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 Journal of the Association for Information Science and Technology Pub Date : 2024-01-12 DOI:10.1002/asi.24869
Deen Freelon, Meredith L. Pruden, Daniel Malmer, Qunfang Wu, Yiping Xia, Daniel Johnson, Emily Chen, Andrew Crist
{"title":"你的 PIE 里有什么?利用 PIEGraph 了解个性化信息环境的内容","authors":"Deen Freelon,&nbsp;Meredith L. Pruden,&nbsp;Daniel Malmer,&nbsp;Qunfang Wu,&nbsp;Yiping Xia,&nbsp;Daniel Johnson,&nbsp;Emily Chen,&nbsp;Andrew Crist","doi":"10.1002/asi.24869","DOIUrl":null,"url":null,"abstract":"<p>Social media have long been studied from <i>platform-centric</i> perspectives, which entail sampling messages based on criteria such as keywords and specific accounts. In contrast, <i>user-centric</i> 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.</p>","PeriodicalId":48810,"journal":{"name":"Journal of the Association for Information Science and Technology","volume":"75 10","pages":"1119-1133"},"PeriodicalIF":2.8000,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What's in your PIE? Understanding the contents of personalized information environments with PIEGraph\",\"authors\":\"Deen Freelon,&nbsp;Meredith L. Pruden,&nbsp;Daniel Malmer,&nbsp;Qunfang Wu,&nbsp;Yiping Xia,&nbsp;Daniel Johnson,&nbsp;Emily Chen,&nbsp;Andrew Crist\",\"doi\":\"10.1002/asi.24869\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Social media have long been studied from <i>platform-centric</i> perspectives, which entail sampling messages based on criteria such as keywords and specific accounts. In contrast, <i>user-centric</i> 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.</p>\",\"PeriodicalId\":48810,\"journal\":{\"name\":\"Journal of the Association for Information Science and Technology\",\"volume\":\"75 10\",\"pages\":\"1119-1133\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Association for Information Science and Technology\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/asi.24869\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Association for Information Science and Technology","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/asi.24869","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
Cover Image Issue Information Embodied and dialogical basis for understanding humans with information: A sustainable view Cover Image Issue Information
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1