新冠疫情虚假信息检测多语言数据集
多语言推特文本数据集,针对新冠疫情虚假信息,提供二分类和多分类细粒度标注,覆盖四种语言。

基本信息
资源简介
新冠疫情虚假信息检测多语言数据集
【数据集背景】
本数据集源站标题为《COVID-19-disinformation》。
【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://modelscope.cn/datasets/QCRI/COVID-19-disinformation
使用许可:Apache License 2.0。
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(ModelScope)
方式一:MsDataset(Python)
# 前置依赖: pip install modelscope
from modelscope.msdatasets import MsDataset
ds = MsDataset.load("QCRI/COVID-19-disinformation", subset_name="default", split="train")
print(ds)
方式二:CLI(命令行)
pip install modelscope
modelscope download dataset QCRI/COVID-19-disinformation
数据集卡片摘录(源站)
COVID-19 Infodemic Multilingual Dataset
This repository contains a multilingual dataset related to the COVID-19 infodemic, annotated with fine-grained labels. The dataset is curated to address questions of interest to journalists, fact-checkers, social media platforms, policymakers, and the general public. The dataset includes tweets in Arabic, Bulgarian, Dutch, and English, focusing on both binary (misinformation detection) and multiclass classification (different types of infodemic content).
Table of Contents:
Dataset Overview
The dataset consists of tweets related to COVID-19, categorized under two tasks:
-
Binary Classification:
Detecting whether a tweet contains misinformation. -
Multiclass Classification:
Classifying the tweet into specific infodemic categories such as conspiracy theories, harmful content, or false cures.
Languages and Splits
The dataset includes the following languages, each with train, development (dev), and test splits:
- Arabic
- Bulgarian
- Dutch
- English
In addition to individual language datasets, a multilang directory contains a multilingual dataset where tweets from all the above languages are combined in the binary and multiclass formats.
File Formats
The dataset is provided in TSV (Tab-Separated Values) format. Each file contains tweet IDs, labels for seven questions (Q1-Q7), and binary/multiclass annotations. The actual tweet text and associated metadata are not included for privacy reasons.
Directory Structure
- Readme.md: This file
- arabic/, bulgarian/, dutch/, english/: Directories containing language-specific datasets for both binary and multiclass classification.
- multilang/: A directory containing the multilingual version of the dataset.
Each language and the multilingual directory include three sets:
traindevtest
The *_binary_* files correspond to binary classification, while the *_multiclass_* files correspond to multiclass classification.
Annotations
The dataset contains labels for the following seven questions (Q1-Q7), each related
许可
Apache License 2.0
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。




