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首页> 外文期刊>ACM Transactions on Information Systems >Extraction, Characterization and Utility of Prototypical Communication Groups in the Blogosphere
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Extraction, Characterization and Utility of Prototypical Communication Groups in the Blogosphere

机译:Blogosphere中原型通信组的提取,表征和实用性

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This article analyzes communication within a set of individuals to extract the representative prototypical groups and provides a novel framework to establish the utility of such groups. Corporations may want to identify representative groups (which are indicative of the overall communication set) because it is easier to track the prototypical groups rather than the entire set. This can be useful for advertising, identifying "hot" spots of resource consumption as well as in mining representative moods or temperature of a community. Our framework has three parts: extraction, characterization, and utility of prototypical groups. First, we extract groups by developing features representing communication dynamics of the individuals. Second, to characterize the overall communication set, we identify a subset of groups within the community as the prototypical groups. Third, we justify the utility of these prototypical groups by using them as predictors of related external phenomena; specifically, stock market movement of technology companies and political polls of Presidential candidates in the 2008 U.S. elections. We have conducted extensive experiments on two popular blogs, Engadget and Huffington Post. We observe that the prototypical groups can predict stock market movement/political polls satisfactorily with mean error rate of 20.32%. Further, our method outperforms baseline methods based on alternative group extraction and prototypical group identification methods. We evaluate the quality of the extracted groups based on their conductance and coverage measures and develop metrics: predictivity and resilience to evaluate their ability to predict a related external time-series variable (stock market movement/political polls). This implies that communication dynamics of individuals are essential in extracting groups in a community, and the prototypical groups extracted by our method are meaningful in characterizing the overall communication sets.
机译:本文分析了一组个人之间的交流,以提取代表性的原型组,并提供了建立此类组实用性的新颖框架。公司可能希望识别代表组(代表整个通信集),因为跟踪原型组比整个集更容易。这对于广告,确定资源消耗的“热点”以及挖掘社区的代表性情绪或温度很有用。我们的框架包括三个部分:原型组的提取,表征和实用性。首先,我们通过开发代表个体交流动态的特征来提取群体。其次,为了表征整体交流,我们将社区内的群体子集确定为原型群体。第三,我们通过将它们用作相关外部现象的预测因子来证明这些原型组的实用性。特别是在2008年美国大选中,科技公司的股票市场动向和对总统候选人的政治调查。我们在两个热门博客Engadget和Huffington Post上进行了广泛的实验。我们观察到,原型组可以令人满意地预测股市走势/政治民意调查,平均错误率为20.32%。此外,我们的方法优于基于替代组提取和原型组识别方法的基线方法。我们基于其电导率和覆盖率度量来评估被抽取群体的质量,并开发度量标准:可预测性和弹性,以评估其预测相关外部时间序列变量(股市变动/政治民意测验)的能力。这意味着,个人的交流动态对于抽取社区中的群体至关重要,而通过我们的方法抽取的原型群体对于表征整体交流集具有重要意义。

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