---
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