社交媒体数据流中的事件跟踪调查

IF 7.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zixuan Han;Leilei Shi;Lu Liu;Liang Jiang;Jiawei Fang;Fanyuan Lin;Jinjuan Zhang;John Panneerselvam;Nick Antonopoulos
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引用次数: 0

摘要

社交网络是我们日常生活中不可避免的一部分,在社交网络中产生了前所未有的大量复杂数据,这些数据与各种不同的应用相对应。因此,必须从传统社会学的角度对社交事件和模式进行研究,以优化源自社交网络的服务。社交网络中的事件追踪有多种应用,如网络安全和社会治理,其中涉及对社交网络上用户群体产生的数据进行实时分析。此外,随着深度学习技术的不断进步和在各个领域的重要突破,研究人员正在利用这一技术逐步优化事件检测(ED)和跟踪算法的有效性。为此,本文对社交网络中事件检测和跟踪的概念和方法进行了深入全面的综述。我们介绍了主流的事件跟踪方法,其中涉及三个主要技术步骤:ED、事件传播和事件演化。最后,我们介绍了 ED 和跟踪的基准数据集和评估指标,以便对主流方法的性能进行比较分析。最后,我们全面分析了该领域的主要研究成果和现有局限性,以及未来的研究前景和挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on Event Tracking in Social Media Data Streams
Social networks are inevitable parts of our daily life, where an unprecedented amount of complex data corresponding to a diverse range of applications are generated. As such, it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social networks. Event tracking in social networks finds various applications, such as network security and societal governance, which involves analyzing data generated by user groups on social networks in real time. Moreover, as deep learning techniques continue to advance and make important breakthroughs in various fields, researchers are using this technology to progressively optimize the effectiveness of Event Detection (ED) and tracking algorithms. In this regard, this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social networks. We introduce mainstream event tracking methods, which involve three primary technical steps: ED, event propagation, and event evolution. Finally, we introduce benchmark datasets and evaluation metrics for ED and tracking, which allow comparative analysis on the performance of mainstream methods. Finally, we present a comprehensive analysis of the main research findings and existing limitations in this field, as well as future research prospects and challenges.
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来源期刊
Big Data Mining and Analytics
Big Data Mining and Analytics Computer Science-Computer Science Applications
CiteScore
20.90
自引率
2.20%
发文量
84
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.
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