Clustering-based burst-detection algorithm for web-image document stream on social media
Clustering-based burst-detection algorithm for web-image document stream on social media
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DOI：
10.1109/icsmc.2012.6377809
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Shingo Tamura;Keiichi Tamura;H. Kitakami;K. Hirahara
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文献类型：
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作者：
Shingo Tamura;Keiichi Tamura;H. Kitakami;K. Hirahara
随着人们对社交媒体的兴趣越来越大，大量的网络图像已经在互联网上创建。因此，从大规模的Web图像中提取有用的知识已经成为一种新的挑战。在本文中，我们专注于网络图像已发布到互联网上通过社交媒体网站。本研究的主要目的是提取事件和跟踪的文档流，其中包括Web图像的主题。为了解决这个问题，本文提出了一种新的方法，在Web图像文档流的突发检测。所提出的方法集成了聚类技术与克莱因伯格的突发检测。为了评估所提出的方法，我们使用了Twitter用户的实际推文。实验结果表明，该方法能够有效地提取社交媒体网站上发布的Web图像的事件并跟踪其主题。
With an increasing interest in social media, a large number of Web images have been created on the Internet. Therefore, extracting useful knowledge from a large-scale set of Web images has become a new type of challenge. In this paper, we focus on Web images that have been posted onto the Internet through social media sites. The main objective of this study is to extract the events and track the topics of a document stream that includes Web images. To address this challenge, this paper proposes a novel method for burst detection in a Web-image document stream. The proposed method integrates a clustering technique with Kleinberg's burst detection. To evaluate the proposed method, we used actual tweets from Twitter users. The experimental results show that the proposed method can extract the events and track the topics related to Web images posted on social media sites.