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---
dataset_info:
features:
- name: id
dtype: int64
- name: image_0
dtype: image
- name: post_message
dtype: string
- name: user_name
dtype: string
- name: timestamp_post
dtype: float64
- name: num_like_post
dtype: float64
- name: num_comment_post
dtype: string
- name: num_share_post
dtype: string
- name: label
dtype: int64
- name: image_1
dtype: image
- name: image_2
dtype: image
- name: image_3
dtype: image
- name: image_4
dtype: image
- name: image_5
dtype: image
- name: image_6
dtype: image
- name: image_7
dtype: image
- name: image_8
dtype: image
- name: image_9
dtype: image
- name: image_10
dtype: image
- name: image_11
dtype: image
splits:
- name: train
num_bytes: 266237686.984
num_examples: 4372
- name: test
num_bytes: 188399599.28
num_examples: 3288
download_size: 453773802
dataset_size: 454637286.264
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# ReINTEL: A Multimodal Data Challenge for Responsible Information Identification on Social Network Sites
We are running a competition based on this dataset at: https://aihub.ml/competitions/795. Top 3 solutions will be invited to submit technical report. The challenge will be concluded with a result paper, to be included in the proceeding of the [Reliable AI workshop at ACML](https://workshop2024.reliable-ai.org/).
Please check our paper for more details: https://aclanthology.org/2020.vlsp-1.16.pdf
>This paper reports on the ReINTEL Shared Task for Responsible Information Identification on social network sites, which is hosted at the seventh annual workshop on Vietnamese Language and Speech Processing (VLSP 2020). Given a piece of news with respective textual, visual content and metadata, participants are required to classify whether the news is `reliable' or `unreliable'. In order to generate a fair benchmark, we introduce a novel human-annotated dataset of over 10,000 news collected from a social network in Vietnam. All models will be evaluated in terms of AUC-ROC score, a typical evaluation metric for classification. The competition was run on the Codalab platform. Within two months, the challenge has attracted over 60 participants and recorded nearly 1,000 submission entries.
## Load Dataset
Load the dataset using the ```datasets``` library in ```python```:
```python
from datasets import load_dataset
train_dataset = load_dataset("ReliableAI/ReINTEL", split="train") # with ground-truth labels
test_dataset = load_dataset("ReliableAI/ReINTEL", split="test") # without ground-truth labels
```
## Data Format
Each instance includes 6 main attributes with/without a binary target label as follows:
id: unique id for a news post on SNSs
user_name: the anonymized id of the owner
post_message: the text content of the news
timestamp_post: the time when the news is posted
image_{i}, where i is in [0,12]: image of type ```PIL.JpegImagePlugin.JpegImageFile``` associated with the news
num_like_post: the number of likes that the news is received
num_comment_post: the number of comment that the news is received
num_share_post: the number of shares that the news is received
label: a manually annotated label which marks the news as potentially unreliable
- 1: unreliable
- 0: reliable |