Vax-Culture
包含6373条标注推文的Twitter疫苗讨论数据集,用于研究疫苗犹豫和误导信息。

基本信息
资源简介
Vax-Culture
【数据内容】
源站原始描述:Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter - mrzarei5/Vax-Culture
【适用方向】
文本类数据可用于医学自然语言处理,如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面:https://github.com/mrzarei5/Vax-Culture
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/mrzarei5/Vax-Culture.git
curl -L -o repo.zip https://github.com/mrzarei5/Vax-Culture/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录(使用方式)
Requirements
Installation
Create a conda environment and install dependencies:
conda create name Vax_Culture file requirements.txt
conda activate Vax_Culture
Dataset
-
Copy the contents of Vax-Culture dataset directory (link) to
filelists. -
Pull the tweets using their provided unique identifiers from Twitter and append their texts to
filelists/Vax_Culture.csvunder the column nametweet_text. (Any subset of the tweets inVax_Culture.csvcan be dropped. Just remember not to alter the header of columns.) -
Create train, validation and test subsets by running
filelists/prepare_datasets.py:
python prepare_datasets.py
Classification Tasks
Run classification.py with the desired model and problem task:
python classification.py model_name Bert_base problem communicated_message
Parameter model_name can be selected from Bertweet, Bert_base, Bert_large, Roberta_base and Roberta_large for any of the four classification problem tasks.
Parameter problem can be selected from any of the values: inaccurate_or_misleadning for misleading or inaccurate information detection task, communicated_message for communicated message prediction task, multilabel_criticism for subjects of criticism prediction task, multilabel_support for subjects of support/promote prediction task.
Text Generation Task
Run generation.py with the desired model:
python generation.py model_name Bart_large problem gen
Parameter model_name can be Bart_large or T5_large.
数据加载示例(表格/文本类)
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
数据缺失?
依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。




