Suppr超能文献

社交媒体数据分析与挖掘特刊。

Special issue on analysis and mining of social media data.

作者信息

Zubiaga Arkaitz, Rosso Paolo

机构信息

School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.

Technical University of Valencia, Valencia, Spain.

出版信息

PeerJ Comput Sci. 2024 Feb 29;10:e1909. doi: 10.7717/peerj-cs.1909. eCollection 2024.

Abstract

This Editorial introduces the PeerJ Computer Science Special Issue on Analysis and Mining of Social Media Data. The special issue called for submissions with a primary focus on the use of social media data, for a variety of fields including natural language processing, computational social science, data mining, information retrieval and recommender systems. Of the 48 abstract submissions that were deemed within the scope of the special issue and were invited to submit a full article, 17 were ultimately accepted. These included a diverse set of articles covering, , sentiment analysis, detection and mitigation of online harms, analytical studies focused on societal issues and analysis of images surrounding news. The articles primarily use Twitter, Facebook and Reddit as data sources; English, Arabic, Italian, Russian, Indonesian and Javanese as languages; and over a third of the articles revolve around COVID-19 as the main topic of study. This article discusses the motivation for launching such a special issue and provides an overview of the articles published in the issue.

摘要

本社论介绍了《PeerJ计算机科学》关于社交媒体数据分析与挖掘的特刊。该特刊呼吁提交的稿件主要聚焦于社交媒体数据在包括自然语言处理、计算社会科学、数据挖掘、信息检索和推荐系统等多个领域的应用。在48篇被认为符合特刊范围并受邀提交全文的摘要投稿中,最终有17篇被接受。这些文章涵盖了多种类型,包括情感分析、在线危害的检测与缓解、关注社会问题的分析研究以及围绕新闻的图像分析。文章主要使用推特、脸书和红迪网作为数据源;英语、阿拉伯语、意大利语、俄语、印尼语和爪哇语作为语言;超过三分之一的文章围绕新冠疫情作为主要研究主题展开。本文讨论了推出这样一个特刊的动机,并概述了该特刊上发表的文章。

相似文献

1
Special issue on analysis and mining of social media data.社交媒体数据分析与挖掘特刊。
PeerJ Comput Sci. 2024 Feb 29;10:e1909. doi: 10.7717/peerj-cs.1909. eCollection 2024.

本文引用的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验