非随机推特死亡率和数据访问限制:影响敏感推特研究的复制

IF 4.7 2区 社会学 Q1 POLITICAL SCIENCE
Andreas Küpfer
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引用次数: 0

摘要

十多年来,Twitter 一直是政治家、记者和公民用来调查仇恨言论、两极分化或恐怖主义等政治现象的最重要的社交媒体平台。由于推特相关的复制数据不完整,且无法完全重新抓取数据集,很大一部分有关情绪化或争议性内容的推特研究限制了其复制研究结果的能力。本文表明,这些 Twitter 研究及其结果受到非随机推文死亡率和平台数据访问限制的严重影响。敏感数据集的删除率明显高于非敏感数据集,而试图复制 Kim(2023,《政治学研究与方法》,11,673-695)关于暴力推文内容的有影响力的研究的关键发现,结果却大相径庭。研究结果强调,鉴于社交媒体研究条件的动态变化,获取完整的复制数据尤为重要。因此,本研究提出了非随机推文死亡率对 Twitter 和类似平台上未来社交媒体研究的广泛影响的担忧和潜在解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NonRandom Tweet Mortality and Data Access Restrictions: Compromising the Replication of Sensitive Twitter Studies
Used by politicians, journalists, and citizens, Twitter has been the most important social media platform to investigate political phenomena such as hate speech, polarization, or terrorism for over a decade. A high proportion of Twitter studies of emotionally charged or controversial content limit their ability to replicate findings due to incomplete Twitter-related replication data and the inability to recrawl their datasets entirely. This paper shows that these Twitter studies and their findings are considerably affected by nonrandom tweet mortality and data access restrictions imposed by the platform. While sensitive datasets suffer a notably higher removal rate than nonsensitive datasets, attempting to replicate key findings of Kim’s (2023, Political Science Research and Methods 11, 673–695) influential study on the content of violent tweets leads to significantly different results. The results highlight that access to complete replication data is particularly important in light of dynamically changing social media research conditions. Thus, the study raises concerns and potential solutions about the broader implications of nonrandom tweet mortality for future social media research on Twitter and similar platforms.
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来源期刊
Political Analysis
Political Analysis POLITICAL SCIENCE-
CiteScore
8.80
自引率
3.70%
发文量
30
期刊介绍: Political Analysis chronicles these exciting developments by publishing the most sophisticated scholarship in the field. It is the place to learn new methods, to find some of the best empirical scholarship, and to publish your best research.
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