Code and data for “From Face to Relations: Politeness strategies in Enron’s workplace email”

This repository contains the data and code supporting the study “From Face to Relations: Politeness Strategies in Enron’s Workplace Email.” It enables full transparency and reproducibility of the analytical pipeline, including data construction, network modeling, and statistical analysis.

The folder discussion_output provides the processed datasets used to generate the figures in the article. These files correspond directly to the visualizations reported in the Discussion section and can be used to reproduce all plotted results.

The files case_candidates_by_PDR (case1–4).csv and case_candidates_by_role (case5–10).csv document the selection procedure for qualitative case analyses. The former identifies candidate emails based on the P–D–R framework (Power, Distance, and Imposition), while the latter selects cases according to organizational roles within Enron (e.g., CEO and other hierarchical positions). These files ensure that all illustrative examples in the paper are traceable and systematically derived.

The script discussion_experiments.py contains the full data analysis workflow used to produce the results in the Discussion section. It corresponds directly to the outputs stored in the discussion_output folder, including statistical summaries and plotting-ready data.

The dataset messages_R_with_politeness.csv is the core message-level file. It includes email metadata and text-derived features, such as request identification, imposition score (R), five decomposed dimensions (cost/effort, urgency, risk/accountability, autonomy constraint, and dependency blocking), and multi-label annotations of four politeness strategies (bald-on-record, positive politeness, negative politeness, and off-record).

The file node_metrics.csv contains node-level network attributes (e.g., centrality measures, clustering, community assignment, and power index), while dyadic_metrics.csv provides edge-level attributes (e.g., tie strength, relative power, and distance-related measures). The file global_metrics.txt reports overall network statistics.

Together, these resources support a multi-level analysis that integrates pragmatics with social network structure, allowing other researchers to replicate, validate, and extend the findings.

本仓库包含支撑研究《从脸面到关系：安然（Enron）职场邮件中的礼貌策略》（From Face to Relations: Politeness Strategies in Enron’s Workplace Email）所用的数据与代码，可实现分析全流程的透明化与可复现性，涵盖数据构建、网络建模与统计分析三个环节。

discussion_output 文件夹内含用于生成论文图表的经预处理数据集，这些文件与论文讨论部分呈现的可视化内容一一对应，可用于复现所有绘图结果。

case_candidates_by_PDR（case1–4）.csv 与 case_candidates_by_role（case5–10）.csv 两份文件记录了定性案例分析的筛选流程：前者基于权力-距离-要求（Power, Distance, and Imposition，简称P-D-R）框架识别候选邮件，后者则依据安然公司内部的组织角色（如首席执行官（CEO）及其他层级岗位）筛选案例。上述文件确保论文中所有示例均可追溯且经系统推导得出。

discussion_experiments.py 脚本包含用于生成讨论部分结果的完整数据分析流程，其与 discussion_output 文件夹中存储的输出结果一一对应，涵盖统计汇总与可直接用于绘图的数据。

messages_R_with_politeness.csv 是核心的消息级数据集，包含邮件元数据与文本衍生特征，具体包括请求识别结果、要求得分（imposition score，记为R）、五个分解维度（成本/投入、紧迫性、风险/问责性、自主权约束与依赖阻断），以及四种礼貌策略（直白记录式（bald-on-record）、积极礼貌策略、消极礼貌策略与非公开式（off-record））的多标签标注。

node_metrics.csv 文件包含节点级网络属性（如中心性指标、聚类系数、社区分配结果与权力指数），而 dyadic_metrics.csv 则提供边级网络属性（如联结强度、相对权力与距离相关指标）。global_metrics.txt 文件则报告整体网络统计量。

综上，本仓库的所有资源支撑了将语用学与社会网络结构相结合的多层级分析，可供其他研究人员复现、验证并拓展该研究的发现。