Reranking partisan animosity in algorithmic social media feeds alters affective polarization

Today, social media platforms hold the 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 anti-democratic 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.
, The dataset is collected with custom instrumentation through a browser extension, web surveys, and with in-feed surveys added to the participants' feeds on X.
, , # Reranking partisan animosity in algorithmic social media feeds alters affective polarization

## Contact

If you have any questions, please feel free to reach out to:

* Martin Saveski ([[e-mail]](mailto:[e-mail]))
* Tiziano Piccardi ([[e-mail]](mailto:[e-mail]))

## Setup

Before running the scripts:

1. Set the working directory to the code folder: `setwd(\"/path-to-repository/code/\")`
2. Configure the paths in `_constants.R`
3. Make sure that you have installed all the packages listed in `requirements.md`

## Scripts summary

* `01_attrition.R`: Attrition analysis
* `02_balance.R`: Covariate balance analysis
* `03_polarizaton_main.R`: Affective polarization analysis
* `04_emotions_main.R`: Emotions analysis
* `05_att_main.R`: Political attitudes analysis
* `06_polarization_hte.R`: Affective polarization, heterogeneous treatment effects analysis
* `07_engagement_analysis.R`: Engagement analysis
* `08a_pate_raking.R`: Population Average Treatment Effects ..., We take the original ID, concatenate it with a secret salt string, and hash the resulting string. Hashing ensures that the original IDs can’t be easily recovered, and adding the salt protects against dictionary-based attacks, where an attacker may have a list of Bovitz or CloudResearch IDs to hash and compare against the anonymized ones. We received user consent to publish the data in de-identified form.

当前，社交媒体平台是研究信息流排序算法效应的唯一主体。我们开发了一种不依赖平台的方法，可实时对参与者的信息流进行重排序，并借助该方法于2024年美国总统竞选期间，在X平台上开展了一项预注册的10天现场实验，共有1256名参与者参与。本实验使用大语言模型（Large Language Model，LLM）对表达反民主态度与党派敌意（Anti-democratic attitudes and partisan animosity, AAPA）的帖文进行重排序。将此类内容的曝光量降低或提升，会使参与者在满分100分的情感温度计量表（Feeling Thermometer）上，对对立党派的敌意出现2分的偏移，且未观测到党派立场相关的显著差异，这为「接触AAPA内容会改变情感极化（Affective Polarization）」提供了因果性证据。本研究确立了一种无需平台合作即可研究信息流排序算法的方法，使得在自然场景下独立评估排序干预措施成为可能。
本数据集通过定制化工具采集，具体包括浏览器扩展程序、网络问卷，以及嵌入参与者X平台信息流的信息流内问卷。

# 重排序算法社交媒体信息流中的党派敌意可改变情感极化

## 联系方式

如有任何疑问，请随时联系：

* 马丁·萨夫斯基（Martin Saveski）：<mailto:[e-mail]>
* 蒂齐亚诺·皮卡迪（Tiziano Piccardi）：<mailto:[e-mail]>

## 实验设置

运行脚本前，请完成以下步骤：
1. 将工作目录设置为代码文件夹：`setwd("/path-to-repository/code/")`
2. 在`_constants.R`中配置路径信息
3. 确保已安装`requirements.md`中列出的所有依赖包

## 脚本概述

* `01_attrition.R`：流失分析
* `02_balance.R`：协变量平衡分析
* `03_polarizaton_main.R`：情感极化分析
* `04_emotions_main.R`：情绪分析
* `05_att_main.R`：政治态度分析
* `06_polarization_hte.R`：情感极化异质性处理效应分析
* `07_engagement_analysis.R`：参与度分析
* `08a_pate_raking.R`：总体平均处理效应分析……
我们将原始ID与秘密盐值字符串拼接后进行哈希运算。哈希操作可确保原始ID无法被轻易还原，而添加盐值则可抵御字典攻击——此类攻击中，攻击者可通过预先准备的Bovitz或CloudResearch ID列表进行哈希运算，再与去标识化后的ID进行比对。我们已获得用户同意，可将去标识化形式的数据公开发布。