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Ayesha Saleem
| August 23

In the ever-evolving landscape of social media, once again our attention is captured with its groundbreaking innovation – ‘Snapchat Dreams,’ a foray into the captivating realm of Generative Artificial Intelligence (AI). This leap forward not only demonstrates Snapchat’s commitment to staying at the forefront of technological advancement but also opens up a world of creative possibilities for its users.

Snapchat Dreams is a new feature that allows users to create and share their own dreamscapes using generative AI. This means that users can create their own avatars, backgrounds, and objects, and then see them come to life in a realistic way. Dreams can be used to express oneself creatively, tell stories, or simply have fun.

Gen Z is known for its creativity and its love of new technologies. Dreams are a perfect platform for these young people to express themselves and explore their imaginations. With Dreams, Gen Z can create anything they can dream of, from fantastical worlds to realistic simulations.

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1. A Glimpse into Dreams: What is Generative AI?

Generative AI, often likened to the creative engine of the digital world, forms the heart of Snapchat’s Dreams. It’s like a wizard’s palette that conjures digital art and content, providing an enthralling merger of technology and imagination.

 

Snapchat Dreams: A creative playground for Gen Z | Data Science Dojo
Generative AI image – Snapchat dreams – source Freepik

 

  • The artistry of AI generation

Generative AI, in essence, is akin to an artist who crafts something new, surprising us at every stroke. This technology enables computers to autonomously produce content, be it images, videos, or even music. Much like a painter’s brush, Generative AI brushes pixels across the canvas of innovation.

  • How “Dreams” came to life

Snapchat’s Dreams is not a mere add-on; it’s a realization of dreams itself. A dedicated team of engineers and artists worked tirelessly to infuse life into this technology. Imagine the blending of an orchestra of algorithms and a symphony of creativity.

2. Unveiling “Dreams”: What makes it captivating?

Snapchat’s Dreams isn’t just a bunch of lines of code; it’s a peek into the future. Here’s what sets it apart:

  • Seamless user experience

Dreams doesn’t require users to have a Ph.D. in computer science. It’s designed with simplicity in mind, making it user-friendly and accessible to everyone, whether you’re a tech-savvy millennial or someone who only recently embraced smartphones.

 

Snapchat-Introduces-Dreams-with-Generative-AI-How-Does-it-Work
Snapchat Introduces Dreams with Generative AI How Does it Work

 

  • Fueling creativity

Remember those art classes where the teacher encouraged you to let your imagination run wild? Dreams is that art class for the digital age. It provides tools and features that amplify your creativity, letting you transform mundane photos into awe-inspiring visual narratives.

  • A new era of personalization

Snapchat understands that personalization is the key to capturing attention in the digital era. With Dreams, you’re not just creating content; you’re crafting experiences that resonate with your audience on a personal level.

 

Read about –> AI driven personalization in Marketing

 

3. Riding the “Dreams” wave: Practical applications

Dreams isn’t just about pixelated dreams; it is about turning the intangible into the tangible. Let’s explore its real-world applications:

  •  Revolutionizing digital marketing

Marketers, hold onto your hats! Dreams offers an innovative channel to engage your audience. Imagine presenting your product through a mesmerizing AI-generated visual story – a story that not only sells but leaves an indelible mark on the viewer’s mind.

  • Redefining social interaction

Snapchat has always been a pioneer in redefining how we connect with others. With Dreams, your snaps aren’t just snapshots; they’re pieces of your imagination that can spark conversations, laughter, and inspiration.

  • Fostering artistic collaboration

Dreams bridge the gap between artists and technology. Collaborative projects can now seamlessly merge the imaginative minds of artists with the computational wizardry of AI, giving rise to artworks that were once confined to the realms of dreams.

4. Ethical considerations: The power and responsibility

As we immerse ourselves in the captivating embrace of Dreams, it’s imperative to consider the ethical facets:

  • Navigating copyright and ownership

While Dreams empower creativity, they also raise questions about intellectual property. Who owns the AI-generated content – the user, the developer, or the AI itself? Snapchat’s ethical compass is being tested as it navigates these uncharted waters.

 

Read more about –> No copyright claim on AI generated art – US court ruling

 

  • The mirage of authenticity

Dreams’ ability to craft convincing content blurs the line between real and synthetic. As consumers, how do we ensure that what we see is indeed rooted in reality? The responsibility to maintain transparency falls on both developers and users.

5. The road ahead: Dreams and beyond

Dreams marks a monumental step, but it’s not the final destination:

  • Continuous evolution

Snapchat’s commitment to innovation means that Dreams is a canvas that will keep evolving. New features, improved algorithms, and enhanced experiences will transform the way we interact with technology.

  • A catalyst for industry-wide transformation

Snapchat’s Dreams isn’t just about Snapchat. It’s a signal to the tech world that generative AI is ready to take center stage, promising to revolutionize industries beyond social media – from entertainment to healthcare.

  •  Unleashing human potential

In the world of Dreams, humans and machines coalesce to create magic. As generative AI amplifies our creativity, it doesn’t replace us – it empowers us to dream bigger, create better, and reimagine reality.

Here are some specific ways that Snapchat Dreams can be used as a creative playground for Gen Z:

  • Storytelling: Dreams can be used to create interactive stories that allow users to explore different worlds and scenarios. This could be a great way for Gen Z to tell their own stories or to experience the stories of others.
  • Art: Dreams can be used to create all sorts of art, from paintings and sculptures to music and movies. This could pave a way for Gen Z to express their creativity and share their unique perspectives.
  • Fashion: Dreams can be used to create custom clothing and accessories for avatars. This could make it possible for Gen Z to experiment with different styles and express their personal identities.
  • Design: Dreams can be used to design furniture, homes, and other objects. This could be a great way for Gen Z to learn about design and create their dream spaces.
  • Gaming: Dreams can be used to create games that are both fun and creative. This could be a feasile way for Gen Z to develop their gaming skills and share their games with others.

 

Learn to build LLM applications                                          

 

Do you have these questions about Snapchat Dreams?

Q1: Is Dreams available to all Snapchat users? A: Yes, Dreams is designed to be accessible to all Snapchat users, regardless of their tech proficiency.

Q2: Can Dreams be used for business marketing? A: Absolutely! Dreams offers a novel way for businesses to engage audiences through captivating AI-generated content.

Q3: Does Dreams work on all devices? A: Currently, Dreams is optimized for smartphones and may have varying functionality on different devices.

Q4: How does Snapchat address AI-generated content ownership? A: Snapchat is actively working on establishing ethical guidelines to address ownership and copyright concerns related to AI-generated content.

Q5: Can Dreams be used for serious artistic endeavors? A: Certainly! Many artists are already exploring Dreams as a tool for pushing the boundaries of their creativity and collaborating with AI.

Data Science Dojo
Melissa Lewis
| April 19

Are you struggling to find top talent in today’s competitive job market? In an increasingly digital world, it’s no surprise that companies are turning to digital recruitment strategies to attract and retain top talent. 

Here’s the twist; simply posting job listings on your website or job boards is no longer enough to stand out. To succeed in today’s fast-paced digital world, you need a recruitment strategy that delivers results. 

That’s why we’ve compiled expert tips and best practices for crafting a winning digital recruitment strategy. From optimizing your job postings for search engines to leveraging social media to reach passive candidates, these tips will help you attract top talent and build a strong employer brand. 

And it doesn’t even matter whether you’re a small business owner or a seasoned HR professional. This guide will equip you with the knowledge and tools you need to succeed in today’s digital landscape. So, let’s dive in and explore the world of digital recruitment strategy together. 

Digital recruitment strategy
Digital recruitment strategy

What is digital recruitment? 

Digital recruitment is the process of using various digital channels and technologies to attract, engage, and hire candidates for job openings. In today’s digital age, traditional recruitment methods such as print ads and job fairs are becoming less effective. 

Companies are turning to digital recruitment strategies to connect with top talent where they spend most of their time – online. According to a recent study, close to 90% of job seekers use their mobile devices to search for jobs, making it essential for companies to have a mobile-friendly recruitment process. 

Digital recruitment strategies can include using social media platforms, job boards, employer review sites, and search engine optimization to attract and engage job seekers. One of the key benefits of a digital recruitment strategy is the ability to reach a wider pool of candidates. 

By leveraging various digital channels, companies can reach passive candidates who may not actively be searching for new opportunities. Additionally, digital recruitment allows for more efficient and cost-effective hiring, with the ability to quickly screen and filter candidates based on skills and experience. 

How do you create a successful digital recruitment strategy? 

Creating a successful digital recruitment strategy requires a deep understanding of your target audience and the ability to leverage the latest digital technologies and channels to connect with them. 

You do not want to miss out on top talent just because your recruitment strategy is not up to par. Here are nine effective tips to help you get started: 

  1. Create an attractive job description

The first step to attracting top talent is to create a compelling job description. Your job description should be detailed and highlight the key skills and experience required for the role. 

It should also be written to appeal to your target audience and showcase your company culture and values. Use clear, concise language, and avoid industry jargon or technical terms that could be confusing to job seekers. It would also help if you included information on compensation and benefits to attract candidates who are a good fit for your company. 

  1. Publish the job advertisement on your website

Your website is one of the most powerful tools for attracting potential candidates. Be sure to publish your job advertisement on your website and make it easy for candidates to apply directly from your site. 

This will help to increase the visibility of your job posting and make it easier for candidates to apply. Make sure your website is mobile-friendly so candidates can easily access and apply for jobs from their smartphones or tablets. 

  1. Ensure that your website is SSL certified

An SSL certificate is essential for any website that collects sensitive data, such as job applications. An SSL certificate ensures that all data transmitted between your website and the user’s browser is encrypted and secure. It is therefore necessary to buy an SSL certificate that can ensure data integrity and data security. 

This can help increase potential candidates’ trust in your company and even better, improve the overall user experience. Make sure that your website has a valid SSL certificate installed, and display the SSL badge prominently on your site to increase confidence in your company’s security measures. 

  1. Optimize your recruitment site for speed

No one likes a slow-loading website. In fact, studies have shown that a one-second delay in page loading time can lead to a 7% decrease in conversions. 

This is why it’s important to optimize your recruitment site for speed. This means ensuring that your website is optimized for fast loading times, especially on mobile devices, which are becoming increasingly popular for job searching. 

Some common suggestions to optimize your recruitment site for speed include compressing images and minimizing code. Also, consider using a content delivery network (CDN) to speed up the delivery of your website’s content. 

  1. Don’t neglect your Google My Business (GMB) profile

Your Google My Business (GMB) profile is important to your digital recruitment strategy. This is especially important if you’re targeting local candidates. 

Your GMB profile provides potential candidates with important information about your business, including your location, website, hours of operation, and reviews. Ensure that your GMB profile is up-to-date and optimized for relevant keywords, to improve your chances of appearing in local search results. 

Click here to read more about “Getting Hired 101 – 7 Things to Follow as a Data Scientist”.

  1. Go for UTM technology

UTM (Urchin Tracking Module) technology is a powerful tool that can help you track the effectiveness of your digital recruitment campaigns. Using UTM parameters in your URLs, you can track how many people are clicking on your job ads. 

You can also track where they are coming from, and which channels drive the most traffic. This can help you optimize recruitment campaigns and allocate resources more effectively. 

  1. Check recruitment metrics

To create a successful digital recruitment strategy, you need to measure the effectiveness of your campaigns. There are several key recruitment metrics that you should track, like the number of applications received, the source of your candidates, and the time-to-hire. 

Tracking these metrics can help you identify important areas for improvement and adjust your strategy accordingly. For example, if you find that most of your candidates come from one job board, you might consider investing more heavily in that channel. Or, if you find that your time-to-hire is longer than the industry average, you may need to reevaluate your hiring process. 

  1. Optimize your website content

Your website content plays a crucial role in attracting potential candidates. Optimize your website content for the right keywords, such as job titles, skills, and qualifications to create a successful digital recruitment strategy. 

This will make it easier for job seekers to find your website when searching for jobs online. Also, your content should be easy to read and understand. 

It should provide a clear value proposition for potential candidates. Consider using video content to showcase your company culture and work environment and feature employee testimonials to give candidates an idea of what it’s like to work at your company. 

  1. Use social media effectively.

Social media platforms, such as LinkedIn, Twitter, and Facebook, can be powerful tools for attracting potential candidates. To use social media effectively, you need to create a strong employer brand and share relevant content that appeals to your target audience. 

You can also use social media to engage with potential candidates by responding to comments and sharing job openings. You can take the help of social media advertising to aim for precise demographics and extent a larger audience. 

Closing thoughts 

A successful digital recruitment strategy requires careful planning and execution. Follow the tips outlined above to create a recruitment strategy that attracts top talent and drives growth. 

Remember to keep track of your metrics and make adjustments as needed to improve your results over time. With the right approach, you should be able to create a digital recruitment strategy that sets your company apart and positions you for long-term success. 

 

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Ahsan Manzoor
| January 2

Billions of users use various social media daily and see a lot of new suggestions there. The content includes text, images, videos, and so on depending on the social platform. Do you know how that content is suggested? 

We will learn about it in this blog.
 

Social media recommendation system: 

It is an algorithm that suggests relevant products to users based on a variety of factors. Sometimes, when you search for a certain product on a website you notice that you start receiving several suggestions of similar products, there is a system behind this. It is generally used to target potential users more efficiently and improve the user experience by suggesting new items, saving users’ time, and narrowing down the set of choices. 

 

Learn about Data Science here

 

Watch the video to see what a recommendation system is and how it is used in various real-world applications. 

 

Introduction to Recommender Systems 

 

 

Now that we know the concept, let’s dive deeper into a real-world application to better comprehend it. 

 

YouTube’s recommendation system journey

YouTube has over 800 million videos, which is about 17,810 years of continuous video watching. It is hard for a user to repeatedly search for certain sorts of videos from millions of videos. This problem is solved by recommendation systems, which provide relevant videos based on what you are currently watching.

The system also works when you open YouTube’s home page and do not watch any videos. In this case, it shows the mixture of the subscribed, most up-to-date, promoted, and most recently watched videos.  

Let’s discuss the journey of the recommendation system on YouTube. 

In 2008, YouTube’s recommendation system ranked videos based on popularity. The issue with this approach was sometimes violent or racy videos get popular. To avoid this, YouTube built classifiers to identify this type of content and avoid recommending them. After a couple of years, YouTube started to incorporate video watch time in its recommendation system.

The reason for this was that users often watched different types of videos and there were different recommendations for them. Later, YouTube took surveys where users rated the watched videos and answered the questions upon giving low or high stars.  

Soon, YouTube’s management realized that everyone did not fill out the survey. So, YouTube trained a machine learning model on completed surveys and predicted the survey responses. YouTube did not stop there; they started to consider the likes/dislikes and share information to make the recommender system better.  

Nowadays, they are also using classifiers to identify authoritative and borderline (doesn’t quite violate community) content to make a better recommender system. 

 

Read more about social media algorithms in this blog

 

Before diving deep into the technical detail, let’s first discuss common types of recommendation systems. 

Classification of recommendation system:  

Recommendation system
Recommendation system

 

These types of recommendation systems are widely used in industry to solve different problems. We will go through these briefly. 

 

1. Content-based recommendation system

 According to the user’s past behavior or explicit feedback, content-based filtering uses item features (such as keywords, categories, etc.) to suggest additional items that are similar to what they already enjoy. 

Content based recommendation system
Content based recommendation system

 

 

2. Collaborative recommendation system 

Collaborative filtering gives information based on interactions and data acquired by the system from other users. It is divided into two types: memory-based, and model-based systems. 

 

a) Memory-based system 

This mechanism is further classified as user-based and item-based filtering. In the user-based approach, recommendations are made based on the user’s preferences that are similar to the preferences of other users. In the item-based approach, recommendations are made based on items similar to other items the active user likes. 

 

Let’s see the below illustration to understand the difference:  

User-based recommendation system
User-based and item-based recommendation system

  

b) Model-based system 

This mechanism provides recommendations by developing machine learning models from users’ ratings. A few commonly used machine learning models are clustering-based, matrix factorization-based, and deep learning models.  

Model-based system
Model-based system

2. Demographic-based recommendation system 

This system provides recommendations based on user demographic attributes, such as age, sex, and location. This system uses demographic information, such as a user’s age, gender, and location, to provide personalized recommendations. This type of system uses data about a user’s characteristics to suggest items that may be of particular interest to them.

For example, a recommendation system might use a user’s age and location to suggest events or activities in the user’s area that might be of interest to someone in their age group.

 

 

3. Knowledge-based recommendation system 

This system offers recommendations based on queries made by the user rather than a user’s rating history. Shortly, it is based on explicit knowledge of the item variety, user preference and suggestion criteria. This strategy is suited for complex domains where products are not acquired frequently, such as houses and automobiles. 

 

4. Community-based recommendation system 

This system provides recommendations based on user-interacted items within a community that shares a common interest. A community-based recommendation system is a tool that uses the interactions and preferences of a group of people with a shared interest to provide personalized recommendations to individual users.

This type of system takes into account the collective experiences and opinions of the community in order to provide personalized recommendations.

 

5. Hybrid recommendation system 

This system is a combination of two or more discussed recommendation systems such as content-based, collaborative-based, and so on. Sometimes a single recommendation system cannot solve an issue, thus we must combine two or more recommendation systems. 

We now have a high-level understanding of the various recommendation systems. Recall the YouTube discussion, what do you think, which recommendation method suits YouTube the most. 

 

It is a memory-based collaborative recommendation system. YouTube can use an item-based approach to suggest videos based on other similar videos using users’ ratings (clicked on and watched videos). To determine the most similar match, we can use matrix factorization. This is a class of collaborative recommendation systems to find the relationship between items’ and users’ entities. However, this approach has numerous limitations, such as  

  • Not being suitable for complex relations in the users and items 
  • Always recommend popular items 
  • Cold start problem (cannot anticipate items and users that we have never encountered in training data) 
  • Can only use limited information (only user IDs and item IDs)  

To address the shortcomings of the matrix factorization method, deep neural networks are designed and used by YouTube. Deep learning is based on artificial neural networks, which enable computers to comprehend and make decisions in the same way that the human brain does.

Let’s watch the video below to gain a better understanding of deep learning.

 

 

YouTube uses the deep learning model for its video recommendation system. They provide users’ watch history and context to the deep neural network. The network then learns from the provided data and uses the softmax classifier (used for multiclass classification) to differentiate among the videos. This model provides hundreds of videos from a pool of over 800 million videos. This procedure was named “candidate generation” by YouTube.  

But we just need to reveal a few of them to a certain user. So, YouTube created a ranking system in which they provide a rank (score) to each of a few hundred videos. They used the same deep learning model that assigns a score to each video for this. The score may be based on the video that the user watched from any channel and/or the most recently watched video topic.

User history and context
User history and context – Source 

Summary 

We studied different recommendation systems that can be used to address various real-world challenges. These systems help to connect people with resources and information that may not have been easily discoverable otherwise, making them a useful tool for solving these challenges.

We discussed the journey of YouTube’s recommendation system, a collaborative system used by YouTube, and examined how YouTube performed well using deep learning in their systems.  

Data Science Dojo
Ayesha Saleem
| September 13

In today’s blog, we will try to understand the working behind social media algorithms and focus on the top 6 social media platforms. Algorithms are a part of machine learning which has also become a key area to measure success of digital marketing; these are written by coders to learn human actions. It specifies the behavior of data by using a mathematical set of rules 

According to the latest data for 2022, users worldwide spend 147 minutes, on average every day on social media. The use of social media is booming with every passing day. We get hooked up on the content of our interest. But you cannot deny that it is often surprising to experience the content we just discussed with our friends or family.  

Social Media algorithms

Social media algorithms sort posts on a user’s feed based on their interest rather than the publishing time. Every content creator desires to get the maximum impressions on their social media postings or their marketing campaigns. That’s where the need to develop quality content comes in. Social media users only experience the content that the algorithms figure out to be most relevant for them.  

1. Insights into Facebook algorithm 

Facebook

Facebook had 2.934 billion monthly active users in July 2022.  

Anna Stepanov, Head of Facebook App Integrity said “News Feed uses personalized ranking, which considers thousands of unique signals to understand what’s most meaningful to you. Our aim isn’t to keep you scrolling on Facebook for hours on end, but to give you an enjoyable experience that you want to return to.” 

On Facebook, which means that the average reach for an organic post is down over 5 percent while the engagement rate is just 0.25 percent which drops to 0.08 percent if you have over 100k followers. 

Facebook’s algorithm is not static, it has evolved over the years with the objective to keep its users engaged with the platform. In 2022, Facebook adopted the idea of showing stories to users instead of news, like before. So, what we see on Facebook is no longer a newsfeed but “feed” only. 

Further, it works mainly on 3 ranking signals: 

  • Interactivity:

The more you interact with the posts from one of your friends or family members, Facebook is going to show you their activities relatively more on your feed.  

  • Interest:

If you like content about cars or automobiles, there’s a high chance Facebook algorithm will push relevant posts to your feed. This happens because we search, like, interact or spend most of our time seeing the content we like.  

  • Impressions:

Viral or popular content becomes a part of everyone’s Facebook. That’s because the Facebook algorithm promotes content that is in general liked by its users. So, you’re also more likely to see what’s everyone talking about today.  

2. How does YouTube algorithm work 

Youtube

There are 2.1 billion monthly active YouTube users worldwide. When you open YouTube, you see multiple streaming options. YouTube says that in 2022, homepages and suggested videos are usually the top sources of traffic for most channels. 

The broad selection is narrowed on the user homepage on the basis of two main types of ranking signals.  

  • Performance:

When a video is uploaded on YouTube, the algorithm evaluates it on the basis of a few key metrics: 

  • Click-through rate 
  • Average view duration 
  • Average percentage viewed 
  • Likes and dislikes 
  • Viewer surveys 

If a video gains good viewership and engagement by the regular followers of the channel, then the YouTube algorithm will offer that video to more users on YouTube.  

  • Personalization:

The second-ranking signal for YouTube is personalization. In case you love watching DIY videos, YouTube algorithm processes to keep you hooked on the platform by suggesting interesting DIY videos to you.  

Personalization works based on a user’s watch history or the channels you subscribed to lately. It tracks your past behavior and figures out your most preferred streaming options.  

Lastly, you must not forget that YouTube acts as a search engine too. So, what you type in the search bar plays a major role in shortlisting the top videos for you.  

3. Instagram algorithm explained  

Instagram

In July 2022, Instagram reached 1.440 billion users around the world according to the global advertising audience reach numbers.  

The main content on Instagram revolves around posts, stories, and reels. Instagram CEO Adam Mosseri said, “We want to make the most of your time, and we believe that using technology [the Instagram algorithm] to personalize your experience is the best way to do that.” 

Let’s shed some light to the Instagram’s top 3 ranking factors for year 2022: 

  • Interactivity:

Every account holder or influencer on Instagram runs after followers. Because that’s the core to getting your content viewed by the users. To get something on our Instagram feed we need to follow other accounts. As much as our interaction with someone’s content occurs, we will be able to see more of their postings.  

  • Interest:

This ranking factor has more influence on reels feed and explore page. The more you show interest in watching a specific type of content and tap on it, the more of that category will be shown to you. And it’s not essential to follow someone to see their postings on reels and explore the page. 

  • Information:

How relevant is the content uploaded on Instagram? This highlights the value of content posted by anyone. If people are talking about it, engaging with it, and sharing it on their stories, you are also going to see it on your feed. 

4. Guide to Pinterest algorithm 

Pinterest

Being the 15th most active social media platform, Pinterest had 433 million monthly active users in July 2022.  

Pinterest is popular amongst audiences who are more likely interested in home décor, aesthetics, food, and style inspirations. This platform carries a slightly different purpose of use than the above-mentioned social media platforms. Therefore, the algorithm works with distinct ranking factors for Pinterest.  

Pinterest algorithm promotes pins having: 

  • High-quality images and visually appealing designs  
  • Proper use of keywords in the pin descriptions so that pins come up in search results. 
  • Increased activity on Pinterest and engagement with other users. 

Needless to mention, the algorithm weighs more for the pins that are similar to a user’s past pins and search activities. 

5. Working process behind LinkedIn algorithm  

LinkedIn

There are 849.6 million users with LinkedIn in July 2022. LinkedIn is a platform for professionals. People use it to build their social networks and have the right connections that can help them succeed in their careers.  

To maintain the authenticity and relevance of connections for professionals, the LinkedIn algorithm processes billions of posts per day to keep the platform valuable for its users. LinkedIn’s ranking factors are mainly these: 

  • Spam:

LinkedIn considers post as spam if it contains a lot of links, has multiple grammatical errors, and consists of bad vocabulary. Also, avoid using hashtags like #comment, #like, or #follow can flag the system, too. 

  • Low-quality posts:

There are billions of posts uploaded on LinkedIn every day. The algorithm works to filter out the best for users to engage with. Low-quality posts are not spam but they lack value as compared to other posts. It is evaluated based on the engagement a post receives. 

  • High-quality content:

You wonder what’s the criteria to create high-quality posts on LinkedIn? Here are some tips to remember: 

Easy to read posts 

Encourages responses with a question 

Uses three or fewer hashtags 

Incorporates strong keywords 

Tag responsive people to the post 

Moreover, LinkedIn appreciates consistency in posts, so it’s recommended to keep your followers engaged not only with informative posts but also conversing with users in the comments section.  

6. A sneak peek at the TikTok algorithm 

TikTok

TikTok will have 750 million monthly users worldwide in 2022. In the past couple of years, this social media platform has gained popularity for all the right reasons. The TikTok algorithm is considered as a recommendation system for its users.  

We have found one great explanation of TikTok “For You” page algorithm by the platform itself: 

“A stream of videos curated to your interests, making it easy to find content and creators you love … powered by a recommendation system that delivers content to each user that is likely to be of interest to that particular user.” 

Key ranking factors for the TikTok algorithm are: 

  • User interactions:

This factor is like the Instagram algorithm, but mainly concerns the following actions of users: 

Which accounts do you follow 

Comments you’ve posted 

Videos you’ve reported as inappropriate 

Longer videos you watch all the way to the end (aka video completion rate) 

Content you create on your own account 

Creators you’ve hidden 

Videos you’ve liked or shared on the app 

Videos you’ve added to your favorites 

Videos you’ve marked as “Not Interested” 

Interests you’ve expressed by interacting with organic content and ads 

  • Video information: 

Videos with missing information, incorrect captions, titles, and tags are buried under hundreds of videos being uploaded on TikTok every minute. On the discover tab, your video information signals tend to seek for: 

Captions 

Sounds 

Hashtags* 

Effects 

Trending topics

  • TikTok account settings:

TikTok algorithm optimizes the audience for your video based on the options you selected while creating your account. Some of the device and account settings that decide audience for your videos are: 

Language preference 

Country setting (you may be more likely to see content from people in your own country) 

Type of mobile device 

Categories of interest you selected as a new user 

Social media algorithms relation with content quality 

Apart from all the key ranking factors for each platform, we discussed in this blog, one thing remains ascertain for all i.e., maintain content quality. Every social media platform is algorithm bsed which means it only filters out the best quality content for visitors. 

No matter which platform you focus on growing your business or your social network, it highly relies on the meaningful content you provide your connections.  

If we missed your favorite social media platform, don’t worry, let us know in the comments and we will share its algorithm in the next blog.  

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