Social media platforms like Facebook, Twitter, and Instagram have to cope with floods of data on a daily basis, making artificial intelligence (AI) applications like machine learning (ML) and deep learning (DL) vital for reducing fraud, spotting and blocking fake bot accounts, and generally delivering a better user experience.
Experiments have shown that it can take just a few days for a new social media account to go from innocently participating in Christian parenting groups to seeing QAnon conspiracy theories, or from sharing yoga posts to being showered with anti-vax material. This slide is largely blamed on AI algorithms, but the same algorithms are also used to weed out hate material and help users find new friends and thought leaders.
Like it or not, AI is the engine driving social media. As we grow more familiar with its capabilities and drawbacks and see how emerging quantum computing systems make it more powerful, there’s reason to hope that AI-driven social media of the future will keep improving. Those investing in quantum computing stocks might note the natural alliance of AI/ML and quantum capabilities for social media.
How social media gets to know you
Social media channels and mobile apps gather data about you every time you visit the platform or use the app. We’re talking not just your name and email address, but also the photos you post, tweets you share, posts you like, and which topics you write about. Often this is legal, included in the terms and conditions that you signed when you registered (even if you didn’t read them), but sometimes it is not.
Once they have the data, companies use ML and other AI analytics to discover deep trends in your moods and preferences so they can understand you better.
AI algorithms work by learning from the patterns they detect. If you repeatedly post about yoga, it pushes more yoga content. If you post about cats, it will show you kitten photos and not puppy photos. The more time you spend on the site or app, the better it knows you.
AI and ML are the engine powering your online community
Among other things, social media platforms use AI tools to:
- Automatically translate posts in a different language
- Suggest people to tag in photos
- Spot trending topics and hashtags
- Suggest filters and align them with your face
- Identify and block hate speech and spam
- Spot and delete accounts
- Understand intent for search queries (especially voice search)
The primary use of AI is to target users with specific content, whether that’s organic content or paid ads. With AI analysis, social media can suggest other users you might know, accounts or topics to follow, and autoplay videos with related content, helping you create a community that reflects your interests and concerns.
AI is the engine, and your data is the fuel, powering Facebook and Instagram’s popular ad marketplaces too. They offer brands the opportunity to choose specific messaging and images for different segments of their target audience, as well as to find lookalike audiences which match their existing customer profiles.
Although sometimes the algorithm slips up and delivers ongoing ads for toilet seats after you bought one on Amazon, for example, it’s also the way that you might find out about a concert by a little-known band that you fall in love with.
AI can also build a slippery slope to radicalization
Social media companies long believed that all engagement is good engagement. The most polarized and emotive content drives engagement, so AI algorithms quickly learned to push this content. Brands and media personalities likewise produce “clickbait,” leading to a race for the bottom where the most radical content wins the engagement stakes.
Frances Haugen, a former Facebook employee who claimed whistleblower status, leaked material showing that Facebook is fully aware of the dark pattern of its algorithm. It happily allowed its AI to learn from the worst impulses of the human psyche, nudging users towards the junk food of online content, like white supremacism, anti-vaccine and anti-covid disinformation, and conspiracy theories.
Platforms do this by suggesting friends, groups, and articles which overlap with your existing interests and gradually lead you further along the path to radicalization.
Social media’s AI future is in our hands
But it would be as unfair to blame AI for the damage caused by social media as it would be to blame a hammer when you miss a nail and hit your thumb. AI algorithms are as accurate as the data they are fed, and reflect the biases of the people who build them. That’s why tech watchdogs are demanding that platforms use AI to do more to remove hate speech, conspiracy theories, and disinformation, and detect and delete accounts belonging to underage children.
Social media companies are trying to prove that they understand the need to adjust the way they use AI tools and to apply them more effectively. For example, Instagram could use AI to push positive content that builds teen self-esteem, instead of tearing them down.
Quantum computing is bringing social media to a fork in the road
All this debate is especially important when social media is on the cusp of what could literally be called a quantum leap. Facebook CEO Mark Zuckerberg has received a lot of mockery for his decision to rename the Facebook group as Meta, but it’s an important signal about the future of AI. He talks about an AI-powered “metaverse,” where you can interact with friends, make purchases, and enjoy immersive experiences entirely online.
It’s not yet here, but there’s an expectation that quantum computing will make it a reality soon. Quantum computers are far more powerful than today’s binary computers, and it’s predicted that as they become mainstream, they’ll be able to support more advanced AI applications, like Zuckerberg’s metaverse and more relevant social media community building.
As more eyes turn to the future of social media experiences, quantum computing stocks are gaining momentum. People interested in being part of the disruptive future of social media interactions are feeling encouraged to invest in quantum computing. Signs that social media platforms understand the power of quantum and advanced AI, and will be using it for the good, are likely to add weight to the trend.
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Defiance Quantum ETF:
Investing involves risk. Principal loss is possible. As an ETF, QTUM (the “Fund”) may trade at a premium or discount to NAV. Shares of any ETF are bought and sold at market price (not NAV) and are not individually redeemed from the Fund. The Fund is not actively managed and would not sell a security due to current or projected under performance unless that security is removed from the Index or is required upon a reconstitution of the Index. A portfolio concentrated in a single industry or country, may be subject to a higher degree of risk. The Fund is considered to be non-diversified, so it may invest more of its assets in the securities of a single issuer or a smaller number of issuers. Investments in foreign securities involve certain risks including risk of loss due to foreign currency fluctuations or to political or economic instability. This risk is magnified in emerging markets. Small and mid-cap companies are subject to greater and more unpredictable price changes than securities of large-cap companies.
The value of stocks of information technology companies are particularly vulnerable to rapid changes in technology product cycles, rapid product obsolescence, government regulation and competition. The possible applications of quantum computing and machine learning are only in the exploration stages, and the possibility of returns is uncertain and may not be realized in the near future.
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