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A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

机译:一种衡量专业知识与社交媒体对名人在微博上的影响之间的相关性的计算方法

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Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users' influence scores. They rarely consider a person's expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally “Sina microblogging”). We found that there is a strong correlation between expertise levels and social media influence scores. In addition, different expertise levels showed influence variation patterns: high-expertise celebrities have stronger influence on the “audience” in their expertise domains.
机译:现有的社会影响力分析方法通常侧重于如何开发有效的算法来量化用户的影响力分数。他们很少考虑一个人的专业知识水平,这对影响衡量指标可能是重要的。在本文中,我们提出了一种计算方法来衡量专业知识与社交媒体影响力之间的相关性,并且我们通过将专业知识纳入影响力分析以新的视角来理解社交媒体影响力。我们精心构建了来自新浪微博的13684名中国名人的大型数据集(字面上称为“新浪微博”)。我们发现专业知识水平与社交媒体影响力得分之间存在很强的相关性。此外,不同的专业知识水平也显示出影响变化的模式:高级专业人士对他们的专业知识领域中的“受众群体”具有更强的影响力。

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