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Deep Fakes, Disinformation, and Fact-Checking

With the amount of information online, how do you discern what’s real and what’s fake?

Social media users and consumers of news now navigate a landscape rich with information but also wrought with fake news, misinformation, disinformation as well as more advanced forms of manipulation of various types of media such as deep fakes.

In order to counter the spread of fake news and false information in general, fact-checking has become imperative in the current information landscape. Human fact-checking can be a time consuming process and with the speed of the spread of fake news, manual verification or validation can become insufficient. Automated fact-checking (AFC) “automates” the process by using digital tools and techniques such as natural language processing (NLP), machine learning, knowledge representation, and databases to detect misinformation.

This report looks into the worldwide patenting landscape for fact-checking or generally, the detection of false information — fake news, misinformation, disinformation — in the form of digital media such as video, images, audio, and text. The verification of online information is also included in the report.

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