Social networks like Twitter, Facebook have recently become the most widely used communication platforms for people to propagate information rapidly. Fast diffusion of information creates accuracy and scalability issues towards topic detection. Most of the existing approaches can detect the most popular topics on a large scale. However, these approaches are not effective for faster detection. This article proposes a novel topic detection approach – Node Significance based Label Propagation Community Detection (NSLPCD) algorithm, which detects the topic faster without compromising accuracy. The proposed algorithm analyzes the frequency distribution of keywords in the collection of tweets and finds two types of keywords: topic-identifying and topic-describing keywords, which play an important role in topic detection. Based on these defined keywords, the keyword co-occurrence graph is built, and subsequently, the NSLPCD algorithm is applied to get topic clusters in the form of communities. The experimental results using the real data of Twitter, show that the proposed method is effective in quality as well as run-time performance as compared to other existing methods.

中文翻译：

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NSLPCD：使用基于节点重要性的标签传播社区检测算法的基于主题的推文聚类

Twitter、Facebook 等社交网络最近已成为人们快速传播信息的最广泛使用的交流平台。信息的快速传播给主题检测带来了准确性和可扩展性问题。大多数现有方法可以大规模检测最流行的主题。然而，这些方法对于更快的检测无效。本文提出了一种新颖的主题检测方法——基于节点重要性的标签传播社区检测（NSLPCD）算法，该算法在不影响准确性的情况下更快地检测主题。该算法分析了推文集合中关键词的频率分布，发现了两类关键词：主题识别关键词和主题描述关键词，它们在主题检测中发挥着重要作用。基于这些定义的关键字，构建关键词共现图，然后应用NSLPCD算法得到社区形式的主题簇。使用 Twitter 真实数据的实验结果表明，与现有的其他方法相比，所提出的方法在质量和运行时性能上都是有效的。