Algorithmic curation

A feed of posts curated for a user on the Mastodon social networ

Algorithm curation is the selection of online media by recommender system and personalized search. Traditionally, curation has been the work of museum and library specialists, carefully selecting relevant materials to develop collections. Today, curation takes the form of selectively sharing content online, creating and maintaining a profile on any of the various social networking platforms, and searching and compiling information for reporting. Curation algorithms leverage this task by implementing different filter approaches such as collaborative filtering and content-based filtering as well as builds off of existing technologies like a recommender system and personalize search. Examples include search engine and social media products such as the Twitter feed, Facebook’s News Feed, and the Google Personalized Search.

History

Early algorithmic curations

Algorithmic curation plays an important role in how people connect online today. There’s so much information and data we see online that deciding what to acknowledge, such as save, archive, share, or ignore, has become a lot of work.Therefore, platforms use newsfeed algorithms to decide what to show each user. These algorithms are complicated, so it is not easy to know how they shape communication.

Information overload

Early on, platforms needed a way to filter information so that users wouldn't get overwhelmed. This led to the first-generation ranking algorithms showing the most recent or most popular posts.Second-generation demonstrated algorithms that curate content to keep people hooked onto the platform for a longer timespan.Algorithmic curation doesn't just sort content, but it also shapes knowledge, attention, and political exposure. In shaping what people view on a day-to-day basis, this gives reasons to the algorithm acting as a powerful gatekeeper and deciding what new material people are exposed to.

How algorithm changes users' feeds over time

Algorithm curation increased source diversity and also reduced the number of external links, which limited access to outside information. Topics based on political content were made more relevant than some COVID-19 health information, which was subdued.Using agent-based modeling, researchers understand the problems within these systems. They come upon the adversities and motives of the algorithms that try to increase user engagement while making misinformation and polarization worse. The target is to discover how user behavior, information, and algorithms all influence each other. Therefore, as a response to information overload, algorithmic curation served as a response to the massive amount of content on social media.

Emergence of AI

With AI driving systems, Newsfeed now predicts, personalizes, and optimizes information, which are core AI functions. These attributes change human perception and behavior by changing what they see, share, and think. Now, researchers have adapted to models of computational simulation to understand how AI-driven curation shapes social outcomes at scale. Other platforms like Twitter have moved to simple chronological feeds. They now use complex, AI-powered ranking systems that personalize information.

Algorithmic curation then evolved onto AI-powered systems focused on developing, which purposefully raises polarization. Now, due to advanced scales and operations, emerging research uses advanced modeling tools to keep up with these AI systems that humans cannot understand. Platforms such as Twitter have moved away from a sequential feed. They switched to AI-powered computational systems to compose personalized information. These systems make decisions that aren't realistic for humans to make.

Approach

Filter types

Collaborative filtering

Collaborative filtering (CF) methods create recommendations based on a person’s usage patterns. CF predicts a person’s desire for an item by matching their interests with people who have similar interests. This process allows for the sharing of ratings between like-minded people. CF is based on human and not machine analysis of content. Users of CF systems (found commonly in social media) rate items that they have interacted with; this rating creates a profile of interests. The CF system then matches that user with other people with similar interests. Once matched, the ratings from those similar users are then used to generate recommendations for the user. The main advantage of collaborative filtering includes the ability to filter by various types of content such as text, art, work, music, and mutual funds; it filter[s] based on complex and hard to represent concepts, such as taste and quality.

Content-base filtering

Another popular recommendation system implementation is content-based filtering (CBF). In CBF, a user profile is built to provide information about the types of items that the user likes. This is based on keywords used to describe the items. In this approach, a recommendation is made by presenting similar items to what the user liked in the past (or items that are similar to what the user is looking for). The CBF method creates a profile for each item based on a set of discrete attributes and features. The system then creates a content-based profile for the user based on a weighted vector of item features. This is made from items the user has previously rated or purchased, or from items the user is currently interested in, or presently viewing. The weights represent the importance of each feature to the user. There are various possible ways of computing these weights, such as Bayesian Classifiers, cluster analysis, decision trees, and artificial neural networks. Regardless of the calculation technique, the goal of the weighted vector is the same: to determine the probability that the user will like a suggested item. One example of content-based filtering to help describe this process is Pandora Radio. When a user visits Pandora, they are prompted to enter artist, genre, or composer to create a station. Pandora then uses CBF to find music with similar attributes to the song, artist, or genre that the user provides.

Technology

Recommender system

An algorithm is curated in part by recommender systems that rank and suggest the most relevant content to users. Content is ranked according to a particular user’s implicit and explicit input. Implicit rankings include elapsed time viewing or engaging with a specific item. The user’s liked items (posts, store pages, articles) and shared items make up the explicit data used by algorithms to recommend content.

Personalized search

By utilizing external factors outside the user’s explicit query, personalized search aims to retrieve results most relevant to the user. The user’s past queries, history, and interests create an additional context that refines the algorithm’s output. [new] Platforms such as X (formerly Twitter) and Bluesky give users recommendations from similar users and the content they interact with. Additionally, personalized search offers users to explicitly filter search results by giving them the option to block content containing certain phrases or hashtags from being recommended. Other types of commercial websites such as search engines and retailers use similar processes to prescribe tailored information and products to a distinct user.

Media

Social media

Algorithmic curation in social media boils down to automated systems that pick, rank, and serve content based on how users interact and what they seem to prefer. Major platforms like Facebook and Twitter lean on these algorithms to craft personalized feeds, trying to boost engagement by tracking behaviors such as likes, shares, and comments. For example, Facebook's News Feed pushes content that matches users' interests, which can lock in their existing civic views through selective exposure. While this does keep users around longer, it also breeds filter bubbles where they only see familiar takes, further polarizing them.

Game theory research can demonstrate how curation algorithms shape network connections and content quality as complements rather than as independent attributes. Users become less picky and forge more ties, but creators lose out to increased competition. Some algorithms, especially those cheap to scale, crank up polarization and feed those bubbles. Others, focused on vertical quality, might ease the problem by mixing in diverse voices. Music streaming platforms run into similar issues: even with personalization, users find algorithmic recommendations a bit cold, which shapes how they listen and what they discover.

As misinformation spread becomes easier, divergent perspectives get harder to find. Empirical evidence shows how users actively filter out opposing views by blocking content, a habit that works with algorithms to cement echo chambers. Regulatory fixes like modifying systems to reduce sensationalism can scatter clickbait, but they would also expose these systems more than would be desired. Current scholarship maintains focus on ethical designs that balance engagement with genuine diversity in information.

AI's contribution

Artificial intelligence now plays a far more prominent role in algorithmic curation, drawing on machine-learning models that can handle vast stores of data to shape content dynamically. Methods such as deep learning and reinforcement learning allow these systems to anticipate user preferences with greater precision than older techniques, making it possible for platforms to adjust what users see almost instantaneously. In the context of social media and streaming services, this means AI arranges feeds to highlight what it deems relevant and carries bias from the training data.

A game-theory framework illustrates how AI-driven curation on platforms encourages clickbait production. When users interact in a competitive environment where algorithms reward engagement-maximizing content, they are highly incentivized to get as much attention as possible through any means. Another study on music consumption differentiates human roles and algorithmic ones, demonstrating that while AI recommendations reduce diversity by increasing repetition, hybrid approaches yield better outcomes.

Ethical concerns range from agency hacking to wider issues of polarization and discrimination within systems. A normative framework evaluates AI curation at both individual levels, emphasizing relevance and accuracy, and broader societal levels, urging governance that protects values such as civic health. As AI moves further into generative forms, combined models that pair computational speed with human judgment hold promise for greater diversity, but they also require transparency and regulatory measures to keep associated risks in check.

See also

• Algorithmic radicalization
• Ambient awareness
• Influence-for-hire
• Social bot
• Social data revolution
• Social influence bias
• Social media bias
• Social media intelligence
• Social profiling
• Virtual collective consciousness