Abstract
Understanding popularity evolution patterns of hot topics is important for online recommendation systems and marketing services. Previous research has analyzed popularity evolution patterns based on the time series which only captures the feature of peaks. However, hot topics experience more complex popularity evolution patterns, not only peaks but also other time series features: level, trend and seasonality. In this paper, we present a method to model and understand popularity evolution patterns based on the three time series features for two types of hot topics: burst and non-burst. Our experimental results demonstrate that the seasonality of the time series is multiplicative, which means the size of the fluctuations in popularity evolution pattern of a hot topic varies with the change of trend. The level and trend of the time series are relatively unstable for burst topics compared with non-burst topics.
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References
Domingos, P., Richardson, M.: Mining the Network Value of Customers. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM Press, New York (2001)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the Spread of Influence through a Social Network. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM Press, New York (2003)
Wu, F., Huberman, B.A., Adamic, L.A., Tyler, J.R.: Information Flow in Social Groups. Physica A: Statistical Mechanics and its Applications 337(1), 327–335 (2004)
Wu, F., Huberman, B.A.: Novelty and Collective Attention. Proceedings of the National Academy of Sciences 104(45), 17599–17601 (2007)
Couronne, T., Stoica, A., Beuscart, J.S.: Online Social Network Popularity Evolution: an Additive Mixture Model. In: IEEE International Conference on Advances in Social Networks Analysis and Mining, pp. 346–350. IEEE Press, New York (2010)
Shmueli, G., Lee, H.: Time Series Forecasting. Tsing Hua University Publishing, Beijing (2012) (in Chinese)
Kalekar, P.S.: Time Series Forecasting Using Holt-Winters Exponential Smoothing. Kanwal Rekhi School of Information Technology 4329008, 1–13 (2004)
Crane, R., Sornette, D.: Viral, Quality, and Junk Videos on YouTube: Separating Content from Noise in an Information-Rich Environment. In: AAAI Spring Symposium: Social Information Processing, pp. 18–20. AAAI Press, Menlo Park (2008)
Crane, R., Sornette, D.: Robust Dynamic Classes Revealed by Measuring the Response Function of a Social System. Proceedings of the National Academy of Sciences 105(41), 15649–15653 (2008)
Figueiredo, F.: On the Prediction of Popularity of Trends and Hits for User Generated Videos. In: ACM International Conference on Web Search and Data Mining, pp. 741–746. ACM Press, New York (2013)
Ahmed, M., Spagna, S., Huici, F., Niccolini, S.: A Peek into the Future: Predicting the Evolution of Popularity in User Generated Content. In: ACM International Conference on Web Search and Data Mining, pp. 607–616. ACM Press, New York (2013)
Sina News, http://news.sina.com.cn/hotnews/
Tianya Search, http://search.tianya.cn/bbs/
Figueiredo, F., Benevenuto, F., Almeida, J.M.: The Tube over Time: Characterizing Popularity Growth of YouTube Videos. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 745–754. ACM Press, New York (2011)
Yang, J., Leskovec, J.: Patterns of Temporal Variation in Online Media. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 177–186. ACM Press, New York (2011)
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., Moon, S.: I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 1–14. ACM Press, New York (2007)
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., Moon, S.: Analyzing the Video Popularity Characteristics of Large-scale User Generated Content Systems. IEEE/ACM Transactions on Networking 17(5), 1357–1370 (2009)
Lin, C.X., Zhao, B., Mei, Q., Han, J.: PET: A Statistical Model for Popular Events Tracking in Social Communities. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 929–938. ACM Press, New York (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: Proceedings of the 20th International Conference on World Wide Web, pp. 695–704. ACM Press, New York (2011)
Lehmann, J., Gonçalves, B., Ramasco, J.J., Cattuto, C.: Dynamical Classes of Collective Attention in Twitter. In: Proceedings of the 21st International Conference on World Wide Web, pp. 251–260. ACM Press, New York (2012)
Ardon, S., Bagchi, A., Mahanti, A., et al.: Spatio-temporal and Events Based Analysis of Topic Popularity in Twitter. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 219–228. ACM Press, New York (2013)
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Hu, C., Hu, Y., Xu, W., Shi, P., Fu, S. (2014). Understanding Popularity Evolution Patterns of Hot Topics Based on Time Series Features. In: Han, W., Huang, Z., Hu, C., Zhang, H., Guo, L. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8710. Springer, Cham. https://doi.org/10.1007/978-3-319-11119-3_6
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DOI: https://doi.org/10.1007/978-3-319-11119-3_6
Publisher Name: Springer, Cham
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