Universidad Carlos III de Madrid, Leganés, Madrid, Spain.
IMDEA Networks Institute, Leganés, Madrid, Spain.
Sci Rep. 2018 May 3;8(1):6955. doi: 10.1038/s41598-018-24874-2.
Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity.
数以十亿计的用户每天都会通过 Facebook、Twitter 或 Google+ 等在线社交网络（OSN）进行密集互动。这使得 OSN 成为广告、营销或政治等领域有价值的信息来源和驱动渠道。为了充分利用 OSN，分析师需要识别出有影响力的用户，以便利用他们来推广产品、传播信息或改善公司形象。在本报告中，我们提出了一种新的无监督方法——大规模无监督异常检测（MUOD），它基于异常值检测，为识别有影响力的用户提供支持。MUOD 具有可扩展性，因此可以用于大型 OSN。此外，它根据特征将异常值标记为形状、大小或幅度。这允许将异常用户分类到多个不同的类别中，这些类别可能包括不同类型的有影响力的用户。将 MUOD 应用于大约 4 亿谷歌+用户的一个子集，它已经能够自动识别和区分异常用户组，这些用户组具有与有影响力的用户的不同定义相关的特征，例如吸引参与的能力、吸引大量关注者的能力或高感染能力。
