情感(语言学)
政治
极化(电化学)
内容(测量理论)
心理学
集合(抽象数据类型)
社会心理学
新闻媒体
广告
样品(材料)
内容分析
政治学
业务
社会学
计算机科学
化学
数学
沟通
法学
社会科学
数学分析
物理化学
色谱法
程序设计语言
作者
Andrew M. Guess,Neil Malhotra,Jennifer Pan,Pablo Barberá,Hunt Allcott,Taylor Brown,Adriana Crespo-Tenorio,Drew Dimmery,Deen Freelon,Matthew Gentzkow,Sandra González‐Bailón,Edward H. Kennedy,Young Mie Kim,David Lazer,Devra Moehler,Brendan Nyhan,Carlos Velasco Rivera,Jaime E. Settle,Daniel Robert Thomas,Emily Thorson
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2023-07-27
卷期号:381 (6656): 404-408
被引量:85
标识
DOI:10.1126/science.add8424
摘要
We studied the effects of exposure to reshared content on Facebook during the 2020 US election by assigning a random set of consenting, US-based users to feeds that did not contain any reshares over a 3-month period. We find that removing reshared content substantially decreases the amount of political news, including content from untrustworthy sources, to which users are exposed; decreases overall clicks and reactions; and reduces partisan news clicks. Further, we observe that removing reshared content produces clear decreases in news knowledge within the sample, although there is some uncertainty about how this would generalize to all users. Contrary to expectations, the treatment does not significantly affect political polarization or any measure of individual-level political attitudes.
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