“物以类聚”：用户同质性是否影响社交媒体中的信息扩散？
"Birds of a Feather": Does User Homophily Impact Information Diffusion in Social Media?
摘要：
本文研究了用户同质性对在线社交媒体中信息扩散的社会过程的影响。几十年来，社会科学家一直对相似性产生联系的想法感兴趣：准确地说是“同质性”。同质性在社会科学中得到了广泛的研究，它指的是社会系统中的用户倾向于与相似的人建立更多的联系，而不是与不同的人。关键的观察是，同质性结构的自我网络的个人和影响他们的沟通行为。因此，这很可能影响信息在它们之间传播的机制。为此，我们研究了不同用户属性的同质性与社交媒体上的信息扩散过程之间的相互作用。在我们的方法中，我们首先提取扩散特征-对应于基线社交图以及根据不同用户属性（例如位置，活动）过滤的图。其次，我们提出了一个基于动态贝叶斯网络的框架来预测未来的扩散特性。第三，属性同质性的影响是量化的预测特征在解释实际扩散的能力，和外部变量，包括搜索和新闻的趋势。在一个大型Twitter数据集上的实验结果表明，给定一个特定的度量和一个主题，homophilous属性的选择可以影响信息扩散的预测。在大多数情况下，属性同质性能够解释实际的扩散和外部趋势约15-25%的情况下，同质性不被考虑。
英文摘要：
This article investigates the impact of user homophily on the social process of information diffusion in online social media. Over several decades, social scientists have been interested in the idea that similarity breeds connection: precisely known as "homophily". Homophily has been extensively studied in the social sciences and refers to the idea that users in a social system tend to bond more with ones who are similar to them than to ones who are dissimilar. The key observation is that homophily structures the ego-networks of individuals and impacts their communication behavior. It is therefore likely to effect the mechanisms in which information propagates among them. To this effect, we investigate the interplay between homophily along diverse user attributes and the information diffusion process on social media. In our approach, we first extract diffusion characteristics---corresponding to the baseline social graph as well as graphs filtered on different user attributes (e.g. location, activity). Second, we propose a Dynamic Bayesian Network based framework to predict diffusion characteristics at a future time. Third, the impact of attribute homophily is quantified by the ability of the predicted characteristics in explaining actual diffusion, and external variables, including trends in search and news. Experimental results on a large Twitter dataset demonstrate that choice of the homophilous attribute can impact the prediction of information diffusion, given a specific metric and a topic. In most cases, attribute homophily is able to explain the actual diffusion and external trends by ~15-25% over cases when homophily is not considered.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
