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.

中文翻译：

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基于计算社会背景的网络事件演化趋势预测

预测Web事件的未来趋势可以帮助显着提高Web服务的质量，例如，提高新闻网站的用户满意度。在这方面，现有的方法主要基于时间模式，并假设手头有足够的时间数据。但是，大多数Web事件的生命周期并不长，而是具有burst属性，这会大大降低时间模式挖掘的性能。此外，这些方法忽略了围绕Web事件的社交环境的影响。在本文中，我们提出了一种基于事件的社交环境而不是时间模式来预测Web事件未来趋势的新颖方法。更具体地说，在建议的方法中，首先将社交环境的计算模型构建为两层的关联链接网络，考虑其属性，例如关联网络属性和小世界属性。然后，基于锚定理论，模拟了Web事件与社交环境之间的交互。最终，定义并评估了一种外部力量，以量化社交环境对Web事件演变的影响，该作用用于预测Web事件的未来趋势。实验表明，该方法的性能优于传统的基于时间序列的方法。定义并评估了外部力量，以量化社交环境对Web事件演变的影响，该作用用于预测Web事件的未来趋势。实验表明，该方法的性能优于传统的基于时间序列的方法。定义并评估了外部力量，以量化社交环境对Web事件演变的影响，该作用用于预测Web事件的未来趋势。实验表明，该方法的性能优于传统的基于时间序列的方法。