通过实时重排信息流，证明减少极端言论可降低政治敌意。
Reranking partisan animosity in algorithmic social media feeds alters affective polarization
- 用大模型实时重排用户信息流，干预极端内容曝光
- 减少极端内容曝光使对立党派敌意下降2个点（100分制）
- 无需平台配合，适合独立研究算法影响的学者
当前社交媒体平台独占信息流算法效果的研究权。我们开发了一种平台无关的实时重排方法，在2024年美国总统大选期间对X平台上的1256名参与者进行了预注册的10天实地实验。实验使用大语言模型对表达反民主态度和政党敌意（AAPA）的内容进行重排。减少或增加AAPA内容的曝光，使对立党派的敌意在100分情感温度计上变化2分，且党派间无显著差异，提供了暴露于AAPA内容会改变情感极化的因果证据。该研究建立了一种无需平台合作即可在自然场景下评估排序干预的方法。
原文摘要 · Abstract (English)
Today, social media platforms hold sole power to study the effects of feed ranking algorithms. We developed a platform-independent method that reranks participants' feeds in real-time and used this method to conduct a preregistered 10-day field experiment with 1,256 participants on X during the 2024 U.S. presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by two points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content alters affective polarization. This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings.
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