关系事件模型能揭示什么?对托马斯-格伦德《谴责的动力》的评论:丑闻的局限性

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS EPJ Data Science Pub Date : 2024-01-11 DOI:10.1140/epjds/s13688-023-00432-3
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引用次数: 0

摘要

摘要 本文对托马斯-格伦德(Thomas Grund)在 2021 年计算社会科学国际会议上发表的主题演讲 "谴责的动态:丑闻的局限性 "的主题演讲。该主题演讲介绍了对加拿大政治丑闻证词中的告发所依据的关系动态的研究成果。格伦德使用关系事件模型来检验有关驱动告发的社会机制的假设。虽然告发应该只取决于谁有罪,而不是取决于谁在告发前说了什么,但格伦德的研究发现了一些证据,支持影响告发过程的关系机制。格伦德认为,过去的告发对证词的明显影响揭示了调查过程本身的局限性及其对丑闻的揭示。本文回顾了格伦德的演讲,并将这项工作置于一个更广阔的背景下,即使用植根于事件历史建模和社会网络理论的方法来阐明界定社会互动数据的过程。文章强调了该主题演讲可为分析此类数据的计算社会科学方法的发展提供信息的方式,并认为此类分析的价值对社会科学以外的学术研究也有影响。
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What relational event models can reveal: Commentary on Thomas Grund’s “Dynamics of Denunciation: The Limits of a Scandal”

Abstract

This article provides a commentary on Thomas Grund’s International Conference on Computational Social Science 2021 keynote “Dynamics of Denunciation: The Limits of a Scandal”. The keynote presents results from research investigating the relational dynamics underpinning the denunciations provided in testimonies relating to a Canadian political scandal. Grund uses relational event models to test hypotheses about the social mechanisms driving the denunciations. Although denunciation should depend only on who is guilty and not on who has said what up to that point, Grund’s study finds evidence in support of a number of relational mechanisms influencing the denunciation process. Grund argues that the apparent influence of past denunciations on testimonies reveals the limits of the inquiry process itself and what it can reveal about a scandal. This article reviews Grund’s talk and puts the work in a broader context of using approaches rooted in event history modelling and social network theory to illuminate the processes defining social interaction data. It highlights ways in which the keynote can inform the development of computational social science approaches to analysing such data, and argues that the value of such an analysis has implications for scholarship beyond the social sciences.

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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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