What is a news feed

A newsfeed is a list or a stream of posts that appears on the users home pages in most social media applications. A newsfeed features posts from accounts that the users follow. The accounts might belong to friends, organizations or entities that the user shows interest in. There are algorithms that are used to curate a user’s newsfeed based on the topics they are interested in. The posts will be curated in a curtain order and frequency based on the users browsing patterns. The purpose of newsfeeds is to keep the users engaged whilst they are using the app of the website. In the context of facebook, they announced in 2006 a new feature which they called newsfeed, since then the feature has been evolving.

Core components of a newsfeed

The core components of a newsfeed involve key features and functionalities designed to deliver personalized and timely content to users. These components typically aim to optimize user engagement, relevance, and overall experience. Here’s an overview of the main elements:

1. Content Items

• Types of Content: Posts, photos, videos, links, advertisements, and live updates.

• Source: User-generated content, curated content, or promotional content from businesses.

• Metadata: Includes timestamps, author details, engagement metrics (likes, comments, shares), and tags.

2. User Profile and Preferences

• Personal preferences, interests, and browsing history influence the content displayed.

• Behavioral data (likes, shares, clicks) helps tailor the feed to individual users.

3. Ranking Algorithm

• Determines the order and priority of items displayed in the feed.

• Factors include:

• User interaction history.

• Content recency.

• Relevance based on user preferences.

• Engagement metrics (popularity of the post).

4. Real-Time Updates

• Automatically refreshes the feed to show new posts without requiring a page reload.

• Push notifications may alert users of breaking news or updates from following accounts.

5. Interaction Features

• Engagement Options: Buttons for liking, commenting, sharing, saving, or reacting to posts.

• Feedback Mechanisms: “Show me less of this” or “I don’t want to see this” options for refining user preferences.

How does Facebook curate your news feed

Well facebook’s goal is to give you a feed that has mixed content from the things you have shown interest, people you have connected with, people you may not know yet.

Facebook uses machine learning to curate your news feed, they use thousands of models to come up with your final feed. The machine learning algorithms uses data that you give to facebook about you, this data includes how long you take to view a post, did you like or comment or share a post, the speed you are scrolling your feed if you suddenly slow down it means you might be interested in that post. This is to just mention only a few signals Facebook uses to know your interests and dislikes.

How feed ranking works for connected content

1. Inventory

First, they gather your recent inventory — all potential new posts, or posts with new activity, that you could see when you open Facebook. This includes all the posts shared by 1) the people you have connected to as “friends”, 2) the Pages you follow, and 3) the Groups you have joined, and excludes content flagged for violating our community standards.

Signals

Then, for each of these posts, they consider thousands of “signals” to make predictions about what you will find most interesting. Many of these signals are pieces of information that you give us directly when you like or share a post, you connect with a friend or group, or you comment on a Page’s post; others are inferred based on the actions you’ve taken on Facebook. I’ve shared more information about the types of signals used in ranking below.

Predictions

From there, the algorithm uses these signals to make a series of personalized predictions about which content you’ll find most relevant and valuable. For example, the systems predict how likely you are to comment on a post, how likely it is that your friends will comment on the post if you share it, or how likely the post is to spark a conversation or back and forth discussion. They also use surveys to ask people whether a post was “worth your time” and these surveys are used to make predictions about other content you’ll find worthwhile. It also makes predictions about whether a piece of content is problematic and should receive reduced distribution. All of these predictions are combined in the next step to produce the final ordering. We’ve shared more information about the predictions used in ranking below.

Ranking by score

Next, the system calculates a “relevance score” for each post and puts the posts in order based on this score. Generally, posts that the system predicts will provide more value for you are shown higher up in your Feed. The system also tries to ensure that your Feed has a balanced mix of content types. That means, for example, you are less likely to see multiple posts from the same groups or from the same Page in a row; rather, you’ll see a range of posts from different sources.

Once our ranking system has calculated the relevance scores, the second-to-last step they take is to intersperse recommended content: They add this to help you explore and discover more about your interests through other people who share them, regardless of whether you’re already connected. Finally, they also include ads in Feed. Once this process is complete, your personalized Feed is ready!

Challenges Facebook faces when developing its news feed

Developing Facebook’s News Feed involves addressing several significant challenges to ensure a personalized, engaging, and secure user experience. Key challenges include:

1. Personalization at Scale

With over 2 billion users, delivering a unique and relevant News Feed to each individual is a monumental task. Facebook’s machine learning algorithms analyze vast amounts of data to predict and display content that aligns with each user’s interests. This requires sophisticated models capable of processing diverse data points to maintain user engagement.

2. Data Privacy and Security

Handling extensive user data necessitates stringent privacy and security measures. Facebook must comply with global data protection regulations and safeguard user information against breaches. Ensuring transparency in data usage and providing users with control over their data are ongoing priorities.

3. Combating Misinformation

The platform faces the continuous challenge of identifying and mitigating the spread of misinformation. Balancing the promotion of authentic content while respecting free expression involves implementing effective content moderation policies and collaborating with fact-checking organizations.

4. Adapting to Diverse Network Conditions

Users access Facebook from various regions with differing network capabilities. Designing the News Feed to function efficiently across both high-speed and limited connectivity environments ensures a consistent user experience worldwide.

5. Evolving User Expectations

As user behaviors and preferences evolve, Facebook must continuously update the News Feed to meet new demands. This includes integrating new content formats, adjusting algorithms to reflect changing interests, and maintaining a balance between user-generated content and other information sources.

Addressing these challenges requires Facebook to invest in advanced technologies, uphold ethical standards, and engage in ongoing dialogue with users and regulatory bodies to adapt to the dynamic digital landscape.

Making the news feed fault tolerant.

Facebook has implemented several strategies to ensure fault tolerance in its News Feed, allowing it to handle massive user engagement and data processing demands. By analyzing key metrics, we can understand how Facebook maintains the reliability and availability of its News Feed service.

1. Throughput Autoscaling

Facebook’s web tier, which handles HTTP requests, employs throughput autoscaling to dynamically adjust capacity based on real-time demand. This approach ensures that during peak usage times, such as when 100 million concurrent users are active, the system scales up to maintain performance. Conversely, during off-peak periods, resources are scaled down to optimize efficiency. This dynamic scaling is crucial for fault tolerance, as it prevents system overloads and ensures consistent user experience.

2. Distributed Caching with Redis

To manage the vast amount of data processed daily — estimated at approximately 3.0035 petabytes — Facebook utilizes distributed caching mechanisms like Redis. By storing frequently accessed data in memory, Redis reduces latency and alleviates the load on backend databases. This setup not only accelerates data retrieval but also contributes to fault tolerance by providing high-speed access to critical data, even during peak traffic periods.

3. Message Queues and Fanout Services

Facebook employs message queues and fanout services to efficiently broadcast events, such as new posts or interactions, to multiple users simultaneously. This architecture ensures that real-time updates are delivered promptly, maintaining the responsiveness of the News Feed. By decoupling components and handling asynchronous communication, these services enhance fault tolerance, allowing the system to manage diverse workloads and user interactions effectively.

4. Predictive and Reactive Scaling Algorithms

To anticipate and respond to fluctuations in user activity, Facebook combines predictive and reactive scaling algorithms. Predictive algorithms forecast future demand based on historical data, enabling proactive resource allocation. Reactive algorithms adjust capacity in real-time to address unexpected surges in demand. This dual approach ensures that the system remains resilient and maintains performance standards, even under unforeseen conditions.

5. Monitoring and Metrics Analysis

Facebook continuously monitors system health, performance metrics, and usage statistics to optimize performance and troubleshoot issues. Tools like Prometheus and Grafana are utilized for this purpose, providing insights into system behavior and facilitating proactive maintenance. By analyzing metrics such as average time spent on the News Feed per user per day, number of sessions per user per day, and number of interactions per user per day, Facebook can make informed decisions to enhance fault tolerance and overall user satisfaction.

In summary, Facebook’s implementation of fault tolerance in its News Feed is a multifaceted approach that combines dynamic scaling, distributed caching, efficient event broadcasting, predictive analytics, and continuous monitoring. These strategies, supported by robust metrics analysis, enable Facebook to deliver a reliable and responsive News Feed experience to its vast user base.

Conclusion

Facebook’s News Feed design exemplifies a balance between personalization, scalability, and fault tolerance. By leveraging advanced machine learning models, robust infrastructure, and continuous innovation, Facebook has created a system that caters to a global user base while addressing privacy, misinformation, and performance challenges.