解锁社交媒体和用户生成内容作为知识管理的数据源

James Meneghello, Nik Thompson, Kevin Lee, Kok Wai Wong, Bilal Abu-Salih
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引用次数: 33

摘要

社交媒体和用户生成内容的普及引发了全球数据量的指数级增长。然而,由于收集和提取方面的挑战,许多提要中的数据、嵌入的评论、评论和推荐都无法作为通用数据源访问。本文将知识管理框架作为知识管理和数据价值提取的范例。这个框架包含了解决方案,可以释放UGC作为分析应用程序的丰富实时数据源的潜力。本文所描述的贡献有三个方面。首先,提出了一种自动导航分页系统的方法来公开供收集的UGC。这是使用集成了动态数据收集的浏览器仿真来评估的。其次,介绍了一种无需先验知识即可收集社交数据的新方法。最后,开发了一个新的测试平台,以反映互联网站点的现状,并公开共享以鼓励未来的研究。讨论将新算法与现有数据提取技术进行比较,并提供了新算法可访问的UGC数据数量增加的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unlocking Social Media and User Generated Content as a Data Source for Knowledge Management
The pervasiveness of Social Media and user-generated content has triggered an exponential increase in global data volumes. However, due to collection and extraction challenges, data in many feeds, embedded comments, reviews and testimonials are inaccessible as a generic data source. This paper incorporates Knowledge Management framework as a paradigm for knowledge management and data value extraction. This framework embodies solutions to unlock the potential of UGC as a rich, real-time data source for analytical applications. The contributions described in this paper are threefold. Firstly, a method for automatically navigating pagination systems to expose UGC for collection is presented. This is evaluated using browser emulation integrated with dynamic data collection. Secondly, a new method for collecting social data without any a priori knowledge of the sites is introduced. Finally, a new testbed is developed to reflect the current state of internet sites and shared publicly to encourage future research. The discussion benchmarks the new algorithm alongside existing data extraction techniques and provides evidence of the increased amount of UGC data made accessible by the new algorithm.
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