mumin 0.1.3

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MuMiN-Build

This repository contains the package used to build the MuMiN dataset from the paper Nielsen and McConville: MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Dataset with Linked Social Network Posts (2021).

This is currently under review at NeurIPS 2021 Datasets and Benchmarks Track (Round 2). This dataset must not be used until this warning is removed as the dataset is subject to change, for example, during the review period.

Installation

The mumin package can be installed using pip:

$ pip install mumin

To be able to build the dataset Twitter data needs to be downloaded, which requires a Twitter API key. You can get one for free here. You will need the Bearer Token.

Quickstart

The main class of the package is the MuminDataset class:

>>> from mumin import MuminDataset
>>> dataset = MuminDataset(twitter_bearer_token=XXXXX)
>>> dataset
MuminDataset(size='large', compiled=False)

By default, this loads the large version of the dataset. This can be changed by setting the size argument to one of 'small', 'medium' or 'large'. To begin using the dataset, it first needs to be compiled. This will download the dataset, rehydrate the tweets and users, and download all the associated news articles, images and videos. This usually takes a while.

>>> dataset.compile()
MuminDataset(num_nodes=XXXXX, num_relations=XXXXX, size='large', compiled=True)

After compilation, the dataset can also be found in the ./mumin folder as separate csv files. This path can be changed using the dataset_dir argument when initialising the MuminDataset class. If you need embeddings of the nodes, for instance for use in machine learning models, then you can simply call the add_embeddings method:

>>> dataset.add_embeddings()
MuminDataset(num_nodes=XXXXX, num_relations=XXXXX, size='large', compiled=True)

Note: If you need to use the add_embeddings method, you need to install the mumin package as either pip install mumin[embeddings] or pip install mumin[all], which will install the transformers and torch libraries. This is to ensure that such large libraries are only downloaded if needed.

It is possible to export the dataset to the Deep Graph Library, using the to_dgl method:

>>> dgl_graph = dataset.to_dgl()
>>> type(dgl_graph)
dgl.heterograph.DGLHeteroGraph

Note: If you need to use the to_dgl method, you need to install the mumin package as pip install mumin[dgl] or pip install mumin[all], which will install the dgl and torch libraries.

Dataset Statistics

Size	#Claims	#Threads	#Replies	#Retweets	#Users	#Languages	%Misinfo
Large	12,347	24,773	1,024,070	695,924	4,306,272	41	94.57%
Medium	5,265	10,195	480,249	305,300	2,004,300	37	94.07%
Small	2,089	4,126	220,862	132,561	916,697	35	92.87%

Related Repositories

• MuMiN, containing the paper in PDF and LaTeX form.
• MuMiN-trawl, containing the source code used to construct the dataset from scratch.