fbpx

Instagram explains how the algorithm for its Reels recommendation system works.

Instagram explains how it ranks the content people see when browsing through Reels. This insight may help you with creating more successful clips.

Instagram is pushing Reels as its next flagship feature. Adam Mosseri, head of Instagram, has gone on record saying he wants to go big on video in an effort to compete directly with TikTok.

As Instagram continues to invest in Reels it’s keeping users informed about how this section of the app works.

In an Instagram post, the company reveals how it chooses which Reels are recommended to each individual user.

If Instagram is part of your marketing strategy, learning how the app recommends Reels to users is the knowledge that can assist you in the future.

Here are the key takeaways.

How Instagram Recommends Reels to Users

The goal of Instagram’s Reels algorithm is to surface content users will not only enjoy watching, they’ll likely engage with it as well.

In order to determine which Reels to show users, Instagram’s algorithm considers how likely an individual is to:

  • Watch a Reel the whole way through
  • Like it
  • Say it was entertaining or funny
  • Go to the audio page to make their own Reel

That last point might sound confusing if you’re not familiar with either Reels or TikTok. It refers to the ability to take an audio track from someone’s video and create your own content with it.

Unless the creator has the feature disabled, each Reel has a page where viewers can grab the audio and make a new video with the same track.

Creating a Reel with a highly shareable sound clip can get you far with the recommendation algorithm — but is it more important than likes and view count?

Most Important Reels Algorithm Signals

Instagram says user activity is the most important signal when it comes to recommending Reels.

The algorithm considers which Reels a user has engaged with in the past, and whether they’ve had any direct interaction with the content creator.

That means responding to comments, DMs, and tags can help get your content shown in peoples’ feeds more often.

After that, Instagram looks at information about the video itself and information about the content creator.

The most important signals for the Reels recommendation algorithm are (in order of importance):

  • User activity: Including recent engagement with Reels and interactions with content creators.
  • Information about the Reel: Such as its popularity, its audio track, and understanding of the video based on pixels and whole frames.
  • Information about the creator: Including who they are and how other users have interacted with them.

Types of Content Instagram Won’t Recommend

There are several types of content Instagram won’t recommend regardless of how popular the creator is or how much engagement the video receives.

Instagram avoids recommending Reels for the following reasons:

  • The video is low resolution and/or watermark.
  • The video contains political content.
  • The video was made by political or government figures.

If you want to get anywhere with Instagram Reels, aim for producing high-quality and original content. Watermarked videos recycled from other sites will not get surfaced in peoples’ feeds unless they follow the creator directly.

Lastly, keep the subject matter light and friendly for all audiences.

[Source: This article was published in searchenginejournal.com By Matt Southern - Uploaded by the Association Member: Jay Harris]
Categorized in How to

Is FLoC switching from cohorts to topics?

With the rollout of FLoC delayed until 2023, there may be an indication that Google is adjusting how the privacy-focused ad-targeting system may work.

“A lead engineer helping guide Google’s Privacy Sandbox development has revealed signs of what may be next for the firm’s most advanced cookieless ad targeting method. The potential update of the Federated Learning of Cohorts targeting technique detailed at a recent engineering research event would involve assigning topic categories to websites and people rather than assigning opaque numerical cohort IDs to them,” wrote Kate Kaye with Digiday.

This may be a response to evidence that the previous method of FLoC (which did not pass muster with GDPR) might enable fingerprinting, which means bad actors could still track individuals — something FLoC is expressly created to prohibit. “Topics have a number of advantages over cohorts. Users can see what’s being said about them and understand it,” said Josh Karlin, a tech lead manager of Google’s Privacy Sandbox team in its Chrome browser division at an Internet Engineering Task Force meeting. 

“We are always exploring options for how to make the Privacy Sandbox proposals more private while still supporting the free and open web. Nothing has been decided yet,” a Google spokesperson told Search Engine Land.

Why we care. While Google is buying itself more time (testing for the latest version of FLoC ended July 13 and it’s taking feedback from the advertiser community into consideration too), this pivot could potentially be better for everyone involved. “Adopting a topic-based approach could give advertisers, ad-tech firms, website publishers, and people a clearer understanding of how ads are targeted through the technique,” said Kaye. 


The SEO Periodic Table: HTML success factors

These elements encompass the HTML tags you should use to send clues to search engines about your content and enable that content to render quickly. Are you describing movie showtimes? Do you have ratings and reviews on your e-commerce pages? What’s the headline of the article you’ve published? In every case, there’s a way to communicate this with HTML. 

Search engines look for familiar formatting elements like Titles (Tt) and Headings (Hd) to determine what your page’s content is about, figuring that these cues to human readers will work just as well for them. But search engines also utilize particular fields like Schema (Sc) markup and Meta Descriptions (Ds) as clues to the meaning and purpose of the page.

 As Google has removed the AMP requirement, we’ve gotten rid of that element and added two new ones: Image ALT (ALT) and Content Shift (CLS). ALT text for images improves accessibility and image SEO. Screen readers use ALT text to help those with visual disabilities understand the images on the page. ALT text for images can also help with image search — surfacing your site in image search results. Content Shift (CLS) focuses on the elements of visual stability. 

Cumulative Layout Shift, which is part of the Core Web Vitals and overall page experience update, refers to unexpected changes in a page’s layout as it loads — it’s annoying for users at a minimum and can cause real damage depending on the severity of the shift and content of the page.

Read more about the HTML success factors or download the whole SEO Periodic Table.


Search Shorts: Get more GMB photos, remote working SEOs and automation advice

Google My Business ‘Photo Updates’: A new way to get customer pics. Another solid local SEO piece by one of our faves, Claire Carlile. “It is now possible to add a photo update without leaving a review if you click… on ‘Add a photo update.’”

Remote forever? Kelvin Newman asked his SEO and digital marketing Twitter followers if they were back in the office yet. Over 60% said no (with 19% saying they’d always been remote). Many replies and QTs expect that trend to stay for a while. 

“Definitely don’t do this.” That’s what Kenny Hyder said in response to a Google Ads tweet about Smart Bidding. Just another case of ads automation vs. ads consultant.


What We’re Reading: Reddit’s new round of funding will go toward driving new users and expanding advertiser options

Reddit announced that it raised $140 million in venture capital which increased the company’s valuation from $6 billion to $10 billion. While initially not planned, the fresh capital gives Reddit more time to figure out how to IPO eventually.

“The company makes most of its money selling advertising, which appears in the feeds of users who browse the many ‘subreddits,’ or topic-focused forums, across the site,” said Mike Issac for The New York Times. But this also means “Reddit must compete against digital advertising giants like Google, Facebook, and Amazon, as well as other ad-based social networking sites, including Twitter, Snap, and Pinterest.”

But the company has been steadily improving its metrics, according to the NYT article: 

  • Reddit surpassed $100 million in revenue in a single quarter for the first time this year, up 192 percent over the same period in 2020.
  • More than 50 million people now visit Reddit daily.
  • The site has more than 100,000 active subreddits.

The company has also been working on moderating subs recently, as well, including banning ‘The_Donald’ and other subreddits that degraded into forums of hate speech and violent conspiracy theories. Many of the other major players competing in the space (Facebook, Twitter) have been trying to do the same.

So what’s next for the cash? The latest round of money means that the forum/social media platform can figure out new ways to garner more users and continue to build its business, especially internationally. Plus they plan to explore more options for video ads and opening their system up to be easier for small businesses looking to take advantage of the niche and targeted advertising. 

[Source: This article was published in searchengineland.com By Carolyn Lyden - Uploaded by the Association Member: Daniel K. Henry] 
Categorized in Search Engine

Looking to get a better understanding of the Instagram algorithm, and how it decides what content to show each individual user - and how you can use that to your own advantage?

You're in luck - this week, as part of its Creator Week event, Instagram is providing some extra insight into its internal processes via series of explainers, with the first focused on the infamous feed algorithm, and how it actually dictates content reach in the app.

As explained by Instagram:

"We want to do a better job of explaining how Instagram works. There are a lot of misconceptions out there, and we recognize that we can do more to help people understand what we do. Today, we’re sharing the first in a series of posts that will shed more light on how Instagram’s technology works and how it impacts the experiences that people have across the app."

The post covers a range of key elements that can help to facilitate more understanding and improve your planning in the app. Here's a look at the key points.

There's no one, all-encompassing algorithm

Instagram first notes that its processes are not defined by a single algorithm, so the idea of 'the algorithm' as such is slightly flawed.

"Instagram doesn’t have one algorithm that oversees what people do and don’t see on the app. We use a variety of algorithms, classifiers, and processes, each with its own purpose. We want to make the most of your time, and we believe that using technology to personalize your experience is the best way to do that."

Instagram explains that, like Facebook, it implemented an algorithm because the flow of content became too much for each user to navigate.

"By 2016, people were missing 70% of all their posts in Feed, including almost half of posts from their close connections. So we developed and introduced a Feed that ranked posts based on what you care about most."

This is why the focus of its feed and Stories algorithms is generally on friends, while Explore and Reels look to uncover more relevant topics based on trends, interests, etc.

Key signals

Instagram says that its algorithms all use key signals, with those signals varying dependent on each element.

Instagram notes that there are "thousands" of signals that its systems can draw from, but for the most part, the main indicators across Feed and Stories, in order of importance, are:

  • Information about the post - These are signals both about how popular a post is – think how many people have liked it – and more mundane information about the content itself, like when it was posted, how long it is if it’s a video, and what location, if any, was attached to it.
  • Information about the person who posted - This helps us get a sense of how interesting the person might be to you, and includes signals like how many times people have interacted with that person in the past few weeks.
  • Your activity - This helps us understand what you might be interested in and includes signals such as how many posts you’ve liked.
  • Your history of interacting with someone - This gives us a sense of how interested you are generally in seeing posts from a particular person. An example is whether or not you comment on each other’s posts.

These are the general algorithm identifiers, similar to Facebook's News Feed, with the key elements being what types of posts you engage with and your relationship to the creator of each.

If you engage with video more often, you'll see more video, if the post is getting a lot of engagement, you're more likely to see it, if you tap Like on a certain post, that's a strong indicator of interest, etc.

Worth noting here that these elements apply to both the main feed and your stories, so if you're looking to maximize reach in these surfaces, these are the key elements that you need to focus on.

Furthering this, Instagram also notes that the feed ranking will also be based on each users' engagement history:

"In Feed, the five interactions we look at most closely are how likely you are to spend a few seconds on a post, comment on it, like it, save it, and tap on the profile photo. The more likely you are to take an action, and the more heavily we weigh that action, the higher up you’ll see the post."

Again, it comes down to incentivizing action - how can you maximize the appeal of your content to prompt these types of responses? That will help ensure more of your posts get priority for each user.

Ranking Explore

Instagram's discovery tab is a little different, with the Explore algorithm focused on showing you other content that you may like, based on who you follow and your engagement history.

"To find photos and videos you might be interested in, we look at signals like what posts you've liked, saved, and commented on in the past. Let’s say you’ve recently liked a number of photos from San Francisco’s dumpling chef Cathay Bi. We then look at who else likes Cathay’s photos, and then what other accounts those people are interested in. Maybe people who like Cathay are also into the SF dim sum spot Dragon Beaux. In that case, the next time you open Explore, we might show you a photo or video from Dragon Beaux. In practice, this means that if you’re interested in dumplings you might see posts about related topics, like gyoza and dim sum, without us necessarily understanding what each post is about."

So the idea here is that the algorithm will look to showcase content to related groups of people based on clusters - if you're regularly engaging with a profile that shares fishing content, then it's likely that other people who engage with the same are also looking at other fishing accounts, which you may also be interested in.

This is where hashtags can help improve discovery, by getting your account in front of people searching for certain topics. If they then engage with your posts, that increases your chances of being shown to their connections, and so on.

Like Feed and Stories, Instagram ranks the Explore listing based on how likely each user is to engage with each post.

"Once we’ve found a group of photos and videos you might be interested in, we then order them by how interested we think you are in each one, much like how we rank Feed and Stories. The best way to guess how interested you are in something is to predict how likely you are to do something with the post. The most important actions we predict in Explore include likes, saves, and shares."

Saves have become a more important consideration more recently, with some noting that Saves have more weight in algorithm distribution, which may or may not be correct. But certainly, it's an element that Instagram is now specifically noting, so it is worth considering how you can incentivize saves of your posts, as this can play a part in improving Explore exposure.

It's worth also noting too, that while the Explore feed is also ranked based on personal engagement elements (the types of post a user has engaged with, relationship with an account, etc.), how popular a post is, based on broader engagement signals, is a much bigger consideration in Explore, and will see content get more exposure in the Explore feed.

Ranking Reels

Instagram's latest algorithm-defined element is its TikTok-like Reels, for which it says the algorithm is "specifically focused on what might entertain you."

"We survey people and ask whether they find a particular reel entertaining or funny, and learn from the feedback to get better at working out what will entertain people, with an eye towards smaller creators. The most important predictions we make are how likely you are to watch a reel all the way through, like it, say it was entertaining or funny, and go to the audio page (a proxy for whether or not you might be inspired to make your own reel.)"

TikTok has almost perfected the most engaging version of the short video algorithm, with its system taking in the exact right signals to show you a constant stream of content that you can't help but keep scrolling through, based on trends, creators, the content of each clip, etc.

Instagram is now working to catch up, and anecdotally, it is improving, with its Reels display hooking into similar elements to make it a more sticky, engaging proposition for users who tap into the Reels feed.

For Reels, Instagram says that these are the four key elements of focus in its algorithm:

  • Your activity - We look at things like which reels you’ve liked, commented on, and engaged with recently. These signals help us to understand what content might be relevant to you.
  • Your history of interacting with the person who posted - Like in Explore, it’s likely the video was made by someone you’ve never heard of, but if you have interacted with them that gives us a sense of how interested you might be in what they shared.
  • Information about the reel - These are signals about the content within the video such as the audio track, video understanding based on pixels and whole frames, as well as popularity.
  • Information about the person who posted - We consider popularity to help find compelling content from a wide array of people and give everyone a chance to find their audience.

So content and creator popularity, overall, is a bigger factor for Reels, while it's also worth noting that Instagram will restrict the reach of Reels that include a TikTok watermark or similar, which it says is designed to improve the user experience (i.e. people criticized Reels as simply being a re-hashed feed of TikTok clips, so it now looks to stop such re-sharing).

These are some helpful pointers as to how Instagram's various algorithms work, and how it looks to showcase certain content to users - and what each creator should be focused on to improve their reach. Essentially, it comes down to audience understanding - doubling down on what works, and dropping what people don't respond to - in order to maximize these key elements, and boost engagement, first with your followers, then subsequently with wider audiences.

Some important notes to factor into your IG planning. You can read Instagram's full algorithm explainer, which also includes notes on Shadowbanning, here.

[Source: This article was published in socialmediatoday.com By Andrew Hutchinson - Uploaded by the Association Member: Dorothy Allen]

Categorized in Social

DuckDuckGo has launched a new browser extension for Chrome that will prevent FLoC, a new tracking technique used by Google to support web advertising without identifying users.

Privacy browser DuckDuckGo has launched a new extension for Chrome that's designed to block Google's new algorithm for tracking users' browsing activity for ad selection.

DuckDuckGo's new browser extension blocks FLoC (Federated Learning of Cohorts), which Google introduced to users in March as a replacement for third-party cookies that track individuals across the web.

FLoC is proposed as a method for offering greater anonymity for users by concealing their browsing activity within a group (or 'cohort') of other anonymized users with similar browsing habits. In doing so, advertisers can offer up relevant ads to cohorts of several thousands of users with similar interests, while the identity of individual users remains hidden.

But some see problems with this proposal. While the idea of 'hiding' individuals within a group sounds like better news for user privacy, websites can still target users with ads based on their assigned 'FloC ID', which essentially offers up a summary of interests and demographic information based on a user's browsing habits. What's more, websites can theoretically still track individuals, owing to the fact that every time you visit a website, it records your IP address.

This is where DuckDuckGo's new tool comes in. Currently, FLoC is only being used within Google Chrome, and while it has not yet been rolled out en masse, Google has announced plans to begin trialing FLoC-based cohorts with advertisers starting in Q2.

The FLoC-blocking feature is included in version 2021.4.8 and newer of the DuckDuckGo extension. DuckDuckGo Search is now also configured to opt out of FLoC.

"We're disappointed that, despite the many publicly voiced concerns with FLoC that have not yet been addressed, Google is already forcing FLoC upon users without explicitly asking them to opt-in," DuckDuckGo said in a blog post.

"We're nevertheless committed and will continue to do our part to deliver on our vision of raising the standard of trust online."

Google's Privacy Sandbox

Google has been working on a replacement for third-party cookies for some time. As detailed in a post on its Chromium Blog in January this year, FLoCs are one of a handful of methods the search giant is looking at as part of its 'Privacy Sandbox' for the web.

The company has claimed that FLoC algorithms are at least 95% as effective as cookie-based advertising when it comes to helping advertisers target users, which it says is "great news for users, publishers, and advertisers".

Chetna Bindra, Google's Group Product Manager for User Trust and Privacy, suggested in a blog post in January that tools like FLoC and other privacy-preserving methods proposed as part of Google's Privacy Sandbox would enhance fraud protection and prevent 'fingerprinting', whereby data from a user's browser is gathered to create a profile.

Bindra labeled FLoC a "privacy-first alternative to third-party cookies" that "effectively hides individuals 'in the crowd' and uses on-device processing to keep a person's web history private on the browser."

Yet others have pointed out that FLoC doesn't eliminate the threat of fingerprinting entirely. As well as the possibility of websites identifying users through a combination of their cohort ID and IP address, cohort IDs will also be accessible by any third-party trackers within the websites that users visit.

Google has said that it will work to ensure that "sensitive interest categories" like religion, identity, sexual interests, race, and medical or personal issues can't be used to target ads to users or to promote advertisers' products or services.

The Electronic Frontier Foundation (EFF), a digital rights group, argues that these precautions don't go far enough. "The proposal rests on the assumption that people in 'sensitive categories' will visit specific 'sensitive' websites, and that people who aren't in those groups will not visit said sites," it said in a blog post.

"But behavior correlates with demographics in unintuitive ways. It's highly likely that certain demographics are going to visit a different subset of the web than other demographics are, and that such behavior will not be captured by Google's 'sensitive sites' framing," the EFF added.

There are other methods for blocking FLoC, as laid out by DuckDuckGo. Unsurprisingly, the main one involves bypassing Google Chrome entirely – bear in mind, of course, that DuckDuckGo has its own competing browser in the game.

Users can also remain logged out of their Google account; switch off ad personalization within the Google Ad Settings; avoid syncing their search history data with Chrome; and disable Web & App Activity within Google's Activity Controls.

Google plans to roll out updated activity controls with the incoming Chrome 90 release.

[Source: This article was published in techrepublic.com By Owen Hughes - Uploaded by the Association Member: Jasper Solander]

Categorized in Search Engine

Google detailed a host of new improvements at its “Search On” event that it will make to its foundational Google search service in the coming weeks and months. The changes are largely focused on using new AI and machine learning techniques to provide better search results for users. Chief among them: a new spell checking tool that Google promises will help identify even the most poorly spelled queries.

According to Prabhakar Raghavan, Google’s head of search, 15 percent of Google search queries each day are ones that Google has never seen before, meaning the company has to constantly work to improve its results.

Screenshot 1

Part of that is because of poorly spelled queries. According to Cathy Edwards, VP engineering at Google, 1 in 10 search queries on Google are misspelled. Google has long tried to help with its “did you mean” feature that suggests proper spellings. By the end of the month, it’ll be rolling out a massive update to that feature, which uses a new spelling algorithm powered by a neural net with 680 million parameters. It runs in under three milliseconds after each search, and the company promises it’ll offer even better suggestions for misspelled words.

Another new change: Google search can now index individual passages from webpages, instead of just the whole webpage. For example, if users search for the phrase “how can I determine if my house windows are UV glass,” the new algorithm can find a single paragraph on a DIY forum to find an answer. According to Edwards, when the algorithm starts to roll out next month, it’ll improve 7 percent of queries across all languages.

Screenshot 3

Google is also using AI to divide broader searches into subtopics to help provide better results (say, helping find home exercise equipment designed for smaller apartments versus just providing general workout gear information).

Screenshot 4

Lastly, the company is also starting to use computer vision and speech recognition to automatically tag and divide videos into parts, an automated version of the existing chapter tools it already provides. Cooking videos, for example, or sports games can be parsed and automatically divided into chapters (something Google already offers to creators to do by hand), which can then be surfaced in search. It’s a similar effort to the company’s existing work in surfacing specific podcast episodes in search, instead of just showing the general feed.

[Source: This article was published in theverge.com By Chaim Gartenberg - Uploaded by the Association Member: Logan Hochstetler]
Categorized in Search Engine

YouTube shares new details about how its recommendation algorithm

New information about how various factors influence YouTube’s video recommendation algorithm is revealed by members of the team responsible for working on it.

Having only been implemented in 2016, we still have a rudimentary of how YouTube’s machine learning algorithm works.

We know video recommendations are influenced by factors such as clicks, watch time, likes/dislikes, comments, freshness, and upload frequency.

We do not know, for example, whether external traffic has any impact on video recommendations.

It’s also not known whether underperforming videos will affect the likelihood of future videos being recommended.

The impact of other potentially negative factors, such as inactive subscribers or too-frequent uploads, is not known either.

Those are the factors YouTube’s team discusses in a new Q&A video about the recommendation algorithm. Here is a summary of all questions and answers.

Underperforming Videos

If one of my videos under-performs, is that going to hurt my channel? Could a few poor videos pull down better videos in the future?

YouTube doesn’t make assessments about a channel as a whole based on the performance of a few videos.

YouTube only cares about how people are responding to a given video when deciding whether to recommend it to others.

The recommendation algorithm is always going to be “following the audience.”

If a video is attracting an audience then it will show up in users’ recommendations regardless of how the channel’s previous videos performed.

Channels shouldn’t be concerned about some kind of algorithmic demotion based on a dip in viewership.

It’s normal for the performance of videos to fluctuate in terms of views and other metrics. So YouTube is careful not to have all of its recommendations driven by those metrics.

Too Many Uploads Per Day

Is there a point at which the number of videos per day/week on each channel is so high that the algorithm is overwhelmed and videos slip through?

There is no limit to how many videos can be recommended to a given viewer from a channel in a single day.

Channels can upload as much as they want. How many views each video receives comes down to viewer preferences.

YouTube’s recommendation system will continue to recommend videos as long as viewers continue to watch them.

If a channel is uploading more videos than usual, and each video is getting progressively fewer views, that may be a sign the audience is getting burned out.

While there is no limit to how many videos YouTube will recommend from a channel in a single day, there is a limit to how many notifications will be sent out.

YouTube only allows 3 notifications per channel in a 24 hour period.

Inactive Subscribers

My channel has been around for quite a few years and I think I may have lots of inactive subscribers. Should I create a new channel and then re-upload the videos in order to appear more acceptable to the algorithm?

Inactive subscribers is not a factor impacting YouTube’s recommendation algorithm.

This goes back to the first question where YouTube says its algorithm follows the audience.

A channel with inactive subscribers can still get its next video shown in the recommendations section if it attracts an audience.

Creating a new channel and re-uploading the same videos will not help with getting those videos shown to more people.

YouTube remembers viewer preferences, so there’s little chance of reaching those inactive subscribers with a new channel.

Creators should only start a new channel if they decide to go in a different direction with their content.

External Traffic

How important is external traffic?

External traffic is definitely a factor that influences YouTube’s recommendation algorithm.

However, its influence only extends so far.

External traffic can help get a video shown in the recommendations section. But once it’s there it has to perform well with viewers.

Long term success of a video depends on how people respond after clicking on it in their recommendations.

I’m getting lots of traffic from external websites which is causing my click-through-rates and average view durations to drop, is this going to hurt my video’s performance?

YouTube says it’s not a problem if average view duration drops when a video receives a significant amount of external traffic.

Apparently it’s common for this to happen, and it has no impact on a video’s long-term success.

Again, YouTube’s algorithm cares more about viewers engage with a video after clicking on it in their recommendations.

The algorithm is not concerned with what viewers do after clicking on a video from an external website or app.

[Source: This article was published in searchenginejournal.com By Matt Southern- Uploaded by the Association Member: Jason bourne]

Categorized in Search Engine

Social media algorithms, artificial intelligence, and our own genetics are among the factors influencing us beyond our awareness. This raises an ancient question: do we have control over our own lives? This article is part of The Conversation’s series on the science of free will.

Have you ever watched a video or movie because YouTube or Netflix recommended it to you? Or added a friend on Facebook from the list of “people you may know”?

And how does Twitter decide which tweets to show you at the top of your feed?

These platforms are driven by algorithms, which rank and recommend content for us based on our data.

Hear directly from the scientists on the latest research.

As Woodrow Hartzog, a professor of law and computer science at Northeastern University, Boston, explains:

If you want to know when social media companies are trying to manipulate you into disclosing information or engaging more, the answer is always.

So if we are making decisions based on what’s shown to us by these algorithms, what does that mean for our ability to make decisions freely?

What we see is tailored for us

An algorithm is a digital recipe: a list of rules for achieving an outcome, using a set of ingredients. Usually, for tech companies, that outcome is to make money by convincing us to buy something or keeping us scrolling in order to show us more advertisements.

The ingredients used are the data we provide through our actions online – knowingly or otherwise. Every time you like a post, watch a video, or buy something, you provide data that can be used to make predictions about your next move.

These algorithms can influence us, even if we’re not aware of it. As the New York Times’ Rabbit Hole podcast explores, YouTube’s recommendation algorithms can drive viewers to increasingly extreme content, potentially leading to online radicalisation.

Facebook’s News Feed algorithm ranks content to keep us engaged on the platform. It can produce a phenomenon called “emotional contagion”, in which seeing positive posts leads us to write positive posts ourselves, and seeing negative posts means we’re more likely to craft negative posts — though this study was controversial partially because the effect sizes were small.

Also, so-called “dark patterns” are designed to trick us into sharing more, or spending more on websites like Amazon. These are tricks of website design such as hiding the unsubscribe button, or showing how many people are buying the product you’re looking at right now. They subconsciously nudge you towards actions the site would like you to take.

You are being profiled

Cambridge Analytica, the company involved in the largest known Facebook data leak to date, claimed to be able to profile your psychology based on your “likes”. These profiles could then be used to target you with political advertising.

“Cookies” are small pieces of data which track us across websites. They are records of actions you’ve taken online (such as links clicked and pages visited) that are stored in the browser. When they are combined with data from multiple sources including from large-scale hacks, this is known as “data enrichment”. It can link our personal data like email addresses to other information such as our education level.

These data are regularly used by tech companies like Amazon, Facebook, and others to build profiles of us and predict our future behaviour.

You are being predicted

So, how much of your behaviour can be predicted by algorithms based on your data?

Our research, published in Nature Human Behaviour last year, explored this question by looking at how much information about you is contained in the posts your friends make on social media.

Using data from Twitter, we estimated how predictable peoples’ tweets were, using only the data from their friends. We found data from eight or nine friends was enough to be able to predict someone’s tweets just as well as if we had downloaded them directly (well over 50% accuracy, see graph below). Indeed, 95% of the potential predictive accuracy that a machine learning algorithm might achieve is obtainable just from friends’ data.

file-20200622-54989-bo83l3.jpg
Average predictability from your circle of closest friends (blue line). A value of 50% means getting the next word right half of the time — no mean feat as most people have a vocabulary of around 5,000 words. The curve shows how much an AI algorithm can predict about you from your friends’ data. Roughly 8-9 friends are enough to predict your future posts as accurately as if the algorithm had access to your own data (dashed line). Bagrow, Liu, & Mitchell (2019)

Our results mean that even if you #DeleteFacebook (which trended after the Cambridge Analytica scandal in 2018), you may still be able to be profiled, due to the social ties that remain. And that’s before we consider the things about Facebook that make it so difficult to delete anyway.We also found it’s possible to build profiles of  — so-called “” — based on their contacts who are on the platform. Even if you have never used Facebook, if your friends do, there is the possibility a shadow profile could be built of you.

On social media platforms like Facebook and Twitter, privacy is no longer tied to the individual, but to the network as a whole.

No more free will? Not quite

But all hope is not lost. If you do delete your account, the information contained in your social ties with friends grows stale over time. We found predictability gradually declines to a low level, so your privacy and anonymity will eventually return.

While it may seem like algorithms are eroding our ability to think for ourselves, it’s not necessarily the case. The evidence on the effectiveness of psychological profiling to influence voters is thin.

Most importantly, when it comes to the role of people versus algorithms in things like spreading (mis)information, people are just as important. On Facebook, the extent of your exposure to diverse points of view is more closely related to your social groupings than to the way News Feed presents you with content. And on Twitter, while “fake news” may spread faster than facts, it is primarily people who spread it, rather than bots.

Of course, content creators exploit social media platforms’ algorithms to promote content, on YouTubeReddit and other platforms, not just the other way round.

At the end of the day, underneath all the algorithms are people. And we influence the algorithms just as much as they may influence us.

[Source: This article was published in theconversation.com By Misha Ketchell - Uploaded by the Association Member: Jay Harris]

Categorized in Search Engine

Learn key insights that will help you understand how the algorithms of Instagram, YouTube, TikTok, Twitter, and Facebook work.

Here’s an old question that gets asked every year:

How do social media algorithms work?

But, you can often uncover strategic insights by looking at an old question like this one from a different perspective.

In fact, there’s a term for this effect.

It’s called the “parallax” view.

parallax-view.png

For example, marketers often look for influencers on the social media platforms with the greatest reach.

But, influencers evaluate these same platforms based on their opportunity to grow their audience and make more money.

This explains why The State of Influencer Marketing 2020: Benchmark Report found that the top five social media platforms for influencer marketing are:

  • Instagram (82%).
  • YouTube (41%).
  • TikTok (23%).
  • Twitter (23%).
  • Facebook (5%).

This list made me wonder why marketers focus on the reach of their campaign’s outputs, but influencers are focused on the growth of their program’s outcomes.

Influencers want to learn how the Instagram and YouTube algorithms work, because they want their videos discovered by more people.

And influencers are interested in learning how the TikTok and Twitter algorithms work, because they are thinking about creating content for those platforms.

Facebook’s algorithm, however, doesn’t seem quite as important to today’s influencers – unless Facebook represents a significant opportunity for them to make more money.

There are a lot of strategic insights that marketers can glean from looking at how social media algorithms work from an influencer’s point of view.

How the Instagram Algorithm Works

Back in 2016, Instagram stopped using a reverse-chronological feed.

Since then, the posts in each user’s feed on the platform has been ordered according to the Instagram algorithm’s ranking signals.

According to the Instagram Help Center:

“Instagram’s technology uses different ways, or signals, to determine the order of posts in your feed. These signals are used to help determine how your feed is ordered, and may include:

  • “Likelihood you’ll be interested in the content.
  • “Date the post was shared.
  • “Previous interactions with the person posting.”

This has a profound impact on influencers – as well as the marketers who are trying to identify the right influencers, find the right engagement tactics, and measure the performance of their programs.

Relevance

The first key signal is relevance, not reach.

Is click fraud affecting your PPC campaigns?
Start a free trial now and experience your GoogleAds account in a fraud-free environment.

Why?

Because Instagram users are more likely to be interested in an influencer’s content if it is relevant – if it’s about what interests them.

In other words, if you’re interested in football (a.k.a., soccer), then the likelihood that you’ll be interested in content by Nabaa Al Dabbagh, aka “I Speak Football Only,” is high.

But, far too many marketers are looking for celebrities and mega-influencers who have lots of Instagram followers (a.k.a., reach), instead of looking for macro-, mid-tier, micro-, or nano-influencers who are creating relevant content that their target audience is more likely to find interesting.

i-speak-football-only.png

Recency

The second key signal is recency, or how recently a post has been shared.

This gives an advantage to influencers like Marwan Parham Al Awadhi, a.k.a., “DJ Bliss,” who post frequently.

dj-bliss.png

Unfortunately, far too many marketers are engaging influencers to create a single post during a campaign instead of building a long-term relationship with brand advocates who will generate a series of posts that recommend their brand on an ongoing basis.

Resonance

The third key signal is resonance.

In other words, how engaging are an influencer’s posts?

Do they prompt interactions such as comments, likes, reshares, and views with the influencer’s audience?

And, unfortunately, way too many marketers assume that an influencer’s post that mentions their brand has increased their brand awareness, using bogus metrics like Earned Media Value (EMV).

If they’d read, Why International Search Marketers Should Care About Brand Measurement, then they’d realize there are a variety of legitimate ways to measure the impact of an influencer marketing campaign on:

  • Brand awareness.
  • Brand frequency.
  • Brand familiarity.
  • Brand favorability.
  • Brand emotions.
  • Purchase consideration.
  • Brand preference.
  • Brand demand.

Using this parallax view, it’s easy to see that too many marketers mistakenly think influencer marketing is just like display advertising.

They’re buying posts from influencers the same way they would buy ads from publishers.

So, marketers who only look at an influencer’s reach shouldn’t be shocked, shocked to discover that some influencers are using bad practices such as fake followers, bots, and fraud to inflate their numbers.

If you use a one-dimensional view of an influencer’s influence, then you reap what you sow.

How Does the YouTube Algorithm Work?

Now, I’ve already written several articles on how the YouTube algorithm works, including:

But, these articles were written for marketers, not influencers.

So, what can we learn from looking at YouTube’s algorithm from an influencer’s point of view?

Well, according to YouTube Help:

“The goals of YouTube’s search and discovery system are twofold: to help viewers find the videos they want to watch, and to maximize long-term viewer engagement and satisfaction.”

So, YouTube influencers need to start by creating great content on discoverable topics.

Why?

Well, YouTube is one of the most-used search engines in the world.

People visit the site looking for videos about all sorts of subjects.

These viewers may not necessarily be looking for a specific influencer’s video, but they’ll discover it if it ranks well in YouTube search results or suggested videos.

Learn how to use Google Trends to find out what your audiences is looking for on YouTube.

The default results in Google Trends show “web search” interest in a search term or a topic.

But, if you click on the “web search” tab, the drop-down menu will show you that one of your other options is “YouTube search” interest.

YouTube influencers can then use what they see to inform their content strategies.

For example, you might learn that there was 31% more YouTube search interest worldwide in the topic, beauty, than in the topic, fashion.

fashion-vs-beauty.png

Or you might discover that there was 18 times more YouTube search interest worldwide in the sport, drifting, than in the sport, motorsport.

motorsport-vs-drifting.png

YouTube’s algorithm can’t watch your videos, so you need to optimize your metadata, including your titles, tags, and descriptions.

Unfortunately, most marketers don’t use this approach to find the search terms and topics on YouTube that are relevant for their brand and then identify the influencers who are creating content that ranks well for these keywords and phrases.

Now, getting your YouTube video content discovered is only half the battle.

Influencers also need to build long watch-time sessions for their content by organizing and featuring content on their channel, including using series playlists.

As YouTube Help explains:

“A series playlist allows you to mark your playlist as an official set of videos that should be viewed together. Adding videos to a series playlist allows other videos in the playlist to be featured and recommended when someone is viewing a video in the series. YouTube may use this info to modify how the videos are presented or discovered.”

Fortunately, one of the guest speakers for NMA’s program was Mark Wiens, one of the most famous food vloggers in the world.

His YouTube channel has more than 1.4 billion views and almost 6.7 million subscribers.

Here are examples of the playlists that he had created, including Thai food and travel guides.

mark wien

Now, marketers could also look over the playlists on the YouTube channels of influencers when they’re evaluating which ones are “right” for a campaign.

However, I strongly suspect that this only happens once in a blue moon.

How Does the TikTok Algorithm Work?

The TikTok Newsroom posted How TikTok recommends videos #ForYou just before I was scheduled to talk about this topic.

Hey, sometimes you get lucky.

tiktok.png

Here’s what I learned:

“When you open TikTok and land in your For You feed, you’re presented with a stream of videos curated to your interests, making it easy to find content and creators you love. This feed is powered by a recommendation system that delivers content to each user that is likely to be of interest to that particular user.”

So, how does this platform’s recommendation system work?

According to TikTok:

“Recommendations are based on a number of factors, including things like:

  • “User interactions such as the videos you like or share, accounts you follow, comments you post, and content you create.
  • “Video information, which might include details like captions, sounds, and hashtags.
  • “Device and account settings like your language preference, country setting, and device type. These factors are included to make sure the system is optimized for performance, but they receive lower weight in the recommendation system relative to other data points we measure since users don’t actively express these as preferences.”

The TikTok Newsroom adds:

“All these factors are processed by our recommendation system and weighted based on their value to a user. A strong indicator of interest, such as whether a user finishes watching a longer video from beginning to end, would receive greater weight than a weak indicator, such as whether the video’s viewer and creator are both in the same country. Videos are then ranked to determine the likelihood of a user’s interest in a piece of content, and delivered to each unique For You feed.”

TikTok cautions:

“While a video is likely to receive more views if posted by an account that has more followers, by virtue of that account having built up a larger follower base, neither follower count nor whether the account has had previous high-performing videos are direct factors in the recommendation system.”

It’s worth noting that Oracle has won the bid to acquire TikTok’s U.S. operations after ByteDance rejected a bid by Walmart and Microsoft.

Meanwhile, YouTube released YouTube Shorts, a TikTok-like feature, while Facebook recently launched Instagram Reels, which is basically a TikTok knock-off.

So, it appears that some very big players are convinced that TikTok represents a significant opportunity to make more money, or a competitive threat to the growth of their own social media platforms.

I wish that I could add more, but I’m a stranger here myself.

How Does Twitter’s Algorithm Work?

When Twitter was launched back in 2006, it had a simple timeline structure and tweets were displayed in reverse chronological order from the people you followed.

But, like other social media, Twitter started using an algorithm to show users posts that different factors indicate they’ll like.

The biggest recent change to Twitter’s algorithm took place in 2017.

According to a Twitter blog post by Nicolas Koumchatzky and Anton Andryeyev:

“Right after gathering all Tweets, each is scored by a relevance model. The model’s score predicts how interesting and engaging a Tweet would be specifically to you. A set of highest-scoring Tweets is then shown at the top of your timeline, with the remainder shown directly below.”

Their post added:

“Depending on the number of candidate Tweets we have available for you and the amount of time since your last visit, we may choose to also show you a dedicated “In case you missed it” module. This modules meant to contain only a small handful of the very most relevant Tweets ordered by their relevance score, whereas the ranked timeline contains relevant Tweets ordered by time. The intent is to let you see the best Tweets at a glance first before delving into the lengthier time-ordered sections.”

How Does Facebook’s Algorithm Work?

The biggest recent change to Facebook’s algorithm took place in January 2018.

In a Facebook post, Mark Zuckerberg announced:

“I’m changing the goal I give our product teams from focusing on helping you find relevant content to helping you have more meaningful social interactions.”

He added:

“The first changes you’ll see will be in News Feed, where you can expect to see more from your friends, family and groups. As we roll this out, you’ll see less public content like posts from businesses, brands, and media. And the public content you see more will be held to the same standard — it should encourage meaningful interactions between people.”

That same day, Adam Mosseri, who was then the head of News Feed, also wrote a Facebbok post that said:

“Today we use signals like how many people react to, comment on or share posts to determine how high they appear in News Feed. With this update, we will also prioritize posts that spark conversations and meaningful interactions between people. To do this, we will predict which posts you might want to interact with your friends about, and show these posts higher in feed. These are posts that inspire back-and-forth discussion in the comments and posts that you might want to share and react to – whether that’s a post from a friend seeking advice, a friend asking for recommendations for a trip, or a news article or video prompting lots of discussion.”

He added:

“Because space in News Feed is limited, showing more posts from friends and family and updates that spark conversation means we’ll show less public content, including videos and other posts from publishers or businesses.”

So, it isn’t surprising that influencers got the memo.

Which explains why so few believe Facebook represents a significant opportunity to make more money.

Ironically, it’s unclear that marketers got the memo.

Far too many are still cranking out Facebook posts and videos despite the fact that few people are reacting to, commenting on, or sharing them.

Or, as I wrote in Two Social Media Vanity Metrics You Need to Stop Tracking, marketers should stop tracking Facebook Page Likes and Followers because “you’re lucky if .0035% of your Fans and Followers even sees your post or tweet these days.”

new-media-academy.jpg

The Takeaway

These are just some of the strategic insights that marketers can discover by looking at how social media algorithms work from an influencer’s point of view.

If you’re a marketer, then I suggest you move most of the people and budget that you’ve dedicated to creating branded content on Facebook into influencer marketing on Instagram and YouTube.

As for TikTok and Twitter, wait until after the dust settles later this year.

[Source: This article was published in searchenginejournal.com By Greg Jarboe - Uploaded by the Association Member: Corey Parker]

Categorized in Social

Google suffered a glitch that negatively impacted search quality. This was not an update. It was a mistake of some kind.

Google’s search algorithm suffered an unprecedented glitch that affected search results.

Many in the search community believed it was an update.

The disruption in Google search was not an update.

Google’s John Mueller tweeted:

“I don’t have all the details yet, but it seems like this was a glitch on our side and has been fixed in the meantime.”

Official Explanation

Tuesday August 11, 2020 Google’s Webmasters Twitter account tweeted an explanation.

Caffeine Index Issue?

Google has a web crawling and indexing system called Caffeine.  Caffeine allowed Google to process data faster than ever before.

 

This Caffeine indexing system empowered Google to continually index the entire web in real-time.

With a fresher index, Google could then show more up to date search results.

Google’s Gary Illyes  tweeted an explanation of how complex a search index is, with a caveat that the list he published was only a partial list.

“The indexing system, Caffeine, does multiple things:

1. ingests fetchlogs,
2. renders and converts fetched data,
3. extracts links, meta and structured data,
4. extracts and computes some signals,
5. schedules new crawls,
6. and builds the index that is pushed to serving.”

Followed by this tweet:

“If something goes wrong with most of the things that it’s supposed to do, that will show downstream in some way. If scheduling goes awry, crawling may slow down. If rendering goes wrong, we may misunderstand the pages. If index building goes bad, ranking & serving may be affected.”

Then he concluded with this:

Google Caffeine Index?

It was kind of surprising to see the Google Caffeine system cited.

It was officially announced in 2010.

The announcement stated that it was a foundation for indexing that was meant to scale for the future.

This is what the official 2010 Caffeine announcement stated:

“We’ve built Caffeine with the future in mind.

Not only is it fresher, it’s a robust foundation that makes it possible for us to build an even faster and comprehensive search engine that scales…”

[moduleplant id="534"]

Google Search Glitch Was Worldwide

The Google search glitch was keenly felt in Europe as well as Asia and all English speaking countries.

Google’s search glitch appeared to affect all languages, countries, and niches.

It affected everything from local services to recipes.

Ecommerce sites reported extreme fluctuations in rankings.

Bad Search Results

Recipe Blog SEO Casey Markee tweeted a screenshot of how bad the recipe search results were.

Think you should be getting better results with your Google Ads?
Your campaign may be suffering from click fraud. Check if you need to protect your ads from competitors & bots. Simple setup. Get a free checkup today.

Google Search Glitch Created Poor Search Results

Google’s search results became incredibly bad, some to the point of being useless.

I tried searching for an article from a specific site and Google wouldn’t show it to me, even when I used the name of the site that contained the article.

It felt somewhat like in the old days when PageRank had a stronger influence.

WebmasterWorld had great real-time coverage of the glitch as it happened.

A member from WebmasterWorld, webdev29, noted how the big sites like Amazon seemed to dominate the SERPs.

“huge update also in France ATM, no word to describe the mess, its simply crazy ! there is no more ecommerces in my SERP (decoration) and mine has just lost everything…6 years destroyed in just one minute and the lives of several employees at stake! it’s not possible that it continues like this, in the SERP, there are only the big marketplaces (cdiscount, amazon, laredoute, aliexpress…) and some more or less recent sites without much interest…all the rest has disappeared on the deep pages of the search engine.”

 

Report from Italy

WebmasterWorld member teokolo shared:

“Seems like a big update in progress here in Italy.
Every niche I follow is messed up. Shops are gone, affiliate sites have disappeared, serps are full of Amazon, ebay and news sites.”

Google Glitch Impact in Norway

mini_007 said:

“wow insane big update here in Norway, never seen so big change.”

Massive Fluctuations in Google Search Results

Whether it was on Facebook, WebmasterWorld or Twitter, the common observation was that there were massive fluctuations in the search results.

This report from WebmasterWorld member Whoa182 is typical:

“What the hell is going on?

Just noticed my articles have gone from page 1 to page 7+

Seems to have just happened in the past few hours! Quite a few of my competitors have all disappeared from the SERPs.

Edit: Okay, it’s just massive fluctuations in page positions. One minute it’s on page 1, next it’s page 7 or whatever, and then back again.”

Google Has Not Yet Explained the Cause

Google’s Danny Sullivan is the one who typically announces updates.

 

Google Webmaster Trends Analysts, Gary Illyes and Mueller also share announcements of changes at Google as well, including glitches.

For example, at the beginning of 2020 Google suffered a glitch that caused an issue with Google’s index. It was Illyes who did the explaining.

Google suffered a massive glitch that caused the search results around the world to become less usable.

The cause of the glitch, according to Google Webmaster Trends Analyst Gary Illyes appears to be related to Google’s Caffeine indexing system or something along those lines.

[Source: This article was published in searchenginejournal.com By Roger Montti - Uploaded by the Association Member: Deborah Tannen]

Categorized in Search Engine

Pinterest aims to display a greater variety of content types in the home feed by utilizing a new ranking model.

Pinterest is introducing a new ranking model to its home feed in an effort to surface certain types of content more often.

Traditionally, Pinterest ranks content in the home feed using a click-through prediction model.

Pins that a user is most likely to click on, as determined by past activity, are prioritized in their home feed

While that model is effective at maximizing user engagement, it’s not the best model for surfacing a variety of content types.

For example, if a user never clicks on video content then they’ll never be shown pins with video in their home feed.

But that doesn’t necessarily mean they wouldn’t engage with video content if it were to be surfaced.

Pinterest found itself with a problem of wanting to boost more content types while still keeping content recommendations relevant.

To solve this problem, Pinterest is introducing a real-time ranking system for its home feed called “controllable distribution.”

Controllable Distribution

Pinterest describes controllable distribution as a “flexible real-time system.”

It’s not a complete algorithm overhaul. Rather, controllable distribution is only applied after the traditional home feed ranking algorithm.

Pinterest will still use its click-through prediction model to find relevant content. Then it will apply controllable distribution to diversify the types of content being displayed.

Controllable distribution makes it possible to specify a target for how many impressions a certain content type should receive.

For example, controllable distribution could be used to specify that 4% of users’ home feeds should contain video content.

This is done through a system that tracks what percentage of the feed was video in the past. Then, the system boosts or demotes content according to how close that percentage is to the specified target.

Pinterest says this can be accomplished while still respecting users’ content preferences.

What Does This Mean for Marketers?

As a real-time system, the controllable distribution model will be continuously adjusted.

On one hand, that means the home feed won’t get stale for users.

On the other hand, it’s not exactly possible to optimize for an algorithm that changes in realtime.

Perhaps the best piece of advice for Pinterest marketers to take away from this is to follow Pinterest’s lead.

Pinterest is diversifying the types of content in the home feed. If you want more opportunities to show up in peoples’ feeds then diversify the types of content you publish.

For example, if you only publish photos, then consider adding some videos or GIFs to the mix. Maybe some product pins if you’re an e-commerce retailer.

Pinterest’s target for displaying certain types content will be changing all the time.

Publishing a wide variety of content will help ensure you have the right type of content available at the time Pinterest wants to display it.

Additional Notes

Pinterest’s home feed ranking team used to do manually what controllable distribution is designed to do algorithmically.

Yes, Pinterest’s home feed ranking team actually used to step in and adjust how often certain types of content appeared in users’ home feed.

Yaron Greif of Pinterest’s home feed ranking team describes the old process as “painful for both practical and theoretical reasons.”

“In practice, these hand-tuned boosts quickly became unmanageable and interfered with each other. And worse, they often stop working over time — especially when ranking models are updated. We regularly had to delay very promising new ranking models because they broke business constraints.

In theory, controlling content on a per-request basis is undesirable because it prevents personalization. If we show each user the same number of video Pins we can’t show more videos to people who really like to watch videos or vice versa.”

Pinterest says it’s committed to investing in the post-ranking stage of surfacing content. So it’s possible we may see this model applied elsewhere on the platform in the future.

[Source: This article was published in searchenginejournal.com By Matt Southern - Uploaded by the Association Member: Edna Thomas]

Categorized in Social
Page 1 of 8

AOFIRS

World's leading professional association of Internet Research Specialists - We deliver Knowledge, Education, Training, and Certification in the field of Professional Online Research. The AOFIRS is considered a major contributor in improving Web Search Skills and recognizes Online Research work as a full-time occupation for those that use the Internet as their primary source of information.

Get Exclusive Research Tips in Your Inbox

Receive Great tips via email, enter your email to Subscribe.