At the Oscars, Parasite Makes Best Picture History

At Sunday’s Oscars, on a night when almost everything went as planned and as usual, the one true surprise came in the biggest moment of all.

For the first time ever, a film in a foreign language won Best Picture when Bong Joon-ho’s Parasite, a comedy-drama-thriller about class and secrets, took the big prize. Bong also won the awards for Best Director and Best Original Screenplay. He    1     three   warm and generous   speeches,   including one when he won for his directing and thanked fellow nominees (被提名的人) Martin Scorsese and Quentin Tarantino for inspiring him and contributing to the progress of his career,     2    .

And, while he gave most of his speeches in Korean with interpreter Sharon Choi, when he won the first one, he stopped and noted in English: “This is very first Oscar to South Korea.” The affection in the room for the film was    3     every time it was mentioned, as well as in its great competition of award. The evening began three and a half hours earlier with a(n)     4     of the old and the new: the kind of themed singing-dancing number that used to be the    5       Oscars opening, but in the absence of a traditional host, it was performed by the thoroughly modern Janelle Monáe. A version of her own song Come Alive was    6     by dancers dressed in costumes showing respect to black filmmakers. The speech that followed, however, was much more    7     and unpleasant, despite the best efforts of Chris Rock   and Steve Martin, the latter of whom    8     blew the name of best actress nominee Cynthia Erivo.

The awards in general have been heavily criticized for how white the nominees are, the failure to nominate any women directors in a year when several made highly regarded films, and plenty of other lack of inclusivity. The Academy’s    9     with that criticism seemed evident: There may not have been a lot of performers of color among the nominees, but there were a lot among the presenters and speakers and performers. Several presenters made mention of the    10     at issue, which is the kind of thing that happens when people are placed in a situation they don’t want to ignore but also aren’t there to challenge too much.

AI can distinguish between bots and humans based on Twitter activity

Artificial intelligence is being used to spot the difference between human users and fake accounts on Twitter.

Emilio Ferrara at the University of Southern California in the US, and his colleagues have trained an AI to detect bots on Twitter based on differences in     1     of activity between real and fake accounts.

The team analysed two separate datasets of Twitter users, which had been classified either manually or by a pre-existing algorithm as either bot or human.

The manually verified dataset     2     of 8.4 million tweets from 3500 human accounts, and 3.4 million tweets from 5000 bots.

The researchers found that human users replied four to five times more often to other tweets than bots did. Real users gradually become more interactive, with the fraction of replies increasing over the course of an hour-long session of Twitter use.

The length of tweets by human users also decreased as sessions     3    . “The amount of information that is exchanged diminishes,” says Ferrara. He believes that the change may result from a cognitive depletion over time, in which people become less likely to expend mental effort    4     original content.

Bots, on the other hand, show no changes in their interactivity or the length of information they tweet over time.

The team also analysed the amount of time between any two consecutive (连读的) tweets from a single user. When this     5     is plotted, bots showed spikes for certain time gaps, such as tweeting at 30-minute or 60-minute intervals.

The team then combined these     6     to train an existing bot-detection algorithm, called Botometer, on the difference in activity patterns. The AI was     7     more likely to accurately detect to fake accounts than when it was not taking into     8     the timing of posts.

The algorithm could be used to     9     other bot-detection tools that analyse the language within posts, says Ferrara.

One of the study’s     10     is that the Twitter data the team analysed is from three years ago. In that time, it’s possible that bots have become more human-like in their activity patterns.