在标签中发现知识

Rizwan Mehmood, H. Maurer, Muhammad Tanveer Afzal
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引用次数: 4

摘要

Twitter是社交网络的一种,在当今世界的交流中扮演着活跃的角色。本文试图将知识发现过程应用于包含标签的Twitter数据集以及视觉分析技术,其目的是向人们提供信息,以便他们毫不费力地理解数据中隐藏的知识。我们进一步分析与每条推文相关的推文文本和元数据,以识别有用的模式,如“谁与谁交谈”和“多少”。我们的研究揭示了可视化和分层聚类技术在分析相似用户组方面的影响。我们进一步研究了不同的社交网络指标,揭示了用户在特定标签中的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge discovery in hashtags#
Twitter is a breed of social networks that are playing a buoyant role in today's world communication. This paper is an attempt to apply knowledge discovery process on Twitter dataset comprising hashtags along with the visual analytic techniques whose purpose is to provide information to the people in such a way so that they understand concealed knowledge in the data effortlessly and meritoriously. We further analyze tweet text and metadata associated with each tweet for identification of useful patterns like "who talks to whom" and "how much". Our research reveals the impact of visualization and hierarchical clustering technique in analyzing similar groups of users. Further we investigate different social network measures that unveil the influence of users in the particular hashtags.
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