Labeled Dataset of Misinformation on Facebook

Labeled Dataset of Misinformation on Facebook
The perils online platforms face, when they don't have high-quality misinformation datasets

Misinformation is a thorny issue for social media platforms. On the one hand, platforms want to allow the spread of new ideas – who knows where the next spark of genius will arise? On the other hand, the spread of misinformation can be dangerous.

It's a tricky balance! Consider, for example, how the CDC called face masks ineffective, or how Twitter blocked tweets about Hunter Biden’s laptop.

We help leading social media companies and nonprofits around the world fact-check and study misinformation. By building teams of Surgers who are politically balanced (for example, by making sure that every post is reviewed by both left-leaning and right-leaning reviewers) and well-versed in the dark corners of the Internet, customers have found that our misinformation datasets are higher-quality than what even professional fact-checkers produce!

To that end, we're releasing a dataset of Facebook misinformation. It contains 500 posts that Surgers across the political spectrum found misinformative.

Explore and download the Facebook misinformation dataset here!

Facebook Misinformation Examples

Here are a few misinformation examples from the dataset.

'An estimated $350 million in undisclosed royalties were paid to the National Institutes of Health (NIH) and hundreds of its scientists, including the agency's recently departed directory, Dr. Francis Collins, and Dr. Anthony Fauci'
The World Economic Forum and Pfizer said: THE GOAL IS TO REDUCE THE WORLD'S POPULATION BY 50% - BY 2023.
Paul Pelosi's DUI charges have been dropped. Is anyone really surprised?

Misinformation Labeling Guidelines

One of the difficulties faced by companies who label misinformation – whether to measure its prevalence, or to train machine learning models to detect it – is that their guidelines often contain subtle errors.

Why does this happen? Guidelines are often written by people who don’t see many examples of misinformation in the real world. Thus, their labeling guidelines are based on idealized thoughts based on examples that they hear about in the news, rather than real world examples in the wild.

For example, we’ve seen misinformation guidelines that ask labelers to only label posts that contain statements of facts. In theory, this makes sense – you don’t want to label opinions as misinformation.

However, what about deepfake videos – for example, posts that put fake audio into the President’s mouth? Or posts that take real content, but splice them together in misleading ways? These may not contain explicit “statements of fact” per se, but likely fall into the bucket of content a Misinformation team should want to capture.

This may seem like an easily resolvable problem, but the problem is that an operations manager designing the labeling task may ask “Does this post contain a statement of fact?” as the first question, and skip the rest of the questions if the answer is No. Operations managers at larger companies often don’t take time to label examples themselves or listen to feedback from their labelers, and in practice, the issue may go undetected for months or years. It’s crucial to have good labeling infrastructure and best practices!

Creating good labeling guidelines is hard. That's why we partner with misinformation researchers, and comb through hundreds of thousands of posts to understand the nuances in order to create ours. If you need help with your labeling guidelines, we’re always happy to help – just reach out to

More Surge AI Datasets

Want to build a custom social media misinformation dataset, or need help with other data labeling or content moderation projects? Sign up and create a new labeling project in seconds, or reach out to for a fully managed end-to-end labeling service.

Interested in more data? Check out our other free datasets and blog posts:

Jefferson Lee

Jefferson Lee

Jefferson leads Surge AI's data labeling and NLP products — whether it's helping customers label their large language models, gather data to train Spam and Hate Speech classifiers, or run large-scale search evaluations. He was previously an early engineer on Airbnb's Trust and Safety ML team, and studied computer science at Harvard.

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