Studying social networks in the age of computational social science

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS EPJ Data Science Pub Date : 2023-12-19 DOI:10.1140/epjds/s13688-023-00436-z
Xinwei Xu
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Abstract

Social and behavioral sciences now stand at a critical juncture. The emergence of Computational Social Science has significantly changed how social networks are studied. In his keynote at IC2S2 2021, Lehmann presented a series of research based on the Copenhagen Network Study and pointed out an important insight that has mostly gone unnoticed for many network science practitioners: the data generation process — in particular, how data is aggregated over time and the medium through which social interactions occur — could shape the structure of networks that researchers observe. Situating the keynote in the broader field of CSS, this commentary expands on its relevance for the shared challenges and ongoing development of CSS.

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在计算社会科学时代研究社交网络
社会科学和行为科学正处于一个关键时刻。计算社会科学的出现极大地改变了社会网络的研究方式。莱曼在 2021 年国际计算社会科学会议(IC2S2)上发表主题演讲,介绍了基于哥本哈根网络研究的一系列研究成果,并指出了许多网络科学从业人员大多没有注意到的一个重要见解:数据生成过程,特别是数据如何随时间推移而聚合,以及社会互动发生的媒介,可能会影响研究人员观察到的网络结构。本评论将主题演讲置于更广阔的 CSS 领域中,阐述其与 CSS 的共同挑战和持续发展的相关性。
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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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