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The best mannequins, Donald Trump's u

Time:2016-11-17 00:24Shoes websites Click:

Best Trump Donald mannequins

In today's roundup we tell you who's winning the #MannequinChallenge; Donald Trump is called out as he changes his mind about electoral colleges; and "post-truth" becomes the Oxford Dictionary's word of the year, much to the dismay of many.

Who's winning the #MannequinChallenge?

Image copyright Instagram/blackinbluefilm_

You know it must be almost over when even we have done the #MannequinChallenge. The phrase has been trending on social media for weeks but who's winning it?

It's a tough competition - pretty much everyone has joined the craze since this group of high school students kicked it off in October.

But one version by actor and director Simone Shepherd, which recreates several events that have driven the Black Lives Matter movement, is by far the most moving to have emerged.

Others that deserve our entirely abitrary special mention include: Rob Kardashian and his wife Blac Chyna, for taking it on in the labour ward, and the Spanish football team, who display more imagination than most in the locker room.

The way things are going it looks like we might need a special category for dogs as well: Enrique Iglesias's is the one to beat.

Image copyright Facebook/Enrique Iglesias

What did you say again?

Image copyright Twitter/@realDonaldTrump

America's president-elect Donald Trump is getting called out again for tweeting something that directly contradicts what he said before.

Yesterday Mr Trump said the electoral college "is actually genius in that it brings all states, including the smaller ones, into play".

It was only a minute before users noted "you changed your tone from four years ago", and began referring to a tweet from 2012, in which he called the electoral college "a disaster for a democracy".

The tweet is only the latest example of what many are citing as evidence that Mr Trump's tendency to flip-flop on positions. Last week many took him to task for another two posts only nine hours apart. "Dude. You know we can still see all your old Tweets, right?" asked one user.

Image copyright Twitter/@realDonaldTrump

Lies, damned lies and post-truth

A man reading the Oxford Dictionary of English

Image copyright PA
Image caption Oxford Dictionaries shortlist for word of the year include "coulrophobia, "Brexiteer", "chatbot" and "hygge"

The Oxford Dictionary has declared "post-truth" their word of the year. And nobody on Twitter appears to like it.

To some it is an indictment of events this year: "The politics of 2016 in a nutshell," said one tweet. "2016: depressing year, depressing word of the year from Oxford Dictionaries," said another.

Others took issue with the fact that, to them, the word itself further loosened our grip on reality:

"'Post-truth' is such an awful, misleading, over-polite and inaccurate description of the of the phenomenon it tries to describe, ironically," said one user.

"Funny old things dictionaries - we used to use the phrase Lies," said a third.

Getting your jam on

Image copyright Sina Weibo/Huangshang Ninzheshi Ximaia

A video of women abandoning their cars to dance in the middle of a traffic jam has been watched several million times on social media in China.

Although state media has praised them for showing a "positive attitude" in the face of long jams caused by heavy smog, social media users are less impressed.

The square-dancing that the women are carrying out in the video is a popular past-time in China, but it has also provoked disputes over the noise involved, as it often takes place in public squares at night.

In 2015, the government introduced regulations to determine things like what times it could take place and how loud the music could be.

The user who first uploaded the video said it showed that people were "still very optimistic" despite being stuck for between five and six hours. But others have laughed off the dancers, saying they were embarrassing, with one joking it looked like "a journey through hell".

Additional reporting by BBC Monitoring


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