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推推算法增加了YouTube上的新闻消费和多样性
Xudong Yu1, Muhammad Haroon2, Ericka Menchen-Trevino3
1Department of Communication, University of North Dakota, Grand Forks, USA.
PNAS nexus
|December 2, 2024
概括
这项研究通过使用平衡的新闻内容来推动YouTube的推系统来减轻社交媒体算法偏见. 这种干预措施增加了新闻消费,减少了意识形态偏见,但没有损害用户参与度.
科学领域:
- 社交媒体的算法
- 计算社会科学 计算社会科学
- 信息科学 信息科学 信息科学
背景情况:
- 社交媒体平台上的推算法显著影响用户的注意力和信息消费模式.
- 关键的偏见包括兴趣偏见 (将内容缩小到非新闻/娱乐) 和意识形态偏见 (加强党派观点).
研究的目的:
- 在推算法中进行计算干预,以减轻兴趣和意识形态偏见.
- 调查是否通过平衡的新闻内容来推动算法增加了新闻推和消费.
- 评估均衡新闻输入对建议多样性和跨界曝光的影响.
主要方法:
- 一个子玩偶实验与一个为期一个月的随机实验相结合,涉及2,142名频繁使用YouTube的用户.
- 干预涉及播放来自经过验证,意识形态平衡的新闻频道的视频,以影响算法.
- 分析了用户层面的推和消费数据,以及调查结果.
主要成果:
- 推动算法显著而可持续地增加了新闻推和消费.
- 建议和消费的意识形态偏见被最小化,特别是对保守的用户来说.
- 算法建议对用户暴露的影响比用户暴露对后续建议的影响更大.
- 直接 nudging 用户对新闻消费没有明显的影响.
- 增加的新闻消费没有显著影响政治参与,信仰准确性,两极分化或民主规范.
结论:
- 平台推算法对用户的注意力和信息暴露有很大的影响.
- 针对算法而不是用户的计算干预可以有效地减轻内容偏见.
- 该干预成功地增加了新闻消费,减少了意识形态偏见,但没有对用户参与产生负面影响,这表明了现实世界应用的潜力.
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