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Chinese internet celebrities convert 470 million fans into consumers

At 6am, Yang Xia, 26, is already up and about, complete with attractive makeup, ready to roll for yet another hectic 12-hour work day to turbocharge new-age Chinese brands.

As the day unfolds, Yang, wearing branded fashion, her own creation, would pose in front of the cameras with several famous Macau landmarks for backdrop.

She is not a Chinese film-slash-TV actress, sports superstar, cultural icon or political luminary; yet, she commands a staggering 2.56 million followers on Twitter-like Sina Weibo.

Her status makes her a hot property among local brand-endorsing celebrities, a one-woman sales army as it were.

Yang is a shining example of a new, arguably unique, breed of internet celebrities who are rewriting the rules of marketing, branding and e-commerce in China.

Dubbed wanghong (Chinese for internet celebrity), China’s online sensations are influential. They help push products through livestreaming and other forms of digital content that sway millions of mobile-savvy, social media-addicted consumers, spawning a consumer goods market worth billions of dollars.

Photo: Weibo

And this is just the beginning, according to Feng Yousheng, founder and CEO of Chinese livestreaming app Inke.

During the 4th World Internet Conference in Wuzhen, Zhejiang province, earlier this month, he said the coming fifth-generation or 5G mobile technology and new-age tech like augmented reality will help make livestreaming even more powerful.

“In recent years, those born after 1995 have taken a profound fancy to livestreaming,” Feng said.

Feng may have well been referring to data from the China Internet Network Information centre, which said the country had 751 million netizens by the end of June – and 96.3 per cent of them access the internet through hand-held devices like smartphones.

Most of them love internet celebrities who are basically of two types: original content producers such as Papi Jiang who makes short comedy videos, and online shopkeepers such as Yang who peddle fashion, makeup, skincare and bodycare products through online marketplaces such as Alibaba’s Taobao.

Yang’s online cosmetics store on Taobao pulled in 2.7 million yuan (S$551,000) on Nov 11, which is celebrated as Singles Day or Double-Eleven – 11-11 – online shopping festival in China.

Wanghong themselves earn much more annually.

For instance, Zhang Dayi, one of China’s well-known cyber-celebrities, reportedly helped sell goods worth more than 100 million yuan during the first half-hour on Nov 11.

Wanghong’s modus operandi usually involves hours of posting picture-perfect slice-of-life images or streaming similar scenes on social media. They also share useful experiences with netizens, especially female consumers, the so-called millennials (those born in the 1980s and 1990s).

Yang, for one, offers a range of feminine advice on makeup, slimming and skincare.

Photo: Weibo

A mother of two kids, she also shares cutesy images of mom-child interactions and postnatal recovery experiences.

This new form of real-time, persuasive, riveting interaction engages consumers and results in purchases of recommended products eventually.

During her college days, Yang was a big fan of fashion. She still is. Owing to limited financial resources back then, she could afford only a few dresses. That necessitated imaginative ways of matching the coats, jackets, pantsuits and skirts in her modest wardrobe.

Encouraged by positive feedback from friends and admiring glances of onlookers, she honed her instinctive sense of what would click as eye-pleasing fashion. That led her to register an online store on Taobao while still at college. And she actually began to run the garment business in 2015.

Last year, her Sina Weibo account peaked with 100,000 followers visiting on a single day.

Emboldened, she set up a cosmetics store on Taobao, which pulls in around 3 million yuan in monthly revenue now. She closed the first garment store last year, only to reopen it this year, which rakes in hundreds of thousands of yuan every month.

“Last year, I really felt tired running two businesses. I wanted to spend more time with my family,” she said.

Photo: Weibo

A report released in June jointly by internet consultancy iResearch and Sina Weibo said China’s internet celebrities are riding their fame to establish new types of businesses of their own, creating a whole new chain surrounding themselves.

Online video streaming and short videos are key parts of these businesses. Their revenues come from a wide range of sources, including e-commerce, gifts from viewers of streamed content, subscriptions and advertising.

Without specifying the actual number of online celebrities with more than 100,000 followers each, Sina Weibo said their tribe increased by 57.3 per cent this year, with a collective fan base of 470 million consumers, up 21 per cent from 2016.

This has led market insiders to refer to this phenomenon as wanghong economy.

Sensing its immense potential to boost sales and spawn brands, investors are actively backing new training schools for, wait for it, internet celebrities.

Such ventures, called talent incubators, aim to unearth and nurture the next Zhang Dayi who could monetise the power to grab eyeballs and influence minds.

Ruhnn, which started as a women’s wear brand on Taobao, is now a leading incubator of internet celebrities. It trains students in various skills like providing integrated e-commerce services.

Aspiring wanghong learn how to better operate online stores, and produce and publish original content.

Last year, Ruhnn signed up Yang Xia to teach her how to run a wide range of businesses.

“I really learnt a lot from Ruhnn. Their professional guidance on design enabled me to have a better understanding of texture and colour of garments, costumes, so on,” said Yang.

According to Ruhnn, it has trained nearly 100 fashion opinion leaders or FOLs (a variant of KOLs or key opinion leaders) – euphemism, or jargon, for wanghong.

The firm claimed it generated more than 1 billion yuan in gross merchandise value last year, with its own valuation reaching 3.1 billion yuan.

Feng Min, founder and CEO of Ruhnn, said online celebrities are actually internet opinion leaders or IOLs, with the ability to influence the millennials.

“Online celebrities serve as both brand ambassadors and content providers. Those with the potential to become online celebrities usually have their own understanding of the internet. They know how to interact with their followers,” Feng said.

For Feng, the blossoming wanghong economy signifies a new trend in e-commerce.

“Currently, we are at a very early phase of wanghong e-commerce industry. It will take some time to integrate it digitally with the whole industry chain.”

In the future, people would not treat online celebrities as a new phenomenon because by then all e-commerce companies will likely have their brands, made popular by original content produced by IOLs, he said.

Some Google searches are now answered by selfie videos from celebrities

Available first on mobile with Mark Wahlberg, Gina Rodriguez, Seth MacFarlane, and others.

Google’s always testing out new features here and there for making its many services as great as can be, and the latest one has to do with its bread and butter, Google Search. In a blog post that was published on December 7, Product Manager Rami Banna announced that some searches on Google will now showcase selfie videos from your favorite celebrities.

d6268_google-search-shortcuts Some Google searches are now answered by selfie videos from celebrities

This is something that’s launching first on mobile, and when asking something such as “Can Will Ferrell really play the drums?”, you’ll see a video that you can play in which Will Ferrell answers that question directly through a selfie video. This is something that seems to have come completely out of left field, but after messing around with it for a couple minutes, it’s actually kind of fun.

You can pause a video once it starts by tapping on your screen, and swiping through the cards near the bottom or letting the video finish playing will take you to the next question.

d6268_google-search-shortcuts Some Google searches are now answered by selfie videos from celebrities

Celebrities that you’ll find video responses from include Priyanka Chopra, Will Ferrell, Tracee Ellis Ross, Gina Rodriguez, Kenan Thompson, Allison Williams, Nick Jonas, Mark Wahlberg, James Franco, Seth MacFarlane, Jonathan Yeo and Dominique Ansel.

The video responses don’t show up when asking questions via Google Assistant, but they work fine when searching through the main Google app.

Oreo update rolling out to Android Wear, already available on Watch Sport

[Watch] French internet celebrities raise funds for Rohingya refugees in Bangladesh

A group of French internet celebrities have teamed up to raise funds for the Rohingya refugees in Bangladesh with the power of social media.

So far, they were able to raise US $579,335 of their $1 million target, reports AJ+, an online news and current events channel run by Al Jazeera Media Network, in a recent video.

The group calls themselves the ‘Love Army’, is running a 48-hour funding drive for the refugees who fled from the persecutions conducted by the security forces in Myanmar.

In the AJ+ video, Jérôme Jarre, one of the French internet celebrities, reaches out to Turkish president in this regard.

In addition, they have also created a page called ‘GoFundMe page’ to collect fund for the Rohingya refugees in Bangladesh.

Meet the ‘big stomach’ internet celebrities behind China’s live-stream eating craze

“Hello everyone! I’m Tongtong, the big stomach.”

Smiling into the video camera, 25-year-old Hu Tongtong is dwarfed by mountains of food. Today, it might be 200 dumplings. Tomorrow, perhaps 93 eggs, or several dozen kebabs.

As the camera rolls, the 43.5kg Heilongjiang native consumes enough food to satisfy a large family, pausing only briefly to wash down her meal with a soft drink or water.

Introduced from South Korea and Japan in recent years, “eating broadcasts” have rapidly gained popularity in China. The hosts are nearly all young, slim women. Some attract audiences with their exaggerated expressions and gestures, others are known for using quirky ways to make food, while those like Hu raise eyebrows because of their bottomless stomachs.

Hu’s single-sitting consumption records include 93 eggs, 200 dumplings, 76 egg tarts, 5kg of hamburgers and 48 lamb kebabs.

At an eating competition held in Chongqing in May, Hu chalked up a convincing victory over her four rivals – three men and one woman – by eating 17 bowls of noodles in 10 minutes. The rest of the pack struggled to finish between five and seven bowls each.

Hu began streaming her ordinary meals on a live broadcasting app in July last year after seeing videos of other “big stomach kings”.

“I can eat a lot, too. Since I also need to have supper, which on average contains much food, why not show it online?” Hu told the South China Morning Post. She said her goal was to let more people share the joy of delicious food.

Hong Kong’s International Burger Eating Competition draws competitive eaters and their fans to Wan Chai fast food restaurant

Her experience is similar to that of many of her broadcasting counterparts who use their big appetites – sometimes seen as an embarrassment for girls in traditional Chinese culture – to achieve fame and fortune.

Online broadcasting apps, a rapidly growing industry in China over the past few years, allow the big eaters’ videos to reach millions.

 Meet the 'big stomach' internet celebrities behind China's live-stream eating craze

A survey by Qq.com last year found that more than half of the people born on the mainland after 1995 aspired to do online broadcasting in hopes of becoming an online celebrity.

Zhang Yumi, better known on the internet as Mi Zi Jun, is one of the most successful eating-broadcasting hosts in the country. She was among the first Chinese people to live-stream themselves eating, and her single-meal records include 4kg of rice, 10 bowls of pineapple rice, eight bowls of rice noodles and 10 pig’s feet.

Why Hong Kong needs to start talking about eating disorders

Zhang weighs less than 50kg and stands 1.58m tall. Fans compliment her on her sweet smile and girl-next-door looks.

With more than 4.7 million followers at Weibo, Zhang has established a studio with her boyfriend for producing food-related videos. Many companies have approached them about doing product placement, offering hundreds of thousands of yuan per broadcast.

Other “big stomach” stars include Kinoshita Yuuka, a Japanese woman, and a South Korean man only known as Banzz.

 Meet the 'big stomach' internet celebrities behind China's live-stream eating craze

Hu and other slim hosts with prodigious appetites have sparked speculation on the internet that they force themselves to vomit after broadcasts, something Hu and Zhang have denied. “I have such a body that I will never get fat no matter how much I eat,” Hu said, adding that she does not diet.

“There are many things that cannot be explained by science, I think.”

But eating to excess produces a host of long-term issues, according to Sun Jianyong of the digestive department of Shanghai’s Zhongshan Hospital.

Emotional eating: why we shouldn’t comfort or reward children with sweets and snacks

“It’s bad for the stomach, liver and pancreas,” he said. “Actually, the whole digestive system will be affected badly. In the long term, eating too much will lead to fatty liver.”

For Hu, a big stomach has been a reality since she was a youngster. “I think my stomach has been expanded little by little,” she said. “I remember when I was a primary school pupil, I could eat four packs of instant noodles a time. Each pack is 90 grams.”

Her food consumption for each meal approximately matched that of 9-10 adults. Her parents are accustomed to her inordinate eating.

 Meet the 'big stomach' internet celebrities behind China's live-stream eating craze

“They don’t object to me [eating so much] since medical examinations show I am healthy,” she said. “My parents also hold a traditional mindset that a person able to eat a lot is an auspicious thing.”

Hu is engaged to a man whose waistline already is showing the effects of her lifestyle. “He gained 15kg in weight in the first two months after we were together,” she said.

Why mainlanders have lost their appetite for Hong Kong’s fast food

When she is not working at her parents’ Tianjin cosmetics company, she does broadcasts in her spare time. With her video work earning her up to 20,000 yuan (US$3,000) a month from advertising, barely covering her food costs, she regards it more as a hobby than a career.

Hu also said she had no desire to turn this pastime into a vocation because at some point she intended to take over her family’s business. Her videos were designed to “share good food” with others, she said, adding she feels happy with the growth in her supporter base.

 Meet the 'big stomach' internet celebrities behind China's live-stream eating craze

One fan wrote on Weibo, “Watching [Hu] Tongtong’s video makes me feel hungry even though I have just eaten.”

“What do these people’s stomachs look like? It’s beyond my imagination!” another netizen wrote.

Gu Jun, a Shanghai University sociologist, said people watched the eating broadcasts out of curiosity.

One in four Hongkongers eats more than twice too much red meat daily, survey finds

“In ancient times, people watched acrobats [perform] stunning feats on the street,” he said. “Now thanks to the new technology, people are watching the performance of big stomach kings.

“It’s humanity that does not change. Those fans are fairly bored.”

I feel like I know these computer-generated celebrities already


3600d_celebs1 I feel like I know these computer-generated celebrities already

If there’s one thing this Earth of ours is short on, it’s celebrities. I mean, if there were enough, why would they keep making them? We need the help of our computer friends. Luckily they are obliging. An artificial intelligence that in its existence has only known the faces of celebs (how I envy it!) was tasked with making up new ones by the dozen. The results are… well, you see for yourself.

Okay, so these wouldn’t all pass muster as headshots. The droplet coming off the fellow’s ear on the lower left up top is frankly unsightly. The earring worn by top center is a bit… unfinished. The older person in the lower right’s ears are unfortunate. Bizarro Tay Tay (?) looks good though. And they all appear to have real personalities, though, despite not existing.

3600d_celebs1 I feel like I know these computer-generated celebrities alreadyHere, it’s the hair that seems to give trouble. Top left has a few flyaways, while top center has a couple stray portals in there — can’t blow those out! And top right’s bangs appear to be making a play for the rest of her face. Lower center I think they caught at a bad moment, and lower left is missing his right… body.

All kidding aside, though, this is pretty impressive. That a computer can essentially dream up people who unless you look closely you might think are ordinary celebrities is pretty impressive. The research was conducted by Nvidia researchers and has been submitted to be presented at next year’s International Conference on Learning Representations.

The paper, which you can read ahead of time here, uses what are called General Adversarial Networks. Essentially, you train two networks using the same data, in this case a ton of celebrity runway photos with the faces centered. One network attempts to learn how to recreate photos like those, while the other learns to recognize them.

You have the creator network attempt to make new photos and then have the recognizer network rate them and send over feedback — at the start, it’s probably pretty rough. But over time and many, many iterations, the creator network will start putting out things that look good enough that the recognition network says, yeah you know, I guess that could be Tiffani Amber Thiessen.

3600d_celebs1 I feel like I know these computer-generated celebrities already

More examples of celebrities generated by the system.

The main insight described by this paper is that both networks perform better if you start them small, with low-resolution images, and work up to bigger ones. That makes sense intuitively: learning to understand the general shape of a face and other gestalt features comes first, so you don’t get weird false positives like walls of flesh or hair-beasts with realistic mouths.

It also has the side effect of requiring less time and processing to train up the models; it’s not easy generating megapixel-size images, and there’s no sense wasting time doing so when early ones will be mostly garbage. Keep the garbage small and easy to create, or the network might just learn to scale up garbage and add inconsequential details, as we see here with the results from a previous GAN system:

3600d_celebs1 I feel like I know these computer-generated celebrities alreadyThe researchers admit that “there is a long way to true photorealism,” but the quality of the new celebrities seen here is good enough that they expect it may be the first category to produce results more or less indistinguishable from real people.

One thing I can say for sure, though. When trained with a wide variety of general images, this GAN sure was great at producing, among other things, nightmare cats, nightmare birds, nightmare cows, nightmare dogs, and nightmare horses. (I guess the last ones are just nightmares.)

3600d_celebs1 I feel like I know these computer-generated celebrities already

Definitely click and view this at full size… if you dare.

I feel like I know these computer-generated celebrities already …


e50e5_celebs1 I feel like I know these computer-generated celebrities already ...

If there’s one thing this Earth of ours is short on, it’s celebrities. I mean, if there were enough, why would they keep making them? We need the help of our computer friends. Luckily they are obliging. An artificial intelligence that in its existence has only known the faces of celebs (how I envy it!) was tasked with making up new ones by the dozen. The results are… well, you see for yourself.

Okay, so these wouldn’t all pass muster as headshots. The droplet coming off the fellow’s ear on the lower left up top is frankly unsightly. The earring worn by top center is a bit… unfinished. The older person in the lower right’s ears are unfortunate. Bizarro Tay Tay (?) looks good though. And they all appear to have real personalities, though, despite not existing.

e50e5_celebs1 I feel like I know these computer-generated celebrities already ...Here, it’s the hair that seems to give trouble. Top left has a few flyaways, while top center has a couple stray portals in there — can’t blow those out! And top right’s bangs appear to be making a play for the rest of her face. Lower center I think they caught at a bad moment, and lower left is missing his right… body.

All kidding aside, though, this is pretty impressive. That a computer can essentially dream up people who unless you look closely you might think are ordinary celebrities is pretty impressive. The research was conducted by Nvidia researchers and has been submitted to be presented at next year’s International Conference on Learning Representations.

The paper, which you can read ahead of time here, uses what are called General Adversarial Networks. Essentially, you train two networks using the same data, in this case a ton of celebrity runway photos with the faces centered. One network attempts to learn how to recreate photos like those, while the other learns to recognize them.

You have the creator network attempt to make new photos and then have the recognizer network rate them and send over feedback — at the start, it’s probably pretty rough. But over time and many, many iterations, the creator network will start putting out things that look good enough that the recognition network says, yeah you know, I guess that could be Tiffani Amber Thiessen.

e50e5_celebs1 I feel like I know these computer-generated celebrities already ...

More examples of celebrities generated by the system.

The main insight described by this paper is that both networks perform better if you start them small, with low-resolution images, and work up to bigger ones. That makes sense intuitively: learning to understand the general shape of a face and other gestalt features comes first, so you don’t get weird false positives like walls of flesh or hair-beasts with realistic mouths.

It also has the side effect of requiring less time and processing to train up the models; it’s not easy generating megapixel-size images, and there’s no sense wasting time doing so when early ones will be mostly garbage. Keep the garbage small and easy to create, or the network might just learn to scale up garbage and add inconsequential details, as we see here with the results from a previous GAN system:

e50e5_celebs1 I feel like I know these computer-generated celebrities already ...The researchers admit that “there is a long way to true photorealism,” but the quality of the new celebrities seen here is good enough that they expect it may be the first category to produce results more or less indistinguishable from real people.

One thing I can say for sure, though. When trained with a wide variety of general images, this GAN sure was great at producing, among other things, nightmare cats, nightmare birds, nightmare cows, nightmare dogs, and nightmare horses. (I guess the last ones are just nightmares.)

e50e5_celebs1 I feel like I know these computer-generated celebrities already ...

Definitely click and view this at full size… if you dare.




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