How you are being categorized by Tinder algorithm

Emily doesn’t swipe anymore. As 57 million people around the world, she was using Tinder. The 21 years old student is now in a relationship. But anyway she may be more reluctant to use the American dating app. More than ever.

‘At best a client, at worst, a product’

Indeed, it has recently been reported by the French journalist Judith Duportail that Tinder was rating people to make accurate suggestions. In her 2019 book L’amour sous algorithme (The Love algorithm), she unveils the existence of the Elo Score, that is to say a desirability rate. Judith Duportail received 800 pages of personal data as stated by the European law on data protection. She had everything : conversations, places geolocated, likes. Everything but her Elo Score.

‘At best a client ; at worst, a product’ Duportail claims, as she shows that the app heighten inequalities within relationships, encouraging men to date younger women, less wealthy and with a poorer education background.

By the author

Emily told us that she had been using the app for ‘a fair amount of time’. She wasn’t looking for anything serious at the time. She ‘thought it’d be a good laugh, a good way to meet new people’. Yet, she quickly noticed that the algorithm was shaping her experience.

I did find it quite addictive and I also noticed that it would stop showing me certain kind of people after a while. And it did feel like it filtered out, based on things like education level, potentially race, or the kind of jobs people had.

Even though the terms and conditions are public, there is allegedly no way for users to find their rate nor to know the way it is calculated. The underlying coding programs are not revealed by Tinder but would apparently be based on the success of our profile picture, the number of complicated words used in the chat.

The app even goes further : people do not have the same clout on our value, as it depends on their own rate. In 2019, the Vox reported that ‘the app used an Elo rating system, which is the same method used to calculate the skill levels of chess players: You rose in the ranks based on how many people swiped right on (“liked”) you, but that was weighted based on who the swiper was. The more right swipes that person had, the more their right swipe on you meant for your score’.

Tinder stated on their blog that the Elo Score was old news, an ‘outdated measure’ and now, the app was relying on the activity of its users. 

According to tinder’s website, ‘Tinder is more than a dating app. It’s a cultural movement.’ 

In a sense, this is not only marketing, this is partly true. Tinder is now renowned worldwide. 9% of French couples who started their relationship between 2005 and 2013 met online according to the National Institute of Demographic Studies. In the UK, based on an Infogram study, this number reached 20% in 2013 and 70% for the same-sex couples according to sociologists Reuben Thomas and Michael Rosenfeld. 

Birds of a feather flock together

Sociologists haven’t been waiting for social media to identify the phenomenon of endogamy (or in-marriage) : we tend to date people from the same social class. We share similar activities and meet them within familiar groups : colleagues, friends, friends of friends. Thus, digital categorizing is pushing further existing dynamics.

When I swiped right, Emily says, it would show me more of those people. That is obviously kind of reinforcing social stratification, I guess, and the fact that people tend to end up with the ones that are similar to them.

By the author

Camille, a 21 years old bisexual user of the app, also noticed she was shown similar profiles. She told us that it appeared ‘rather obvious when you use the app’. Yet, Camille thinks it helps to find someone you are likely to meet in real life.

It seems as a good way to select the people to interact with. It seems rather innocent. It’s not something you give a lot of thoughts about.

Indeed, Tinder’s algorithm aims at finding the same interests, equivalent wages (if you tell your job in your description, the average income can be found). But key words can be identified without being understood. Surprisingly enough, Tinder doesn’t get irony or expressions, hence awkward date experiences.

The biases of machine-learning systems 

This might be a matter of time. As Diggit Magazine recently reported, algorithms are machine-learning systems, fed by societal practices. This can be for the best… And the worse. That is to say that this love conditioning has its biases as the app can be supplied with racist or sexist contents. 

Moreover, Aude Bernheim unveiled in her AI study L’intelligence artificielle, pas sans elles ! that 88% of algorithms were made by men.

By the author

It might account for the unbalance within Tinder’s users. Only 38% of Tinder users are female against 62% of males. The experience as female queer user also raises questions as Camille’s story proves.

About women, I noticed that the app had lots of problems identifying the kind of girls I would like to date. I do believe that for queer people, especially women, it is actually harder to interact on the app. There are so many couples looking for a third person. So the algorithm works when you want an heterosexual relationship but for queer people… It does not work as well as it should. It is sadly predictable and that’s probably why I stopped using Tinder.

The app is well aware of these situations and tries to look inclusive on its website “Swipe life” made out of articles on ‘dating tips’ such as ‘How to Talk to Your Partner About Non-monogamy’ or ‘What You Should Know When Dating Someone With Bipolar Disorder’. Yet, the lack of transparency on their algorithm is still widely criticized. 

By the author

They also seem to put the emphasis on who insignificant dating has become. They report on their website that ‘70% of these college students have never met up with their matches…and 45% say they use Tinder mostly for confidence boosting procrastination’ Dating is no longer a big deal. But it is a big business as the app now has 800 million euros of annual turnover.

If the categorization seems harder to understand due to the end of the Elo Score, both analysts and users claim the profiling hasn’t come to an end yet.

Melanie Lefkowitz wrote that ‘Although partner preferences are extremely personal, it is argued that culture shapes our preferences, and dating apps influence our decisions.’ And Emily couldn’t agree more as she experienced it herself.

On the one hand, it really opens up your circle because there’s suddenly all those people you would never have bumped into in your life but you can message, and meet up with. On the other hand, the algorithm kind of dictates who you’re able to see as being out there. It’s actually quite limiting.

The Invisible Walls of YouTube

Photo by: Dado Ruvic/Reuters

November 2019 was a watershed month for American politics. It marked the beginning of the formal impeachment inquiry of the President of the United States, Donald Trump, only the third time an American president was to be impeached. Public hearings were held, investigations were made, and American news was abuzz with objective discussions, opposing arguments and rancorous accusations.

Politics can be controversial, with largely divided opinions along partisan lines. The party division is marked by differences in principles of government, and religious and secular world views. This is further complicated by each individual having their opinions and beliefs based on their own experiences, principles and values. In a democracy, some would argue that it is important that the public remain actively engaged in the activities and conduct of their government. It is especially easy in this day and age to do so. Social media and digital platforms have become the cornerstone of modern society, with a plethora of content at each individual’s fingertips.

YouTube is a large player in this global phenomenon, having grown from 2005 into the second largest social network in the world today. With 73% of US adults reportedly frequenting the video media platform, it can be said that YouTube is as influential as televised news channels. The platform has even hosted livestreams of debates during US presidential elections, giving American citizens real time access to information which may ultimately influence their vote in a US election.

Given the power it wields over such a massive number of American viewers, one might assume YouTube would capably present videos involving Trump’s impeachment without bias, apart from political alignment. A quick use of its search engine will reveal it hosts news organizations with different political stances, posting through their own official channels their respective interpretation of stories as events unfold. On the surface, this seems fine. Nothing wrong with a platform that presents both sides of a story along with different points of view. If anything, this makes YouTube an objective place to learn about news surrounding the impeachment, right?

Wrong, for a number of reasons. Studies indicate that social media usage has given rise to ‘echo chambers’. Echo chambers describe the mental concept of restricting one’s media consumption from opposing views. From videos to online forums, this isolation from other perspectives of an issue causes the reinforcement of the individual’s point of view, believing it to be more true or right than others. This bias can grow to the point of taking priority over facts and logic. In short, echo chambers are real, dangerous and EVERYWHERE in the digital space.

And what better way to observe the potency of this effect than searching for video coverage of the historical third impeachment of a US President on YouTube. Using a neutral browser (a browser without search history, a digital footprint, or geographic preferences), three different news channels were looked up, each with their own political stances as presented in the Media Bias Chart: Buzzfeed, Fox News and NBC.

The Media Bias Chart. Image By: Ad Fontes Media

One random video was then pulled from each channel with the word ‘impeachment’ in the title, all uploaded within November 2019, when the impeachment process began. These videos are:

Buzzfeed News – Let’s Hear It For The Whistleblower – Impeachment Today Podcast

Fox News – Tucker’s big takeaways from the Trump impeachment saga

NBC News – Highlights: Fiona Hill And David Holmes’ Impeachment Hearing Testimony

Next, the YouTube Data Tool was used to scrape each video for their recommended video section. This data was then processed by the network analysis program, Gephi, to visualize the networks within which these three videos reside.

Visualization of Video Recommendation by News Channel. Image by: Author

Lo and behold, it appears that overlaps in recommended videos among these three channels are rare. Between the left leaning NBC and the conservative Fox News, a mere 5 videos out of a cumulative 171 video recommendations could possibly lead to the network of videos of the other news channel. This means that a user has only a 3% chance of being exposed to any content representing the opposing political stance of both NBC and Fox News, regardless of which channel they had started with. The echo chamber looks even stronger for Buzzfeed, with its network of recommended videos sharing absolutely no overlap of recommended videos with other news channels. This means that if you start looking up ‘impeachment’ through Buzzfeed in November 2019, you would have no exposure to any politically opposing content featured in other news channels.

As borne out in the above exercise, exposure to opposing political viewpoints is rare for a YouTube user. It is therefore likely that an individual will maintain his or her political viewpoints. This is simply the result of YouTube’s algorithm following its programming and doing its job. Engineers at Google released a paper in 2016, explaining how YouTube has used algorithms for years. A complex machine learning process gathers and analyses data down to each specific user. The result is an adaptive program that presents videos personalized to individual viewing patterns and preferences. This may be fine if you are looking to kill time watching meme videos, but it restricts users from a more expansive world view.

“It isn’t inherently awful that YouTube uses AI to recommend video for you, because if the AI is well tuned it can help you get what you want.” – AlgoTransparency Founder, Guillaume Chaslot

The former algorithm developer for Google and YouTube, Guillaume Chaslot, pointed out the flaws in the video recommendation algorithm. He stated that the primary purpose of YouTube’s algorithm is not to inform or educate viewers, but to capture their attention and keep them on the platform. This encourages the existence of echo chambers within digital communities, continually reinforcing pre-existing views. We’ve got to realize that YouTube recommendations are toxic and it perverts civic discussion,” said the algorithmic transparency advocate, Chaslot at a recent tech conference.

The issue of algorithms enabling echo chambers raises questions about YouTube being an objective platform for political media distribution. We cannot deny the data showing how popular YouTube is. It would be foolish for news organizations to not reach out to a large segment of Americans through this video platform. But with analysis proving the effectiveness of the algorithm and developers acknowledging civic concerns, what is recommended to you will not always be what is best for you. If individuals wish to break through these invisible walls, it would be wise to make a deliberate effort to look beyond them.

Random video recommendation or subtle mental manipulation?

Digging deep into the YouTube algorithm


Who hasn’t come across an absurd YouTube recommendation? You’re watching CrashCourse Philosophy and ending up on a conspiracy about Donald Trump being a lizard. Sometimes videos seem completely irrelevant and there exists a whole channel on Google Supportfor user problems.

YouTube is so powerful that even young children are obsessedwith it. The platform’s recommendations section is constantly trying to find what we would like to watch nextwhilstscanning massive amounts of personal data. With great power comes great responsibility, and we better think twice before blindly trusting the platform.


Trusting the algorithm?

YouTube profiles are designed for crafting and personalisation, using affordances as subscribing, upvoting, creating lists. Then, AI scans user activity, likes, dislikes, previously viewed videos… and all other sorts of personal informationlike phone number, home and work address, recently visited places and suggests potentially likeable video content. YouTube uses this information as a “baseline”and builds up recommendations linked to users’ viewing history.

In 2016, Google publishedan official paper on the deep learning processes embedded in the YouTube algorithm. The algorithm, they write, combines gathered data based on factors such as scale, freshness, and noise – features linked to viewership, the constant flow of new videos, and previous content liked by the user. They provide analysis of the computation processes, but they still cannot explain the glitches commonly found in the system – for instance, why is the algorithm always pushing towards extremes?


The dark side of the algorithm

Adapting to one’s preferences might be useful, but it seems like YouTube is prompting radicalism, as if you are never “hard core” enough for it.

Guillaume Chaslot, founder of AlgoTransparency– a project aiming towards web transparency of data, claims that recommendations are in fact pointless,they are designed to waste your time on YouTube, increasing your time-view. Chances are – you will either get hooked onto the platform or will end up clicking on one of the ads, thus generating revenue. Chaslot says that the algorithm’s goal is to increase your watch time, or in other words – time spent on the platform, and doesn’t necessarily follow user preferences.

It seems like YouTube’s algorithms are promoting whatever is both viral and engaging, and are using wild claims and hate speechin the process. Perhaps this is why the platform has been targeted by multiple extremist and conspiracy theory channels. However, it is important to acknowledge that YouTube has taken measures against that problem.


Our investigation

 Inspired by recentresearchon this topic, we conducted our own expedition down the YouTube rabbit hole. The project aims to examine the YouTube recommendation algorithm, so we started with a simple YouTube search on ‘Jeremy Corbin’ and ‘anti-Semitism’. The topic is completely random and provoked solely by the fact that we are London residents familiar with the news. For clarity’s sake, here is a visual representation of the data (Figure1.0).

On Figure 1.0, we can see the network formed by all videos related to the key terms which will end up in the recommendations section. The network has 1803 nodes and 38 732 edges, each of them representing political videos on current global events and how they relate to one another.


Figure 1.0


Alongside with the expected titles including key words such as ‘Jeremy Corbin’, ‘Theresa May’, ‘Hebrew’, ‘Jewish’, one may notice a miniature cluster far on the left-hand side. It has three components, or YouTube videos, that are, least to say, hilarious. Let’s zoom in.


Figure 2.0


Figure 3.0

At a first glance, they seem completely random and are positioned furthest of the network and are unrelated to whether Jeremy Corbyn is an anti-Semite or not. So, there must be something hidden in the underlying meaning of the videos which makes them somehow relatable. I will refer to the videos in this cluster as ‘random’, however, in the following lines, the reader will be persuaded in the lack of any randomness whatsoever.

The three videos (Figure 3.0) have a vivid variation in content: from a teenage girl that bought Justin Bieber’s old iPhone filledwith R Rated personal material; through a woman who got pregnant by her boyfriend’s grandpa; all the way to the story of a daughter who tried to surprise her mother in jail only to end up in prison not being able to recognise her own mother who had gone through plastic surgery to become a secret spy (???).

It is easy to spot the production similarities between the three ‘random’ videos, nevertheless, they would usually not appear in the same context as they have different topics, keywords, and are produced by different channels. All videos are animated and have a cartoon protagonist that guides the viewers through their supposedly fascinating life story, and all seems made-up. The creators produrces used visual effects to affect human perceptions – animation, fast-moving transitions, exciting background music.


The ‘random’ videos and some commentary. Snapshots: YouTube. Edit is done by the author.

Caricature is the artists’ way of presenting personal opinion on a more radical case. It’s therefore understandable why caricatures often include political figures and international affairs. Further, humour renders the brutality of life easier to handle. Animation has become a tool for distribution and reproduction and is associated with conditions of conflict, both national and international. Since the foregoing videos are associated with extremes, YouTube algorithm suggests what it finds extreme – apparently ‘Jeremy Corbyn’ and ‘anti-Semitism’.

After observing the visual part of the content, we moved on to linguistic and semantic investigation. It is found that words as ‘scandal’, ‘very important people’, ‘controversial situations’, ‘jail’, ‘accusations’ might be the reason why those videos appear in the network related to the key words ‘Jeremy Corbyn’ and ‘Anti-Semitism’.

Interestingly, all three comment sections in the ‘random’ cluster are filled with jokes and general opinion of the videos being fake. Very little of the public believes in the validity of the stories. If we browse comments from nodes with political videos, we can find similar language. That proves that AI not only scans language but detects opinion and irony and links common themes together.

The reader now understands why my area of interest is focused on this particular cluster, as it is a metaphorical representation of the whole network. Eventually, the research proved that Jeremy Corbyn is not perceived as an anti-Semitist by the online public (or by the algorithm).


What is the algorithm suggesting?

To get a better grasp of the common assets in the network, we observe the nodes that are closest to the ‘random’ cluster (Figure 2.0). Following logical conclusions, can we say that the algorithm suggests all those political events are either a scam or a mockery? As the three videos are linked in a network with other, definitely not-so-humorous videos, this means they share keywords, topics, creators, or audience. The algorithm appears to find a similarity between the absurdity of the animated YouTube videos and the nodes closest to the cluster. Could this be the algorithm manifesting its opinion?

Of course, these are all speculations, and factors such as viewership and watch time are not to be neglected. As both viewers and producers, we should also remember that content may be interpreted differentlyin diverse social groups.

YouTube and the Echo Chamber of Secrets

There has been a variety of talk around the topic of how big the impact of social media networks was on elections in recent history. Most of the focus was on the platforms Facebook and Twitter and what role they played in them especially when it came to the topic of ‘filter bubbles’ and ‘echo chambers’ on those websites. However, many of those discussions forgot about one big play when it comes to the life online which is YouTube.

The Google Home Page - Photo by: Caio Resende
The YouTube App on a smartphone- Photo taken from

Social media networks have become a huge part when it comes to how people find their news daily. For instance, research by the “Pew Research Centre” shows that around 65% of the adult citizens in the USA get some of their news via Facebook which a lot people do not like. In addition to people not liking that their feed is full of news stories every day, there are people like Eli Pariser who are actively warning of the effects this development might have on politics and society in general. In one of his ‘TED Talks’, he says that people getting their news from social media will become a big problem because at this moment of time the Internet is showing us what it thinks we want to see and not what we need to see. Thus, it is creating personal bubbles for each person separately which in his opinion will be hard to escape for people

“I’m already nostalgic for the days that social media was just a fun diversion.”

Andrew Wallenstein, Co-Editor-In-Chief of Variety Magazine


An example of the increase of personal bubbles online can be found in the increase in the number of news organizations who are currently working on their own personal news services. Some of the news organization in questions are the New York Times and the Washington Post who both are currently working on their own version of personalized news services for their customers. However, there is already one platform who can do something similar to that which is YouTube. The platform is able to do that by using the data it has gathered on its users while also employing a great team in data analytics who are able to make the most out of that data. One instance of this is the recommended videos feature on the platform who makes use that data.

There are a variety of people who believe that this data usage, however, might also create problems. Kenneth Boyd, for instance, argues that because of that there is a risk that people could end up watching the same content repeatedly on YouTube and thus live in their own echo chamber on the platform. In addition to that, he also says that this is not a problem when it comes to cute cat videos but that it might be an immense problem when it comes to the spreading of misinformation and fake news on the platform

To investigate the claims made by Boyd and Pariser concerning the risk of people ending up in their own echo chamber on things like YouTube. Therefore, this article will have a look at the network’s videos create when one has a look at their recommended videos, which can be done via the help of tools from the Digital Methods Institute. To do that, 3 different media sources from the USA, with different political stances, BuzzFeed News, NBC News, and Fox News,, were used who all released videos around the same topic, the impeachment hearings in 2019.

The network graph of the recommended videos for the Buzzfeed News video “Let's Hear It For The Whistleblower - Impeachment Today Podcast -”
The network graph of the recommended videos for the Buzzfeed News video “Let’s Hear It For The Whistleblower – Impeachment Today Podcast –”

First of all, the above graph shows the various categories of recommended videos YouTube gives it users when it looks up a video on the impeachment hearings on BuzzFeed News. The graph shows that most of the videos people get recommended are videos which deal with the topic of news and politics. However, when one combines this network of videos with the network of videos of FOX News and NBC News, as seen in the graph below, one can see that those videos for BuzzFeed News do not have any connection to the other outlets. Furthermore, when looking at the videos of the other two media outlets one can see that there is an overlap between them. There were four videos, three on the side of FOX News and one on the side of NBC News, which played prominent roles in the network of the other media outlet.

The combined graph of the recommended videos networks for the FOX News video “Tucker's big takeaways from the Trump impeachment saga -” and the NBC News video “Highlights: Fiona Hill And David Holmes’ Impeachment Hearing Testimony | NBC News -”
The combined graph of the recommended videos networks for the FOX News video “Tucker’s big takeaways from the Trump impeachment saga –” and the NBC News video “Highlights: Fiona Hill And David Holmes’ Impeachment Hearing Testimony | NBC News –”

As a consequence of that one can see that both graphs show interesting aspects of how YouTube is making use of its recommended video feature. Firstly, it shows that when people are looking for news, they tend to get news like seen in the case of BuzzFeed News. However, it also shows that there is strong chance that people who only watch BuzzFeed News might just stay in their own bubble because the YouTube algorithm does not recommend them videos from sites like NBC News or FOX News. Moreover, it is also striking to see that this might also be the case with FOX News viewer because even though the graph shows ties between FOX News and NBC News, the videos which tie them together are all videos from FOX News themselves and from no other sources.

“… good idea to consider what is not being shown to you.”

Kenneth Boyd

Because of things like the creation of bubbles as seen with BuzzFeed News and with FOX News, Kenneth Boyd is warning about the risks of relying too much on platforms like YouTube when it comes to gathering news. In his opinion, it is important to hear about an political argument from all sides of the aisle and it is clear that YouTube does not offer that when looking at its recommended video feature.

Nevertheless, it has to be said that, in the end, people cannot start putting blame on platforms like YouTube for not showing people a wider view of videos and creating personalized bubbles and echo chambers but that it is rather the job of each citizen on their own to try to seek out as many diverse sources as possible on political issues and not rely on YouTube or other platforms to do that for them.

Falling down YouTube’s Rabbit Hole: How does their Recommendation Algorithm Work?

Have you ever started watching a video on YouTube and then hours later realise you have fallen into a crazy YouTube rabbit hole? We’ve all been there. But why is it that YouTube can drift us so far away from the original video we started at, and who decides what videos are recommended?

Image: By PixaBay

That would be YouTube’s recommendation algorithm. The software is responsible for what videos are seen in the “Up Next” column and on user’s homepages. Over 70% of all time spent on YouTube by users is spent watching videos recommended by the platform’s algorithm. With that much power over what we watch, we should find out how it works.

There is no public information into the exact specificities of the algorithm, however, according to Google, who created the current algorithm for YouTube made by their Artificial Intelligence company, it works primarily through 2 steps:

1) Personalisation: This is when the algorithm selects videos for the particular user based on videos that were watched by other users who watch similar videos to that user and also are similar demographically.

2) Ranking: This involves the selected videos being put into order of how likely the user will watch them, using data such as how many videos they have already watched on that channel and search queries.

If the algorithm worked perfectly, then every video YouTube recommended would be incredibly interesting for us, however, this was not the case for Nick Brown. Nick is a teenager who spends lots of time on YouTube, primarily watching videos about UK politics. As a strong labour supporter, Nick spends most of his time watching videos about the party’s news. However, one Tuesday evening in December 2019, just two days before the next general election, the teen was an hour deep into a YouTube binge session gathering as much information about the upcoming election, when he was led down a strange path. “On my up next was a Maroon 5 music video” Nick stated. “I’ve never listened to Maroon 5 in my life.”

The music video is very popular on YouTube, with over 2.5 billion views. Despite the video being an obvious hit on YouTube, Nick was not so impressed. “I don’t get it” he muttered angrily, “what does the labour party have to do with Maroon 5?”.

This is a question I have been asking myself since speaking to the young teen. Why would YouTube recommend such an unrelated video? With 500 hours of videos being uploaded every minute, I understand that YouTube has a lot of footage to deal with. However, with YouTube being one of the largest and most powerful online platforms, it seems fitting to investigate what is really going on with YouTubes recommendation algorithm.

In order to uncover the truth behind how the recommendation algorithm works, I embarked on an investigation.

Fitting with the recent UK general elections in December 2019, I decided to look into what YouTube videos would be recommended when I searched ‘Jeremy Corbyn, Anti-Semitism’.

Image: By Pixabay

On December 12th 2019, the UK general election took place, which resulted in the Conservatives winning with a landslide majority. The reason for the extreme result of the 2019 election has been in speculation since it took place, with talks of Brexit and the NHS. However, one factor that may have contributed to Jeremy Corbyn’s extreme defeat is the number of allegations of anti-Semitic behaviour that have surrounded the Labour party recently, resulting in nine members of the Labour party to resign in protest.

I used a digital tool that scraped all videos that YouTube’s algorithm would recommend from my search. 1,803 videos were recommended from my search ‘Jeremy Corbyn, Anti-Semitism’, and I visualised the network of videos in to the graph below.



Screen Shot 2020-01-05 at 15.29.13
Image: by the author 

The different colours symbolise clusters of videos that are similar to each other. As I zoomed in to the blue cluster, I was equally shocked and amused at the video titles that appeared. Incredibly extreme titles such as “I broke my legs to satisfy my mom but it was not enough” or “I Like Older Men So I Got Pregnant By A Grandpa” were present. Despite the insane obscurity of the video titles, I was intrigued to click on them for that very same reason, and I was not alone in that. The videos had millions of views, with thousands of outraged comments and dislikes.

Screen Shot 2020-01-07 at 10.18.34
Image: by the author 

Despite the complete irrelevance to Jeremy Corbyn or Anti-Semitism, YouTube knows that shocking video titles are more likely to get clicked on.

This section of the graph suggests that they specifically include extreme video titles instead of finding actual relevant and personalised videos for the user. Despite the irrelevance of these videos, YouTube are obviously doing something right still, as 70% of all time spent on YouTube is occupied watching recommended videos. Guillaume Chaslot, founder of AlgoTransparency, stated that the recommendation algorithm is often not related to what the individual wants, and focuses on what is likely to get clicked on.

The investigation taught me that the algorithm just assumes that popular videos that are clicked on regularly might satisfy everyone. Pew Research centre found in a study that the 50 videos that were recommended the most times by the algorithm had been viewed over 456 million times each.

My brief investigation suggests that the algorithm isn’t really as clever as one might assume, and mainly focuses on what will get the masses to click rather than what will please the individual.

Did Youtube algorithms convey to voters that there was anti-semitism in the Labour party?

On the 12th of December, the Conservatives won the general election with an 11.2% lead over the Labour party. There has been speculation about what happened leading up to the election. An overarching theory put forward has been that Jeremy Corbyn did not represent the views of Labour voters and that the majority opinion was suppressed by the loud Corbynite minority who advocated for Corbyn’s vision of a socialist Britain.

One way in which Corbyn’s failed leadership has been said to have manifested itself is with his handling of allegations of anti-Semitism within the party. Whilst this was not a damning issue when the Conservatives were accused of islamophobia, Corbyn appears to have been much slower to respond to, and express his disdain for, the behavior than his Conservative counterparts. It was only when the leader was put under pressure that he released a public statement.

Many party members and representatives felt that their leader did not respond correctly to what is a serious issue. The sentiment became so great that in February 2019, nine MPs resigned from the party in protest. Not only was this feeling within the party leaders but also amongst the voters. Labour’s former whip, Graham Jones, said that while on the campaign trail, he encountered voters stating that the antisemitic sentiment in the party was one of the reasons why they would not be voting Labour in the 2019 general election. To the voters for whom this mattered, it would have undoubtedly served to make them feel isolated within their party.

polling station

Photo by Elliott Stallion on Unsplash

Social media was a factor of particular weight in this election campaign. Gephi – a data analysis tool that shows how videos are recommended and the link between them – can help us delve further into this point. Using YouTube as a platform, due to its great popularity, we can attempt to look into the sort of material the voter is being exposed to by way of personalised recommendations. Youtube uses the user’s history in the algorithm in order to give personalised recommendations.The aim of the investigation is to see how these videos are linked and the potential impact it has on the voters.  To specify the search, I used the keywords ‘Jeremy Corbyn’ and ‘Anti-Semitism’.


What does the graph show?  

Within the network, there was a focus on the graph below. This cluster in the network showed the highest volume of videos recommended that were related to the theme of anti-Semitism. Among the name Jeremy Corbyn, the names Theresa May and Nigel Farage appeared several times. This can be attributed to the fact that they were the running opponents to Jeremy Corbyn. The themes that showed up were anti-Semitism, UKIP, and Palestinian conflict.

gephi graph

Image: by the author

Thus, from this evidence, we can move to infer that – although it does no make a conclusive case that the videos impacted the voters, from the videos – we can see how the recommendations were centred around the topics above. Further to this, the YouTube recommendations move past the title and are also based on the content of the videos. For example, in the BBC Newsnight interview, there was discussions on the Labour party’s stance on the Israeli-Palestinian conflict. This video was one of the main nodes within the network. The words ‘Israel’ or ‘Palestine’ did not appear in the title. From here, the algorithm then recommends a video with ‘Palestine’ in the title. This suggests that the algorithm also considers the content as well as the title.

The central node in this cluster is The Nigel Farage Show, where he discusses his stance on Jeremy Corbyn. When the tabloid began running the anti-Semitism story, this gave Farage ample opportunity to attack his running opponent. Several videos within the cluster are opponents who are publicly speaking out against the candidate. From a voters perspective, this exacerbates the story. If there was any doubt of the candidate’s stance on the issue, the recommendation algorithm creates a higher shadow of doubt by presenting strong sentiment from other leaders. Thus, the negative perception fostered by the initial video which was likely directly about the issue, is further developed by the recommendations of videos with Corbyn’s opponents berating him for it.


What does the network show?  

This network demonstrates the power that social media algorithms can have on the voters. Digital campaigning has become an integral part of political campaigning globally, and has been utilised during this general election. While analysing the recommendation algorithm, one must consider to what extent these platforms are working against the politicians. In the case of the cluster examined, the specific data points, in relation to the topics recommended, evidently served to hurt Corbyn’s public perception. Any voter with doubts about the magnitude of the anti-semitism allegations would have subsequently been pushed further videos discussing it; likely with opposition politicians giving their opinions.

The existence of, or extent of, the anti-semitism within the party is not in question nor the issue. The point of this investigation is to analyse to what extent Youtube’s algorithm can influence the voters decision. Platforms like YouTube are undoubtedly a large content provider for voters in the modern age. From the information gathered, it appears that the recommendations are indeed based off similar themes within either the title or the content; thus, where a video with an opinion is viewed or searched, this might produce recommendations that serve to reinforce that opinion.

However, it also undoubtedly works on the contrary. So positive Jeremy Corbyn videos would be recommended if one searched for this. Ultimately, in tense political climates, the negative sentiment is always repeated and highlighted and in this case, YouTube algorithms facilitated the promotion of content that pushed onto voters the discussion about anti-semitism within the Labour Party.

Hey Google, do you think I’m beautiful?

Screenshot of Google search engine: By Author


Beauty is in the eye of the beholder. However, it seems Google Image’s search engines have their own ideas of what is “beautiful”. “This is something we should care about” urges Safiya Umoja Noble, a professor of Information Studies and African American Studies, regarding the racial biases evident in some of our most trusted search engines. In a TED talk at the University of Illinois, she explains; “Search engines are an amazing tool for us, but what we don’t see is the ethical biases that are inherently built into them”. Umoja Noble, who is African American, recounts an anecdote based on her friend’s experience with Google Image search: “When she did a search for ‘beauty’: this is what came up”. The screen fills with images of young, white women. Looking concerned, she explains that this is reinforcing the social biases of society: “they get replicated in our search engines”. How are we, as a society, to combat racial inequality if the tools that we depend on the most, inherently reinforce these barriers? Is this an algorithmic issue, or simply a reflection of the views of Google as a company?

Screenshot of video recording of Safiya Umoja Noble’s TED Talk available: : By Author

Unsurprisingly, this is not the first time Google search algorithms have come under fire. In a piece by Mind Matters, an ex-Senior Google Search Engineer, Gregory Coppola, contacted a watchdog group (project veritas) to warn users about political biases in Google’s search engine results. He states: “No private company should have either the right or power to manipulate large populations without their knowledge”. I reached out to Coppola and asked him what his approach would be to identifying biases in a search engine such as Google. In an exchange on the issue, his response was to the point – all of it is biased.

Screenshot of personal correspondence with Gregory Coppola : By Author

In his blog, Coppola proposes “Coppola’s Law” which states “the social bias in a software product is the social bias of the organisation that produced it”. And Coppola isn’t the only one with concerns. Gabriel Weinberg, CEO of rival Duck Duck Go and critic of Google search engines: “This filtering and censoring of search engines and news results in putting users in a bubble of information that mirrors and exacerbates ideological divides” quoted in The Observer.

Google search engines have a lot of critics, but then if they are so bad, why are they so popular? According to Google processes 1.2 trillion searches per year worldwide – that’s 1.2 trillion opportunities for reinforcing (or addressing) social divides worldwide. Surely something we are so dependent on cannot be as big a social evil as it seems? So says Google’s CEO Sundar Pichai. According to CNBC In 2018, he appeared in a hearing before US Congress to explain how Google’s algorithm worked, including the results that it favoured. He was vigorous in his defence of the search tool. “Getting access to information is an important human right” explained Pichai. But core principles of human rights are equality, non-discrimination and respect for the worth of every human, irrespective of race and culture. Search results that suggest beauty is confined to Caucasians simply to do not bear that out.

We decided to see the racial biases in Google Image search engine for ourselves and carried out a small practical experiment. We typed the search term “beauty ideal” in 6 different languages (English, Irish, Arabic, Indonesian, Japanese and Korean) in the Google Image search tool that most digital natives use for just about anything. The aim was to identify the different representations of racial groups that each translation of the term produced through the search engines ranking system using the Google Image Scraper tool.


These are some of the top search results from each language:

Screenshot of a sample of the search results used in the project : By Author

At first glance, the top results seem to be heavily representative of young, thin, white women which is a backwards step of beauty standards in many ways. The graph below shows the representations of racial groups in the first 20 results of each query:

Graph showing representation of racial groups in the search results: By Author

According to Google, being white seems to be a “beauty ideal” across language and culture. What’s more disturbing is that some races are not even represented in some of the top search results! This is unrepresentative of the diversity in society’s actual views, as “beauty ideals” are unique, not just to individual opinion, but cultures and ethnicities. How challenging will it be to secure equality in society, when one of our most used tools reinforces some of the very barriers that we are trying to break down?

In Copolla’s approach, can we assume that Google, a worldwide multi-billion dollar tech giant, is racially biased against certain groups? Or would these results change if I wasn’t a young, white woman myself and these results are simply what Google thinks that I want to see based on the data it has on me? The mystery of how Google’s search algorithms work makes identifying what the causes of this misrepresentation of certain racial groups difficult to uncover. But, as far as I can see; one thing is for sure – there is a lack of diversity in the image representations of “beauty ideal”.

Does this make Google racist, or is it just a user-pleaser? What about representations of gender, sexual identity or age? There are so many areas for problems to occur in search results, especially in something as subjective (and arguably vague) such as a “beauty ideal”. Search engines like Google cannot please everyone. Something they can work on however, is keeping up society’s standards of inclusion and equality.

Although we don’t need Google to tell us if we meet the standards of beauty, there is something to be said about the lack of representation in digital media’s depiction of beauty. Google is used by everyone, so everyone should feel represented. This may seem like a challenge, but Google is one of the greatest innovators – this one should be a breeze. Come on Google, aren’t we all beautiful?