可靠性
适度
感知
启动(农业)
心理学
关系(数据库)
情感(语言学)
滤波器(信号处理)
计算机科学
社会心理学
政治学
数据挖掘
计算机视觉
沟通
植物
生物
发芽
神经科学
法学
作者
Yu Won Oh,Chong Hyun Park
标识
DOI:10.1177/00027642231174331
摘要
As artificial intelligence (AI)-powered technology enables the efficient processing of large volumes of comments, more companies, including news publications, are experimenting with and adopting AI moderation to manage their commenting platforms. However, the resulting user experiences have been largely underexplored in relation to these technical advances in comments section management. This study used an experiment to examine the impact of AI filters on individuals’ perceptions about comments sections (i.e., bias, credibility, positive and negative affect, and use intention). The findings indicate that AI moderation had statistically significant impacts on perceived credibility and use intention. Beyond the main effects, priming with a deceptive comment issue moderated the impacts of comment filtering on user perceptions as well.
科研通智能强力驱动
Strongly Powered by AbleSci AI