Have you ever wondered why your social media feeds seem to know you better than you know yourself? In a world where every click, like, and share is monitored, AI algorithms are constantly shaping our online experiences. Artificial intelligence knows how to learn what we like and don’t like, ultimately allowing it to reinforce our preferences online. These systems make the behind-the-scenes decisions that determine every post you see in your feed, which comes with surprisingly serious implications.
Let’s explore how AI algorithms work to understand us and then use that information to influence what we see online.
These detection methods help AI identify and understand user preferences.
A user interaction occurs every time a user takes an action on social media in direct response to content. AI learning models use information from these interactions to better understand what attracts engagement.
Examples of Interactions Include:
When social media users set up their profiles and engage with the platform, they often share specific inputs. Some of this information will come from the questions that arise when setting up a profile. Others come from search queries and other user actions. Each piece of information helps the system learn more.
Information like your age or the latest trend you looked up will all factor into what you see in your feed.
For the average social media user, behaviors happen naturally. More often than not, users will not even realize they have specific behaviors when using social media. How much thought do you put in when scrolling, liking, and sharing?
Artificial intelligence observes how we behave in real-time. In fact, behavioral data helps AI systems to better understand engagement and preferences on an individual basis and across communities.
Examples of Behaviors Include:
These top machine-learning models influence digital spaces daily. Are they influencing you?
When it comes to looking to the future, predictive analytics play a leading role. These data-backed developments rely on monitoring past user behavior. Based on this information, the machine learning models begin to make informed guesses regarding what the user will do or what kind of content they will enjoy.
If you’re always liking videos of adorable animals, AI knows to give you more!
Language defines cultures, and it defines subcultures as well. For this reason, platforms use artificial intelligence to better understand how users interact with textural content. Using NLP, AI systems explore the types of language users interact with and find other content that aligns with these interests.
Have you ever noticed that watching videos of text posts often leads the algorithm to share more content that touches on those same topics?
Social media is a multimedia game, and images and videos bring it to life. While these pieces of content attract new users, they also provide a perfect reference point for community analysis. Using object recognition, AI learns more than you might think about different types of content.
When analyzing images and videos on a platform, artificial intelligence identifies clear themes and cross-references that information with user habits. This creates a comprehensive understanding of the patterns in content pieces that have high engagement.
Every user is unique, but user preferences often come in trends. Using AI algorithms that focus on user reviews and information and then applying that to other similar user profiles helps provide tailored content for a better user experience. By aligning users with similar preferences, this approach adds more significance to top trends and helps people with similar profiles and behaviors find content they love.
Tailored to you or limiting what you see? AI reinforces user preferences and limits exposure to certain viewpoints.
The big difference between social media platforms when they first started and the social media platforms of today is the introduction of content personalization. While users used to have to deal with seeing whatever content was recent or trending, today’s user experience focuses on personalization.
With personalization, users see content that they want to see more often than not. Through the acts of identifying preferences and lifting up information from relevant communities, feeds give users what they want to see, even as their moods change.
If you have ever felt like purchase recommendations can read your mind, you know this goes well beyond the kind of content you see. Targeted advertising lets brands lock onto your preferences to market products to you directly.
Modern social media platforms create feedback loops using engagement metrics and reinforcement learning. In feeds, content that receives more engagement also receives more views, making algorithms favor certain content pieces over others.
As the algorithm observes the preferences and reactions of users, it will adapt to maximize engagement based on the information it collects.
Given the tendency for artificial intelligence to highlight certain pieces of content for specific user groups, two clear risks arise — the creation of echo chambers and filter bubbles.
These problems happen when users engage with limited content that reinforces their beliefs and preferences. While this is not always bad, it may limit exposure to diverse perspectives and perpetuate misinformation sharing.
At the end of the day, no one should only see views they personally agree with. Society needs larger discussions with multiple viewpoints.
Creating more ethical online spaces requires platforms and users to work together to improve information sharing and access.
Social media impacts individuals, groups, and belief systems. As these realizations come to light, ethical considerations become more important than ever before. Both businesses and users must understand the potential for reinforcing bias, as well as the potential for creating polarized online communities.
Underneath these issues, another concern lurks — user privacy. As social media platforms use AI to create feeds based on user profiles, users give up personal insights into themselves and their preferences. For this reason, we must think critically about data collection and user profiling to create more ethical AI. Already, privacy concerns in social media lead to important discussions every day.
In some ways, AI may know more about your tendencies than you do–and this can be very dangerous.
Social media platforms and users alike must acknowledge the risks that algorithms pose for the general public. There needs to be a clear understanding of how these algorithms work, as well as their impact on user behaviors online and in the real world.
To create truly ethical AI, we must push back against unintentional discrimination and misinformation sharing.
Empowering users to manage their data and understand the implications of these data profiles provides an excellent approach to creating more ethical AI. Since AI algorithms play a pivotal role in shaping the user experience, we all must think more critically about what we reinforce through our user preferences.
Educated users are users who can help to improve these algorithms and create stronger and more diverse online communities.
What does the future of AI and social media mean to you? Share your thoughts on how we can create better digital spaces below!
