Effects of community management on user activity in online communities

Data and code needed to reproduce the results of the paper "Effects of community management on user activity in online communities", available in draft here.

Instructions:

Unzip the files.

Start with JSON files obtained from calling platform APIs: each dataset consists of  one file for posts, one for comments, one for users. In the paper we use two datasets, one referring Edgeryders, the other to Matera 2019.

Run them through edgesense (https://github.com/edgeryders/edgesense). Edgesense allows to set the length of the observation period. We set it to 1 week and 1 day for Edgeryders data, and to 1 day for Matera 2019 data. Edgesense stores its results in a file called JSON network.min.json, which we then rename to keep track of the data source and observation length.

Launch Jupyter Notebook and run the notebook provided to convert the network.min.json files into CSV flat files, one for each netwrk file

Launch Stata and open each flat csv files with it, then save it in Stata format.

Use the provided Stata .do scripts to replicate results.

Please note: I use both Stata and Jupyter Notebook interactively, running a block with a few lines of code at a time. Expect to have to change directories, file names etc.

本仓库包含复现论文《在线社区社群管理对用户活跃度的影响》（Effects of community management on user activity in online communities）结果所需的数据与代码，论文草案可在此处获取。

操作指南：
1. 解压所有文件。
2. 首先处理通过调用平台API获取的JSON格式数据：每个数据集包含帖子、评论、用户三类数据文件。本论文使用两个数据集，分别对应Edgeryders平台与2019年马泰拉（Matera 2019）项目数据。
3. 将数据导入Edgesense工具（https://github.com/edgeryders/edgesense）。该工具支持自定义观测窗口时长，我们对Edgeryders数据集设置为1周1天，对Matera 2019数据集设置为1天。Edgesense会将分析结果存储为名为`network.min.json`的文件，后续我们会对该文件重命名，以标注数据源与观测窗口时长。
4. 启动Jupyter Notebook，运行提供的Notebook代码，将所有`network.min.json`文件转换为CSV格式的扁平化文件，每个网络数据文件对应一个CSV文件。
5. 启动Stata软件，导入上述生成的CSV扁平化文件，随后将其保存为Stata格式文件。
6. 使用提供的Stata .do脚本复现论文结果。

注意事项：
本教程采用交互式方式运行Stata与Jupyter Notebook，每次仅运行数行代码块。请根据实际运行环境调整工作目录、文件名等参数。