acred: review the credibility of web content

Implements a number of AI bots to automatically review and explain the credibility of web content such as articles, tweets, sentences and websites.

Example Reviews

Not credible

Article "Clinton Body Count Series #5: Woman Set to Testify Against Clintons Blown Up in Home Explosion" seems not credible like its least credible Sentence Shawn Lucas Who Filed Fraud Case Against DNC and Hillary Found Dead! which agrees with:

• An FBI agent who exposed Hillary Clinton's corruption has been found dead. that seems not credible based on fact-check by snopes with textual claim-review rating 'false'

Article "Bill Gates Outlines 2018 Plan To Depopulate The Planet" seems not credible like its least credible Sentence Make no mistake, when Gates talks about making people healthier, what he is really talking about is enforcing the mandatory roll out of his range of experimental vaccinations. which agrees with:

• Bill Gates has openly admitted that vaccinations are designed so that governments can depopulate the world. that seems not credible based on fact-check by snopes with textual claim-review rating 'false'.

Credible

Article "Obama Urges Bernanke, Paulson to Fight Foreclosures, Hold Homeownership Summit" seems credible like its least credible Sentence WASHINGTON, DC -- U.S. Senator Barack Obama today sent a letter to Federal Reserve Chairman Bernanke and Treasury Secretary Paulson urging them to immediately convene a homeownership preservation summit with key stakeholders to fight foreclosures driven by growth in the subprime mortgage market. which agrees with:

• 'Two years ago...I wrote to Secretary Paulson, I wrote to Federal Reserve Chairman Bernanke, and told them [subprime lending] is something we have to deal with.' that seems credible based on fact-check by politifact with textual claim-review rating 'true'

Uncertain

TODO

How does it work?

Given some web-content d (this can be a sentence, tweet or webpage), acred will perform an analysis as depicted below:

Essentially, we:

• decompose the item d into sub items. For example a tweet contains individual sentences and references to webpages. Web pages, contain sentences and are published on specific websites, etc. The goal here is to arrive at a level (currently sentences and websites) for which we can make a credibility assessment.
• item linking: here we try to find similar items in our database, while keeping track of the specific link. For example, we can estimate the level of similarity and potential stance between 2 sentences.
• lookup credibility reviews for the items in our database. These are human-provided ratings from fact-checkers or journalists.
• aggregate: here, we apply heuristics while taking into account the item and decomposing links we found earlier to calculate the overall credibility for item d.

How well does it work?

As good as any automated system as of August 2020 🦾, which is not that great ☹️.

As of August 2020, acred achieves state of the art performance on:

• Clef'18 CheckThat! Factuality
• FakeNewsNet (the PolitiFact articles fragment)
• coinform250

State-of-the-art performance sounds (and is) good, but you only need to play around with the system a short while before you'll see many incorrect labels and explanations which are clearly incorrect. This is still very much a research prototype and you should not rely solely on the output of acred. Having said that, the overall architecture seems sound and easily extensible and assuming (i) progress in the underlying NLP tasks (semantic similarity, stance and checkworthiness detection) and (ii) an up-to-date and high-quality database of fact-checked claims; we expect acred to be able to evolve into a production-ready library.

Further details

See our paper Linked Credibility Reviews for Explainable Misinformation Detection to appear at the International Semantic Web Conference 2020.