news in social media: forecasting popularity. In: 6th International AAAI Conference on Weblogs and Social Media, pp. 26–33. AAAI Press, Dublin (2012)
Yang, Z., Guo, J., Cai, K., Tang, J., Li, J., Zhang, L., Zhong, S.: Understanding retweeting behaviors in social networks. In: 19th ACM International Conference on Information and Knowledge Management, pp. 1633–1636. ACM Press, Toronto (2010)
Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In: 20th International Conference on World Wide Web, pp. 695–704. ACM Press, Hyderabad, India (2011)
Xiong, X., Zhou, G., Huang, Y., Ma, J.: Predicting popularity of tweets on Sina Weibo. J. Inf. Eng. Univ. 13(4), 496–502 (2012)
Li, Y., Yu, H., Liu, L.: Predict algorithm of micro-blog retweet scale based on SVM. Appl. Res. Comput. 30(9), 2594–2597 (2013)
Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in Twitter. In: 20th International Conference on World Wide Web, pp. 57–58. ACM Press, Hyderabad, India (2011)
Li, Q., Jiang, J., Li, Y., Liu, Y.: The retweeting scale classification prediction of government micro-blogs in China. J. Intell. 37(1), 95–99 (2018)
Tan, C.: Short text classification based on LDA and SVM. Int. J. Appl. Math. Stat. 51(22), 205–214 (2013)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(2003), 993–1022 (2003)
Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: The 2009 Conference on Empirical Methods in Natural Language Processing, pp. 248–256. ACL Press, Singapore (2009)
Sina Science and Technology Homepage. http://tech.sina.com.cn/i/2018–02-13/doc-ifyrmfmc2280063.shtml. Accessed 2018/4/6
Zhou, Z., Bandari, R., Kong, J., Qian, H., Roychowdhury, V.: Information resonance on Twitter: watching Iran. In: 1st Workshop on Social Media Analytics, pp. 123–131. ACM Press, Washington (2010)
Acknowledgements
This work is supported in part by National Natural Science Foundation of China (Project No. 71701134), The Humanity and Social Science Youth Foundation of Ministry of Education of China (Project No. 16YJC630153), National Taipei University of Technology- Shenzhen University Joint Research Program (Project No. 2018003), and Natural Science Foundation of Guangdong Province of China (Project No. 2017A030310427).
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Liu, L., Yang, C., Liu, T., Chen, X., Weng, SS. (2018). A Classification Method for Micro-Blog Popularity Prediction: Considering the Semantic Information. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_33
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DOI: https://doi.org/10.1007/978-3-319-93818-9_33
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