基于共同身份和纽带理论的话题群体和社会群体的区分
Distinguishing Topical and Social Groups Based on Common Identity and Bond Theory
摘要：
社交团体在社交媒体平台中发挥着至关重要的作用，因为它们构成了用户参与和参与的基础。群体是由社区成员明确创建的，但也是在成员互动时有机形成的。由于其重要性，它们已被广泛研究（例如，社区发现、进化、活动等）。要理解这些群体如何演变，关键问题之一是是否存在不同类型的群体以及它们之间的差异。在社会学中，已经提出了一些理论来帮助解释这些群体是如何形成的。特别是，共同身份和共同纽带理论指出，人们根据身份加入群体（即，对所讨论的主题感兴趣）或键连接（即，社会关系）。该理论已被定性地应用于小团体，将其分为主题或社会。我们使用身份和键理论来定义一组特征，将群体分为这两个类别。使用Flickr的数据集，我们提取用户定义的组和自动检测的组，从社区发现算法获得。我们讨论的过程中，手动标记的群体到社会或专题和预测组标签的基础上定义的功能目前的结果。我们直接验证了理论的预测，表明该指标能够以高精度预测群体类型。此外，我们提出了一个比较申报和检测组沿话题性和社会性方面。
英文摘要：
Social groups play a crucial role in social media platforms because they form the basis for user participation and engagement. Groups are created explicitly by members of the community, but also form organically as members interact. Due to their importance, they have been studied widely (e.g., community detection, evolution, activity, etc.). One of the key questions for understanding how such groups evolve is whether there are different types of groups and how they differ. In Sociology, theories have been proposed to help explain how such groups form. In particular, the common identity and common bond theory states that people join groups based on identity (i.e., interest in the topics discussed) or bond attachment (i.e., social relationships). The theory has been applied qualitatively to small groups to classify them as either topical or social. We use the identity and bond theory to define a set of features to classify groups into those two categories. Using a dataset from Flickr, we extract user-defined groups and automatically-detected groups, obtained from a community detection algorithm. We discuss the process of manual labeling of groups into social or topical and present results of predicting the group label based on the defined features. We directly validate the predictions of the theory showing that the metrics are able to forecast the group type with high accuracy. In addition, we present a comparison between declared and detected groups along topicality and sociality dimensions.
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。
