Kai Nakamura, Sharon Levy, and William Yang Wang. 2020. r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
Website: https://fakeddit.netlify.app/
Codalab Competition: https://competitions.codalab.org/competitions/25337
Paper: https://arxiv.org/abs/1911.03854
Our lab: http://nlp.cs.ucsb.edu/index.html
Follow the instructions to download the dataset. You can download text, metadata, comment data, and image data.
Note that released test set is public. Private test set is used for leaderboard (coming soon).
Please read the Usage section. It is important.
Please let us know if you encounter any problems by opening an issue or by directly contacting us.
Please read the USAGE section before using or downloading. Download the v2.0 dataset from here
Option 1: (RECOMMENDED) Download the images here.
Option 2:
The *.tsv dataset files have an image_url column which contain the image urls. You can use the URLs to download the images.
For convenience, we have provided a script which will download the images for you. Please follow the instructions if you would like to use the attached script.
Fork or clone this repository and install required python libraries
$ git clone https://github.com/entitize/Fakeddit
$ cd Fakeddit
$ pip install -r requirements.txt
Copy image_downloader.py to the same directory/folder as where you downloaded the tsv files.
Run image_downloader.py in the new directory/folder
$ python image_downloader.py file_name
Download the comment data from here
Please note that results in the paper are based on multimodal samples only (samples that have both text and image). In our paper, only samples that have both image and text were used for the baseline experiments and error analysis. Thus, if you would like to compare against the results in the paper, use the samples in the multimodal_only_samples folder.
If there are Unnamed... columns, you can ignore or get rid of them. Use the clean_title column to get filtered text data.
comments.tsv consists of comments made by Reddit users on submissions in the entire released dataset. Use the submission_id column to identify which submission the comment is associated with. Note that one submission can have zero, one, or multiple comments.