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User behavior mining on social media: a systematic literature review

机译：社交媒体上的用户行为挖掘：系统的文献综述

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User behavior mining on Social Media (UBMSM) is the process of representing, analyzing, and extracting operational and behavioral patterns from user behavioral data in social media. It discusses theories and methodologies from different disciplines such as combining theorems and techniques from computer science, data mining, machine learning, social network analysis, and other related disciplines. User behavior mining provides a deep understanding of user behavioral data such that we observe not only individual behavioral patterns, but also interaction and communication among users by considering collective behavior of users. The aim of this study is to provide a systematic literature review on the significant aspects and approaches in addressing user behavior mining on social media. A systematic literature review was performed to find the related literature, and 174 articles were selected as primary studies. We classified the surveyed studies into four categories based on their focused area: users, user-generated content, the structure of network that content spreads on it and information diffusion. The majority of the primary articles focus on user aspect (66%); 6% of them focus on content aspect; 6% of them focus on network structure aspect, 22% of them focus on information diffusion aspect.
机译：社交媒体上的用户行为挖掘（UBMSM）是从社交媒体中的用户行为数据表示，分析和提取操作和行为模式的过程。它讨论了来自不同学科的理论和方法，例如将来自计算机科学，数据挖掘，机器学习，社交网络分析和其他相关学科的理论和技术相结合。用户行为挖掘提供了对用户行为数据的深入了解，因此我们不仅通过考虑用户的集体行为来观察个体行为模式，而且可以观察用户之间的交互和交流。这项研究的目的是就处理社交媒体上用户行为挖掘的重要方面和方法提供系统的文献综述。进行系统的文献综述以查找相关文献，并选择174篇文章作为基础研究。我们根据研究重点将调查的研究分为四类：用户，用户生成的内容，内容在其上传播的网络结构和信息传播。大多数主要文章集中在用户方面（66％）；其中6％专注于内容方面；其中6％专注于网络结构方面，其中22％专注于信息传播方面。

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