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Web event evolution trend prediction based on its computational social context

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Abstract

Predicting future trends of Web events can help significantly improve the quality of Web services, e.g., improving the user satisfaction of news websites. Existing approaches in this regard are based mainly on temporal patterns mined with the assumption that enough temporal data is available on hand. However, most Web events do not have a long lifecycle, but a burst property, which drastically reduces the performance of temporal patterns mining. Furthermore, these approaches overlook the influence of the social context surrounding the Web events. In this paper, we propose a novel method to predict future trends of Web events, based on their social contexts rather than temporal patterns. More specially, in the proposed method, a computational model for the social context is first built as a two-layer Association Linked Network considering its properties, such as the associative network property and the small world property. Then, the interaction between a Web event and the social context is simulated, based on the anchoring theory. Finally, an external force is defined and evaluated to quantify the influence of the social context on the evolution of Web events, which is used to predict future trends of Web events. Experiments show that the performance of the proposed method is better than that of the traditional time series-based approaches.

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Notes

  1. The evolution here does not mean that one event changes/eolves to another one, but the content of webpages following this event change/evolve over time.

  2. The social context here mainly denotes the context on the Web, but we want to keep this name to highlight its social nature.

  3. http://news.baidu.com/

  4. Note that not all of the webpages can be downloaded because some of them may be videos and some of them cannot be linked and crawled.

  5. http://nlp.stanford.edu/software/corenlp.shtml

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under grant nos. 91746203, and by the Australian Research Council (ARC) under discovery grant DP190101733.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, China

    Junyu Xuan & Xiangfeng Luo

  2. The Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW, 2007, Sydney, Australia

    Junyu Xuan, Jie Lu & Guangquan Zhang

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  1. Junyu Xuan
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This article belongs to the Topical Collection: Computational Social Science as the Ultimate Web Intelligence

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Xuan, J., Luo, X., Lu, J. et al. Web event evolution trend prediction based on its computational social context. World Wide Web 23, 1861–1886 (2020). https://doi.org/10.1007/s11280-019-00753-2

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