基于性别的用户对 Facebook 帖子反应分析

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2023-12-25 DOI:10.26599/BDMA.2023.9020005

Yassine El Moudene;Jaafar Idrais;Rida El Abassi;Abderrahim Sabour

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引用次数: 0

摘要

在线社交网络（OSN）以共享不同类型的信息和各种互动（评论、反应和分享）为基础。其中一个重要的行为就是对内容的情绪反应。Facebook （即 FB）上反应类型的多样性使用户能够表达自己的情感，其可追溯性创造并丰富了用户在虚拟世界中的情感认同。本文基于对 119875012 次 FB 反应（赞、爱、哈哈、哇、悲伤、愤怒、感恩和自豪）的多层次（发表、评论和子评论）分析，对用户的情感行为进行研究和分类，可视化不同反应类型的分布，并分析性别对情感产生的影响。所有这些都可以通过解决这些研究问题来实现：谁的反应最多？哪种情绪表达最多？

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Gender-Based Analysis of User Reactions to Facebook Posts

Online Social Networks (OSNs) are based on the sharing of different types of information and on various interactions (comments, reactions, and sharing). One of these important actions is the emotional reaction to the content. The diversity of reaction types available on Facebook (namely FB) enables users to express their feelings, and its traceability creates and enriches the users' emotional identity in the virtual world. This paper is based on the analysis of 119875012 FB reactions (Like, Love, Haha, Wow, Sad, Angry, Thankful, and Pride) made at multiple levels (publications, comments, and sub-comments) to study and classify the users' emotional behavior, visualize the distribution of different types of reactions, and analyze the gender impact on emotion generation. All of these can be achieved by addressing these research questions: who reacts the most? Which emotion is the most expressed?

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.