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Early detection method for emerging topics based on dynamic bayesian networks in micro-blogging networks

机译：微博网络中基于动态贝叶斯网络的新兴话题早期发现方法

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摘要

Micro-blogging networks have become the most influential online social networks in recent years, more and more people are used to obtain and diffuse information in them. Detecting topics from a great number of tweets in micro-blogging is important for information propagation and business marketing, especially detecting emerging topics in the early period could strongly support these real-time intelligent systems, such as real-time recommendation, ad-targeting, marketing strategy. However, most of previous researches are useful to detect emerging topic on a large scale, but they are not so effective for the early detection due to less informative properties in a relatively small size. To solve this problem, we propose a new early detection method for emerging topics based on Dynamic Bayesian Networks in micro-blogging networks. We first analyze the topic diffusion process and find two main characteristics of emerging topic which are attractiveness and key-node. Then based on this finding, we select features from the topology properties of topic diffusion, and build a DBN-based model by the conditional dependencies between features to identify the emerging keywords. An emerging keyword not only occurs in a given time period with frequency properties, but also diffuses with specific topology properties. Finally, we cluster the emerging keywords into emerging topics by the co-occurrence relations between keywords. Based on the real data of Sina micro-blogging, the experimental results demonstrate that our method is effective and capable of detecting the emerging topics one to two hours earlier than the other methods. (C) 2016 Elsevier Ltd. All rights reserved.
机译：近年来，微博客网络已成为最具影响力的在线社交网络，越来越多的人被用来获取和传播其中的信息。从微博中的大量推文中检测主题对于信息传播和业务营销非常重要，尤其是在早期发现新兴主题可以大力支持这些实时智能系统，例如实时推荐，广告定位，市场策略。然而，大多数先前的研究对于大规模检测新兴话题很有用，但由于信息量较小且相对较小，因此对早期检测效果不佳。为解决这一问题，我们提出了一种基于动态贝叶斯网络的微博网络中新兴话题的早期发现新方法。我们首先分析主题传播过程，发现新兴主题的两个主要特征，即吸引力和关键节点。然后，基于此发现，我们从主题扩散的拓扑属性中选择特征，并通过特征之间的条件依赖性来建立基于DBN的模型，以识别新兴的关键字。新兴的关键字不仅在给定的时间段内具有频率属性，而且会随着特定的拓扑属性扩散。最后，我们通过关键词之间的共现关系将新兴关键词聚集成新兴主题。根据新浪微博的真实数据，实验结果表明，该方法是有效的，能够比其他方法提前一到两个小时检测到新兴话题。 （C）2016 Elsevier Ltd.保留所有权利。

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