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Who is more positive in private? Analyzing sentiment differences across privacy levels and demographic factors in Facebook chats and posts

机译：谁在私下方面更积极？分析Facebook聊天和帖子中隐私级别和人口统计学因素之间的情感差异

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Understanding users' sentiments in social media is important in many domains, such as marketing and online applications. Is one demographic group inherently different from another? Does a group express the same sentiment both in private and public? How can we compare the sentiments of different groups composed of multiple attributes? In this paper, we take an interdisciplinary approach towards mining the patterns of textual sentiments and metadata. First, we look into several existing hypotheses in social science on the interplay between user characteristics and sentiments, as well as the related evidence in the field of social network data analysis. Second, we present a dataset with unique features (Facebook users' chats and posts in multiple languages) and a procedure to process the data. Third, we test our hypotheses on this dataset and interpret the results. Fourth, under the subgroup-discovery paradigm, we present an approach with two algorithms that generalizes single-attribute testing. This approach provides more detailed insight into the relationships among attributes, and reveals interesting attribute-value combinations with distinct sentiments. Furthermore, it offers novel hypotheses for examination in future studies.
机译：在社交媒体中，了解用户的情绪在许多领域都很重要，例如营销和在线应用程序。一个人口群体在本质上与另一个人口群体不同吗？团体是否在私人和公共场合表达相同的情绪？我们如何比较由多个属性组成的不同群体的情感？在本文中，我们采用跨学科方法来挖掘文本情感和元数据的模式。首先，我们研究了社会科学中有关用户特征和情感之间相互作用的几种现有假设，以及社会网络数据分析领域的相关证据。其次，我们提供具有独特功能的数据集（Facebook用户的多种语言的聊天和帖子）以及处理数据的过程。第三，我们在该数据集上检验假设并解释结果。第四，在子组发现范式下，我们提出了一种使用两种算法概括单一属性测试的方法。这种方法可以更详细地了解属性之间的关系，并揭示具有不同情感的有趣的属性-值组合。此外，它为将来的研究提供了新颖的假设。

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