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Tweetonomies智慧:从社会意识流获取潜在概念结构

The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams
课程网址: http://videolectures.net/www2010_wagner_twt/  
主讲教师: Claudia Wagner
开课单位: 莱布尼兹社会科学研究所
开课时间: 2010-05-17
课程语种: 英语
中文简介:
尽管有人可能会说,140个字符或更少的信息中几乎没有什么智慧可以传达,但本文试图探索社交意识流(如Twitter)中的信息聚合是否传递了关于某个特定领域的有意义的信息。作为一个研究群体,我们对这些流的结构和语义特性以及它们如何被分析、特征化和使用知之甚少。本文介绍了一种社会意识流的网络理论模型,即所谓的“tweetonomy”,以及一组基于流的度量,使研究人员能够系统地定义和比较不同的流聚合。我们将模型和度量应用于从Twitter获取的数据集,以研究所选流中出现的语义。本文介绍的网络理论模型及相应的措施,对社会意识流中的信息检索和本体学习有一定的参考价值。我们的实证结果表明,不同的社会意识流聚合呈现出有趣的差异,使它们适合不同的应用。
课程简介: Although one might argue that little wisdom can be conveyed in messages of 140 characters or less, this paper sets out to explore whether the aggregation of messages in social awareness streams, such as Twitter, conveys meaningful information about a given domain. As a research community, we know little about the structural and semantic properties of such streams, and how they can be analyzed, characterized and used. This paper introduces a network-theoretic model of social awareness stream, a so-called "tweetonomy", together with a set of stream-based measures that allow researchers to systematically defi ne and compare diff erent stream aggregations. We apply the model and measures to a dataset acquired from Twitter to study emerging semantics in selected streams. The network-theoretic model and the corresponding measures introduced in this paper are relevant for researchers interested in information retrieval and ontology learning from social awareness streams. Our empirical fi ndings demonstrate that di fferent social awareness stream aggregations exhibit interesting differences, making them amenable for different applications.
关 键 词: 计算机科学; 网络分析; 社交网络
课程来源: 视频讲座网
最后编审: 2020-12-19:yumf
阅读次数: 36