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Actions Speak as Loud as Words: Predicting Relationships from Social Behavior Data

机译：言行一致：根据社会行为数据预测关系

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摘要

In recent years, new studies concentrating on analyzing user personality and finding credible content in social media have become quite popular. Most such work augments features from textual content with features representing the user's social ties and the tie strength. Social ties are crucial in understanding the network the people are a part of. However, textual content is extremely useful in understanding topics discussed and the personality of the individual. We bring a new dimension to this type of analysis with methods to compute the type of ties individuals have and the strength of the ties in each dimension. We present a new genre of behavioral features that are able to capture, the "function" of a specific relationship without the help of textual features. Our novel features are based on the statistical properties of communication patterns between individuals such as reciprocity, assortativity. attention and latency. We introduce a new methodology for determining how such features can be compared to textual features, and show, using Twitter data, that our features can be used to capture contextual information present in textual features very accurately. Conversely, we also demonstrate how textual features can be used to determine social attributes related to an individual.
机译：近年来，专注于分析用户个性并在社交媒体中找到可信内容的新研究已变得非常流行。大多数此类工作使用代表用户的社交纽带和领带强度的特征来增强文字内容的特征。社会纽带对于理解人们参与的网络至关重要。但是，文本内容对于理解讨论的主题和个人个性非常有用。我们为这种类型的分析带来了新的维度，提供了计算个人拥有的联系类型以及每个维度中联系强度的方法。我们提出了一种新的行为特征类型，无需文本特征就可以捕获特定关系的“功能”。我们的新颖功能是基于个人之间的沟通模式的统计特性，例如互惠，分类。注意和潜伏期。我们引入了一种新的方法来确定如何将这些功能与文本功能进行比较，并使用Twitter数据显示我们的功能可用于非常准确地捕获文本功能中存在的上下文信息。相反，我们也演示了如何使用文本功能来确定与个人相关的社会属性。

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