测试五种新算法，降低社交平台极化，同时保持用户活跃度。
The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement
- 用浏览器插件替换三平台内容排序，对比五种新算法效果。
- 极化指数平均降0.03标准差，党派情感温差减少1.5度。
- 提升社交体验负面反馈但不损害心理状态与新闻认知。
我们首次在多个平台上直接比较多种替代性社交媒体算法对社会重要指标的影响。通过浏览器插件，随机将9,386名桌面用户分配至对照组或五种替代排序算法之一，在美国2024年总统大选期间连续六个月调整三个平台的内容展示。结果显示，预注册的效价极化指数平均下降0.03个标准差（p < 0.05），包括党派内/外情感温度计差异下降1.5度。Facebook活跃时长减少0.37分钟/天，Reddit减少0.2分钟/天，但X/Twitter增加0.32分钟/天（p < 0.01）。负面社交体验报告增多，但对幸福感、新闻知识、跨党派共情、对党派暴力的感知与支持均无显著影响。这表明，引入连接性内容可在不牺牲平台参与度的前提下改善部分社会结果。
原文摘要 · Abstract (English)
We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify which posts were shown to desktop social media users, randomly assigning 9,386 users to a control group or one of five alternative ranking algorithms which simultaneously altered content across three platforms for six months during the US 2024 presidential election. This reduced our preregistered index of affective polarization by an average of 0.03 standard deviations (p < 0.05), including a 1.5 degree decrease in differences between the 100 point inparty and outparty feeling thermometers. We saw reductions in active use time for Facebook (-0.37 min/day) and Reddit (-0.2 min/day), but an increase of 0.32 min/day (p < 0.01) for X/Twitter. We saw an increase in reports of negative social media experiences but found no effects on well-being, news knowledge, outgroup empathy, perceptions of and support for partisan violence. This implies that bridging content can improve some societal outcomes without necessarily conflicting with the engagement-driven business model of social media.
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