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---
layout: default
---
<div class="header-container jumbotron">
<div class="container">
<h1>CDL Misinfo Datasets</h1>
<p>A repository for misinformation datasets and benchmarks for detection made by <a href="https://www.complexdatalab.com/">Complex Data Lab </a></p>
</div>
</div>
<div class="container">
<div class="row">
<div class="col-md-6">
<h6 class="lead">Misinformation is a challenging societal issue, and mitigating solutions are difficult to
create due to data deficiencies. To address this problem, we have curated the largest collection of
(mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of
the 36 datasets that consist of statements or claims.
If you would like to contribute a novel dataset or report any issues,
please <a href="mailto:misinfodataset@googlegroups.com">email us</a>, visit our <a
href="https://huggingface.co/datasets/ComplexDataLab/Misinfo_Dataset">Hugging Face</a>, or <a
href="https://github.com/ComplexData-MILA/misinfo-datasets">GitHub</a>.</h6>
</div>
<div class="col-md-6 text-center">
<img src="{{ '/assets/img/misinfo_logo.png' | relative_url }}" alt="Jekyll logo" class="img-responsive" style="width: 400px; height: auto;">
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<hr>
<div class="row">
<div class="col-sm-4">
<h1 class="text-center"><i class="fa fa-cubes" aria-hidden="true"></i></h1>
<!-- <h1 class="text-center"><img src="{{ "/assets/img/network.png" | relative_url }}" alt="Jekyll logo" class="img-responsive"></h1> -->
<h3 class="text-center">The largest collection of (mis)information datasets </h3>
<h5>A curated collection of <a href="https://arxiv.org/abs/2409.00009">75 misinformation datasets</a>, and a
unified setup to work with the 36 claim and statement datasets, available <a
href="/docs/dataset_overview/">here.</a></h5>
</div>
<div class="col-sm-4">
<h1 class="text-center"><i class="fa fa-cogs" aria-hidden="true"></i></h1>
<h3 class="text-center">Dataset Quality Assessment</h3>
<h5>We evaluated the quality of 36 datasets, identifying potential flaws such as insufficient label quality,
spurious correlations, and political bias. This helps researchers select datasets that are suitable for
their work.</h5>
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<div class="col-sm-4">
<h1 class="text-center"><i class="fa fa-wrench" aria-hidden="true"></i></h1>
<h3 class="text-center">Evaluation of Detection Models</h3>
<h5><a href="url">Our paper</a> provides state-of-the-art baselines for misinformation detection models on
these datasets, demonstrating the limitations of categorical labels and suggesting alternative
evaluation methods.</h5>
</div>
</div>
<hr>
<!-- <blockquote>
Camille et al (2024).
<strong>A Guide to Misinformation Detection Datasets</strong>. arXiv.
<a href="https://arxiv.org/abs/2409.00009" target="_blank">https://arxiv.org/abs/2409.00009</a>
</blockquote> -->
</div>