Dataset and code for predicting users's indirect aggression

Code: Run the Python script main.py to build and evaluate the model. Data: "Raw_data" DIRECTORY includes files for each users' profile and user-generated content.`` "Survey_score" FILE contains the users' scores for three dimension of indirect aggression. The column "Anonymous_id" in the XLSX FILE can be matched to the names of JSON FILES in "Raw_data" DIRECTORY. user_features+label.zip is the processed file with users' features and labels. Note that all the information is strictly anonymized.代码说明： 运行Python脚本`main.py`即可构建并评估该模型。 数据说明： "Raw_data"目录包含各用户的档案文件及用户生成内容文件。 "Survey_score"文件存储了用户在间接攻击三个维度上的得分。 该XLSX文件中的"Anonymous_id"列可与"Raw_data"目录下的JSON文件名称进行匹配关联。 `user_features+label.zip`为经过预处理的文件，包含用户特征与标签信息。 备注：所有信息均已完成严格匿名化处理。