你的 PIE 里有什么?利用 PIEGraph 了解个性化信息环境的内容

IF 2.8 2区 管理学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Deen Freelon, Meredith L. Pruden, Daniel Malmer, Qunfang Wu, Yiping Xia, Daniel Johnson, Emily Chen, Andrew Crist
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引用次数: 0

摘要

长期以来,人们一直从以平台为中心的角度对社交媒体进行研究,这就需要根据关键字和特定账户等标准对信息进行采样。相比之下,以用户为中心的方法则试图重建用户为自己创造的个性化信息环境。大多数以用户为中心的研究分析的是用户通过浏览器直接访问的内容(如点击),而不是他们在社交媒体上看到的内容。本研究引入了我们自己设计的名为 PIEGraph 的数据收集系统,该系统将调查数据与从参与者的个性化 X(以前称为 Twitter)时间线中收集的帖子联系起来。因此,与以往的研究不同,我们的数据不仅包括用户决定点击的内容。我们测量了参与者各自馈送中的数据总量,并对其他三个感兴趣的量进行了描述性和推论性分析:政治内容、意识形态倾斜度和事实质量评级。我们的研究结果与当前有关数字回声室、错误信息和阴谋论的争论息息相关;根据数据的可用性,我们的一般方法论可应用于 X/Twitter 以外的社交媒体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What's in your PIE? Understanding the contents of personalized information environments with PIEGraph

Social media have long been studied from platform-centric perspectives, which entail sampling messages based on criteria such as keywords and specific accounts. In contrast, user-centric approaches attempt to reconstruct the personalized information environments users create for themselves. Most user-centric studies analyze what users have accessed directly through browsers (e.g., through clicks) rather than what they may have seen in their social media feeds. This study introduces a data collection system of our own design called PIEGraph that links survey data with posts collected from participants' personalized X (formerly known as Twitter) timelines. Thus, in contrast with previous research, our data include much more than what users decide to click on. We measure the total amount of data in our participants' respective feeds and conduct descriptive and inferential analyses of three other quantities of interest: political content, ideological skew, and fact quality ratings. Our results are relevant to ongoing debates about digital echo chambers, misinformation, and conspiracy theories; and our general methodological approach could be applied to social media beyond X/Twitter contingent on data availability.

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来源期刊
CiteScore
8.30
自引率
8.60%
发文量
115
期刊介绍: The Journal of the Association for Information Science and Technology (JASIST) is a leading international forum for peer-reviewed research in information science. For more than half a century, JASIST has provided intellectual leadership by publishing original research that focuses on the production, discovery, recording, storage, representation, retrieval, presentation, manipulation, dissemination, use, and evaluation of information and on the tools and techniques associated with these processes. The Journal welcomes rigorous work of an empirical, experimental, ethnographic, conceptual, historical, socio-technical, policy-analytic, or critical-theoretical nature. JASIST also commissions in-depth review articles (“Advances in Information Science”) and reviews of print and other media.
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