Image purporting to show CNN graphic of Texas election results is fake – full facts

A fake image is circulating on social media that appears to show CNN broadcasting the US election results from the state of Texas before Election Day.

But the broadcaster has confirmed that the graphic, which looks like a “major race warning” and shows Kamala Harris six points ahead of Donald Trump with 2.1 million votes, is not real.

Since the early hours of November 3rd (UK time), several social media accounts have been sharing on (sic).” It was also shared on Instagram and threads.

The image also includes a progress bar in the right corner, indicating that a percentage of votes have already been counted, and in the left corner it says “Poll closed at 9:00 a.m. ET.”

Although early voting was available in Texas from October 21st to November 1st, the image was circulating days before the polls opened on November 5th at 7:00 p.m. CST (that would be 8:00 p.m. ET, not 9:00 p.m p.m.) closed in the state, and no results — including early voting — have been announced yet.

And the math in the picture isn't right either. Although the graphic claims that Ms. Harris is 121,408 votes ahead of Mr. Trump, the difference between the votes attributed to them is actually 131,408.

Emily Kuhn, CNN's senior vice president of communications, told Full Fact via email: “This image is completely fabricated and manipulated and has never aired on any CNN platform.”

The results of the US presidential election will not be confirmed until the last polls close on November 6 at 1 a.m. EST (6 a.m. GMT). Polls in Texas will be open on Election Day from 7:00 a.m. to 7:00 p.m. CST, and in El Paso from 7:00 a.m. Mountain Time. Early voting results for Texas are often released shortly after polls close at 7 p.m.

Full Fact has debunked a number of misleading social media posts during the election campaign, including fake CNN headlines, doctored photos and likely AI-generated images.

We've written a series of guides to help you, including how to spot misleading images and videos. We also created a toolkit to help identify misinformation and wrote about how to identify AI-generated images and videos.