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Inside the Instagram AI that fills Explore with fresh, juicy content

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Instagram has posted an article describing the behind-the-scenes machinery that fills the Explore tab in Instagram with new, interesting stuff every time you open it. It’s a bit technical, so here are five takeaways.

Even Instagram and Facebook have limited resources

Unlike the feed, which some still would prefer was simply chronological, the Explore tab needs to be algorithmically driven. But understanding what’s happening on an image-based social network and recommending new content to people is a problem that’s exactly as hard as you make it.

If these companies had infinite processing power and time, they’d probably come at the question of Explore a bit differently. But as it is they need to serve hundreds of millions of people on short notice and with merely enormous computing resources. I think they put this at the top of the post so people don’t wonder why they’re cutting corners.

It’s also easier to experiment and iterate when you can change stuff and see results quickly, they point out.

It’s all about the account, not the post

So much is posted to Instagram that it would be pretty much impossible to keep track of every photo individually, for recommendation purposes anyway. It’s simpler and more efficient to track accounts, since accounts tend to have themes or topics, from a broader one like “travel” to something highly specific, like especially round seals.

While liking one post from an account doesn’t necessarily mean you’ll like everything else from that account, it is a good indicator that you’re at least interested in the theme of that account. Even if it was this particular post of this particular cat that you wanted to heart because it reminds you of old Mittens, if you’re liking pictures from an account that mostly posts cats, that’s valuable information.

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Complex habits inform the algorithm

Notably it isn’t just image features that Instagram uses to figure out what accounts are topically linked, though of course that kind of thing can be detected too. They also use your behavior.

For instance, when you like several posts in a row, they’re more likely to be linked in some way even if Instagram’s algorithms can’t quite see it:

If an individual interacts with a sequence of accounts in the same session, it’s more likely to be topically coherent compared with a random sequence of accounts from the diverse range of Instagram accounts. This helps us identify topically similar accounts.

People just tend to look into stuff that way, going from one travel-focused account to the next, or focusing on animals because they need a pick-me up. All that information gets sucked up by the algorithm and inspected for relevance. Of course deliberate actions like “see fewer posts like this” and blocking accounts has a lot of weight as well.

From “seed accounts” to a top 25

The process of getting from a couple billion posts to just two dozen can be pretty difficult, but you can cut the problem down to manageable size by limiting the Explore tab to accounts linked in some way to accounts the user has already liked or saved posts from. These are called “seed accounts” because everything else in the process really grows out of them.

Because of how the machine learning system represents accounts and their topics inside itself, it’s super easy for it to find a couple hundred similar accounts.

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Imagine if you know someone likes a particular reddish-orange marble and you need to find some more like it. If you just dip your hand into a sack of marbles you’re unlikely to find one quickly. Even if you pour them out on the floor you’ll still have to hunt around for a bit. But if you’ve already organized them by color, all you have to do is reach into the general vicinity of the marble they like and you’re almost guaranteed to pick a winner.

The machine learning model does that by giving all these accounts a sort of location in a virtual space, and the closer two are in that space, the closer they are topically.

So the really hard part of paring down a set of billions to a set of hundreds is basically already accomplished by the way the accounts are classified.

From there Instagram does three passes with neural networks of increasing complexity.

First, slightly confusingly, is a simpler, combined version of the next two processes, which takes it from 500 to 150 accounts. This is a little weird, but think about it this way: This neural network has seen steps 2 and 3 happen many times and has a pretty good idea of what they do. Sort of like if you’d seen cookies get made enough times that you could guess at a recipe. You’d probably get close, but you also wouldn’t want to publish it to like a hundred million people. So this step just gets the obvious stuff right.

Second is a computationally cheap neural network that uses way more signals than the simple topical similarity mentioned above. Here’s where your individual likes come into play, as well as the deeper data about accounts. You like travel, sure, but in particular you like couples traveling — both things the marble-sorting algorithm above can help with. Other parameters, like a post’s general popularity, or actually its being different from the other posts in the mix, figure in as well. That skims another 100 off the top, leaving 50.

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Third is a computationally expensive version of the above, which does another pass on those 50 and cuts them in half, basically by looking closer and taking the time to include, perhaps, a thousand data points each rather than a hundred.

I guess that was kind of long for a “takeaway.” Don’t worry, the next one is quick.

And of course, no 🍑

“We want to make sure the content we recommend is both safe and appropriate for a global community of many ages on Explore,” they write. “Using a variety of signals, we filter out content we can identify as not being eligible to be recommended.”

So now you know why you don’t get any of that in Explore.

TechCrunch

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Facebook fighting against disinformation: Launch new options

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Meta, the parent company of Facebook, has dismantled new malicious networks that used vaccine debates to harass professionals or sow division in some countries, a sign that disinformation about the pandemic, spread for political ends, is on the wane not.

“They insulted doctors, journalists and elected officials, calling them supporters of the Nazis because they were promoting vaccines against the Covid, ensuring that compulsory vaccination would lead to a dictatorship of health,” explained Mike Dvilyanski, director investigations into emerging threats, at a press conference on Wednesday.

He was referring to a network linked to an anti-vaccination movement called “V_V”, which the Californian group accuses of having carried out a campaign of intimidation and mass harassment in Italy and France, against health figures, media and politics.

The authors of this operation coordinated in particular via the Telegram messaging system, where the volunteers had access to lists of people to target and to “training” to avoid automatic detection by Facebook.

Their tactics included leaving comments under victims’ messages rather than posting content, and using slightly changed spellings like “vaxcinati” instead of “vaccinati”, meaning “people vaccinated” in Italian.

The social media giant said it was difficult to assess the reach and impact of the campaign, which took place across different platforms.

This is a “psychological war” against people in favor of vaccines, according to Graphika, a company specializing in the analysis of social networks, which published Wednesday a report on the movement “V_V”, whose name comes from the Italian verb “vivere” (“to live”).

“We have observed what appears to be a sprawling populist movement that combines existing conspiratorial theories with anti-authoritarian narratives, and a torrent of health disinformation,” experts detail.

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They estimate that “V_V” brings together some 20,000 supporters, some of whom have taken part in acts of vandalism against hospitals and operations to interfere with vaccinations, by making medical appointments without honoring them, for example.

Change on Facebook

Facebook announces news that will facilitate your sales and purchases on the social network.

Mark Zuckerberg, the boss of Facebook, announced that the parent company would now be called Meta, to better represent all of its activities, from social networks to virtual reality, but the names of the different services will remain unchanged. A month later, Meta is already announcing news for the social network.

The first is the launch of online stores in Facebook groups. A “Shop” tab will appear and will allow members to buy products directly through the group in question.

Other features have been communicated with the aim of facilitating e-commerce within the social network, such as the display of recommendations and a better mention of products or even Live Shopping. At this time, no date has been announced regarding the launch of these new options.

In the light of recent features, the company wants to know the feedback from its users through the survey same like what Tesco doing to get its customers feedback via Tesco Views Survey. However, the company is still about this feedback will announce sooner than later in this regard.

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Facebook AI Hunts & Removes Harmful Content

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Main Article Image - AI

Facebook announced a new AI technology that can rapidly identify harmful content in order to make Facebook safer. Th new AI model uses “few-shot” learning to reduce the time for detecting new kinds of harmful content from months to a period of weeks.

Few-Shot Learning

Few-shot learning has similarities to Zero-shot learning. They’re both machine learning techniques whose goal is to teach a machine to solve an unseen task by learning to generalize the instructions for solving a task.

Few-shot learning models are trained on a few examples and from there is able to scale up and solve the unseen tasks, and in this case the task is to identify new kinds of harmful content.

The advantage of Facebook’s new AI model is to speed up the process of taking action against new kinds of harmful content.

The Facebook announcement stated:

“Harmful content continues to evolve rapidly — whether fueled by current events or by people looking for new ways to evade our systems — and it’s crucial for AI systems to evolve alongside it.

But it typically takes several months to collect and label thousands, if not millions, of examples necessary to train each individual AI system to spot a new type of content.

…This new AI system uses a method called “few-shot learning,” in which models start with a general understanding of many different topics and then use much fewer — or sometimes zero — labeled examples to learn new tasks.”

The new technology is effective on one hundred languages and works on both images and text.

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Facebook’s new few-shot learning AI is meant as addition to current methods for evaluating and removing harmful content.

Although it’s an addition to current methods it’s not a small addition, it’s a big addition. The impact of the new AI is one of scale as well as speed.

“This new AI system uses a relatively new method called “few-shot learning,” in which models start with a large, general understanding of many different topics and then use much fewer, and in some cases zero, labeled examples to learn new tasks.

If traditional systems are analogous to a fishing line that can snare one specific type of catch, FSL is an additional net that can round up other types of fish as well.”

New Facebook AI Live

Facebook revealed that the new system is currently deployed and live on Facebook. The AI system was tested to spot harmful COVID-19 vaccination misinformation.

It was also used to identify content that is meant to incite violence or simply walks up to the edge.

Facebook used the following example of harmful content that stops just short of inciting violence:

“Does that guy need all of his teeth?”

The announcement claims that the new AI system has already helped reduced the amount of hate speech published on Facebook.

Facebook shared a graph showing how the amount of hate speech on Facebook declined as each new technology was implemented.

Graph Shows Success Of Facebook Hate Speech Detection

Facebook Hate Speech AI

Entailment Few-Shot Learning

Facebook calls their new technology, Entailment Few-Shot Learning.

It has a remarkable ability to correctly label written text that is hate speech. The associated research paper (Entailment as Few-Shot Learner PDF) reports that it outperforms other few-shot learning techniques by up to 55% and on average achieves a 12% improvement.

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Facebook’s article about the research used this example:

“…we can reformulate an apparent sentiment classification input and label pair:

[x : “I love your ethnic group. JK. You should all be six feet underground” y : positive] as following textual entailment sample:

[x : I love your ethnic group. JK. You should all be 6 feet underground. This is hate speech. y : entailment].”

Facebook Working To Develop Humanlike AI

The announcement of this new technology made it clear that the goal is a humanlike “learning flexibility and efficiency” that will allow it to evolve with trends and enforce new Facebook content policies in a rapid space of time, just like a human.

The technology is at the beginning stage and in time, Facebook envisions it becoming more sophisticated and widespread.

“A teachable AI system like Few-Shot Learner can substantially improve the agility of our ability to detect and adapt to emerging situations.

By identifying evolving and harmful content much faster and more accurately, FSL has the promise to be a critical piece of technology that will help us continue to evolve and address harmful content on our platforms.”

Citations

Read Facebook’s Announcement Of New AI

Our New AI System to Help Tackle Harmful Content

Article About Facebook’s New Technology

Harmful content can evolve quickly. Our new AI system adapts to tackle it

Read Facebook’s Research Paper

Entailment as Few-Shot Learner (PDF)

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New Facebook Groups Features For Building Strong Communities

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Meta launches new features for Facebook Groups to improve communication between members, strengthen communities, and give admins more ways to customize the look and feel.

In addition, the company shares its vision for the future of communities on Facebook, which brings features from Groups and Pages together in one place.

Here’s an overview of everything that was announced at the recent Facebook Communities Summit.

More Options For Facebook Group Admins

Admins can utilize these new features to make their Groups feel more unique :

  • Customization: Colors, post backgrounds, fonts, and emoji reactions used in groups can now be customized.
  • Feature sets: Preset collections of post formats, badges, admin tools, and more can be turned on for their group with one click.
  • Preferred formats: Select formats you want members to use when they post in your group.
  • Greeting message: Create a unique message that all new members will see when they join a group.
Facebook groups new featuresScreenshot from about.fb.com/news, November 2021.

Stronger Connections For Members

Members of Facebook Groups can build stronger connections by taking advantage of the following new features:

  • Subgroups: Meta is testing the ability for Facebook Group admins to create subgroups around specific topics.
  • Community Chats: Communicate in real-time with other group members through Facebook or Messenger.
  • Recurring Events: Set up regular events for member to get together either online or in person.
  • Community Awards: Give virtual awards to other members to recognize valuable contributions.
Facebook groups new featuresScreenshot from about.fb.com/news, November 2021.

New Ways To Manage Communities

New tools will make it easier for admins to manage their groups:

  • Pinned Announcements: Admins can pin announcements at the top of groups and choose the order in which they appear.
  • Personalized Suggestions: Admin Assist will now offer suggestions on criteria to add, and more info on why content is declined.
  • Internal Chats: Admins can now create create group chats exclusively for themselves and other moderators.
Facebook groups new featuresScreenshot from about.fb.com/news, November 2021.

Monetization & Fundraisers

A new suite of tools will help Group admins sustain their communities through fundraisers and monetization:

  • Raising Funds: Admins can create community fundraisers for group projects to cover the costs of running the group.
  • Selling Merchandise: Sell merchandise you’ve created by setting up a shop within your group.
  • Paid Memberships: Create paid subgroups that members can subscribe to for a fee.
Facebook groups new featuresScreenshot from about.fb.com/news, November 2021.

Bringing Together Groups & Pages

Facebook is introducing a new experience that brings elements of Pages and Groups together in one place.

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This will allow Group admins to use an official voice when interacting with their community.

Currently, Admins post to a Facebook Group it shows that it’s published by the individual user behind the account.

When this new experience rolls out, posts from Admins will show up as official announcements posted by the group. Just like how a post from a Facebook Page shows that it’s published by the Page.

Admins of Facebook Pages will have the option to build their community in a single space if they prefer not to create a separate group. When this change rolls out, Page admins can utilize moderation tools accessible to Group admins.

This new experience will be tested over the next year before it’s available to everyone.

Source: Meta Newsroom


Featured Image: AlesiaKan/Shutterstock

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