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Dave Gale's blog on finance from June 2022

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Dave Gale's blog on finance from June 2022

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As part of the AMSP’s Core Maths Summer Conference, I’m presenting a small slot on the financial aspects of the course. This blog post is just to support that session but may well be useful for others too.

The expensive brownie

£10 for Pensioners

£1 000 000

Would one million pounds be enough money for someone to retire at the age of 50?

Car vs Moped

Get students to compare the cost of buying and running a car versus a moped.

APR and AER

A reminder that these are not big parts of the course.

There are several Core maths videos I’ve made in this playlist here.

Buying a House

Using Rightmove, Compare the Market and a Mortgage calculator website, what sort of job might you need to afford the house you want?

National Insurance and Tax

Could be an interesting year as the tax bands are the same as last year for Income Tax but the National Insurance bands are different and also change part way through the year!

An older blog post about this.

This Income Tax video will still work.

This National Insurance video has the right concepts but the numbers will change.

What interesting questions could be asked in the exam (or class) based on this?

Hopefully that’s enough ideas to inspire you. Feel free to follow this blog to stay updated and I’d welcome any comments.

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Interpretation of data, statistics, graphs, infographics in context, critiquing graphs, analysing and comparing data in order to develop and present a conclusion, making assumptions, making predictions and reading scales

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MetLink - Royal Meteorological Society Core Maths - Trees and Carbon Capture -

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What is the maximum number of trees that can be planted in one square kilometre?

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What is the maximum number of trees that can be planted in one square kilometre?

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A website dedicated to the puzzling world of mathematics.

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HOW MANY TREES ARE SOLD EACH YEAR?
It is estimated that 6 - 8 million Christmas trees are sold in the UK each year.
HOW HAS CORONAVIRUS IMPACTED THE INDUSTRY?
Due to Covid19, Christmas tree growers have adapted how they will sell this year to ensure everyone's safety; introducing social distancing measures, sanitisers, one-way systems, payment by card only etc. so consumers can still enjoy the experience of choosing their own tree. Some growers have also introduced online orders and deliveries and click and collect services.

Already, early orders for UK trees from garden centres and farm shops are significantly ahead of 2019 and this year's sunny weather means Christmas trees are in the best condition.

HOW OLD IS A TYPICAL FULL HEIGHT TREE FOR THE HOME AND WHAT CARE GOES INTO IT THROUGHOUT THE GROWING STAGES?
A typical 6 to 7 feet high Christmas tree is between 10 and 12 years old. Seed is collected from trees either in the wild or in specially selected seed orchards and sown in a nursery where the seedlings then grow for three to four years. The young plants are then planted by a grower and grown on for a further seven to nine years.
The grower must fertilise, shape and prune the tree many times each year, and keep it healthy and beautifully shaped, ready for the customer to enjoy.

HOW CAN I FIND OUT WHO STOCKS A TREE IN MY AREA?
Visit www.bctga.co.uk to access a list of BCTGA members.
WHAT ARE THE DIFFERENT TYPES OF CHRISTMAS TREES?
Nordmann Fir - This is the most popular Christmas Tree in the UK as it has excellent needle retention, with lush, dark green needles. They are symmetrical trees with strong branches, great for displaying ornaments.

Fraser Fir - These trees have great fragrance with dark green, needles that are silvery underneath. Good needle retention and a pyramid-shaped, strong branches which turn upward.

Noble Fir - Ideal for great needle retention and a fresh fragrance. With bluish-green needles and short, stiff branches; great for heavier ornaments; keeps well.

Douglas Fir - probably the strongest scent of all Christmas trees, very popular in the USA.

A true fir with great shape but a little harder to decorate with baubles as usually much denser.

Norway Spruce - Keep them well watered for good needle retention and enjoy this traditional Christmas tree which is dark green and has a strong fragrance along with a great conical shape.

Blue Spruce - Beautiful, unique blue colouring, needs watering well and has good stiff branches, great for hanging heavy decorations. An ideal small or second tree.

Lodgepole Pine - A low needle-drop tree with beautiful tapering branches, that rarely lose their needles if well hydrated. The needles are green/yellow in colour. The traditionalists Christmas tree.

Serbian Spruce - Tall and slender, with graceful upswept branches. It has glossy dark green needles with slender streaks of white, a very good ornamental alternative.

WHICH IS THE MOST POPULAR VARIETY?
BCTGA estimate about 80% of the trees sold are Nordmann Fir, around 10-15% Norway Spruce, and the remainder are made up from the other varieties.
WHY SHOULD I BUY FROM A BCTGA MEMBER? WHY BRITISH?
Buy a British grown tree to support the local economy, agriculture and the environment. Buying British means money is going directly back into the country's economy and helping provide employment in the agricultural sector. It's also good for the environment, Christmas trees provide shelter for birds and wildlife while the trees are growing.

All BCTGA members are asked to comply with a code of practice so that British Christmas trees are grown to the best environmental and sustainable practice. Members aim to grow quality Christmas trees that with the appropriate care will survive the duration of the festivities.

HOW DO I KNOW IF MY TREE IS A BCTGA GROWN TREE?
Buy directly from our growers or ask your supplier where the tree has been grown.

REAL OR ARTIFICIAL?
A real Christmas tree bought locally has the lowest carbon footprint.

The Carbon Trust say a real Christmas tree has a "much lower" carbon footprint than an artificial tree, particularly if it is disposed of thoughtfully.

They state that a natural two-metre Christmas tree without roots, disposed of into landfill generates a carbon footprint of around 16kg of CO2.

If the same sized tree is disposed of by burning it on a bonfire, planting it or having it chipped to spread on a garden, it will have a carbon footprint of around 3.5kg of CO2 - an impressive four and a half times less carbon footprint.

A two-metre Christmas tree made from plastic has a carbon footprint of around 40kg of CO2, over 10 times greater than that of a real tree, disposed of properly

Unlike artificial trees, a real Christmas tree naturally absorbs CO2 and releases oxygen and the Soil Association also highlights how a real tree provides a habitat for wildlife and captures carbon from the atmosphere during the 10-12 years they take to grow.

Unlike artificial trees, real trees can also be recycled. Many BCTGA members and local councils across the country offer Christmas tree collection services, where used Christmas trees are picked up and recycled.

There is no need to worry about deforestation when buying a natural Christmas tree, because the majority are grown by BCTGA members as a horticultural crop and are not felled from pre-existing forests.

When a Christmas tree is cut down, it is immediately replaced by another seedling, with up to 10 trees being planted for every average size tree that is grown.

HOW DO I KNOW I'M CHOOSING A FRESH TREE?
A fresh tree will have a healthy green appearance with few browning needles. Needles should be flexible and not fall off if you run a branch through your hand. Raise the tree a few inches off the ground and drop it on the butt end. Very few green needles should drop off the tree but it is normal for a few inner brown needles to drop off.

HOW DO I KEEP MY TREE AT ITS BEST?
When the tree is brought home, it is advantageous if about half an inch is cut off the trunk in order to open up the pores of the tree. The tree should be kept outside in a cool shaded place, standing in water, until it is required indoors. When the tree is brought indoors, mount it in a water-holding stand and place it away from direct heat, such as a radiator. Keep the container topped up with water every day; you will be surprised how much the tree drinks.

HOW LONG HAS THE BCTGA NATIONAL COMPETITION TAKEN PLACE?
The BCTGA was established in 1980 and its annual competition began in 1998.

WHEN WILL THE WINNER BE CHOSEN AND WHAT DO THEY WIN?
The competition takes place in October, with the winners taking the title of "Christmas Tree Grower of the Year" and "Champion Festive Wreath".
Open to Association members, each tree and wreath entered is assessed and the winners are traditionally granted the honour of delivering an 18' 6" tree to stand outside No. 10 Downing Street and a wreath to hang on the famous door.

FUN FACTS
1. Every year since 1947, the tree in London's Trafalgar Square has been a gift from the city of Oslo, Norway.
2. In a 2004 survey of the nation's favourite smells, real Christmas trees came eighth just behind the sea but ahead of perfume.
3. Manufactured Christmas tree ornaments were first sold by Woolworths in 1880.
4. Even before the time of Christ, evergreen trees were seen in winter as a symbol of fertility
5. The 16thcentury monk, Martin Luther, is credited with the idea of lights on Christmas trees by adding candles to his tree to look like stars in a forest.
6. England's first Christmas tree was brought to Windsor by Charlotte, wife of George III, in 1800...
7. ...but it was the trees brought in the 1840s by Prince Albert, consort of Queen Victoria, that led to their popularity throughout the UK.
8. The first use of the term 'Christmas tree' in English was in 1835.
9. The world's tallest Xmas tree at 221ft high was erected in a Washington shopping mall in 1950.

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HOW MANY TREES ARE SOLD EACH YEAR? It is estimated that 6 - 8 million Christmas trees are sold in the UK each year. HOW HAS CORONAVIRUS IMPACTED THE INDUSTRY? Due to Covid19, Christmas tree growers have adapted how they will sell this year to ensure everyone's safety; introducing social distancing measures, sanitisers, one-way systems, payment by card only etc.

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A Victorian house style emerged during the reign of Queen Victoria (1837 to 1901) during the 19th century. It's important to note that Victorian architecture technically refers to the era and not the design style that we most associate these homes with.

British architectural styles of the time included the Italianate and the Queen Anne, which were popular during the Victorian era.

What are the characteristics of Victorian Style?

The architecture from this era embraces steeply pitched roofs, wraparound porches and most homes included cylindrical turrets and bay windows.

One particular feature that became popular during the Victorian era of architecture was the European influenced gothic revival style using ornate and decorative wooden trim. It's sometimes described as a 'gingerbread' style. It became prevalent in the architectural side of design as well as the interior through furniture.

Victorian houses also adopted the Queen Anne style which was another point of difference during this era, particularly so in higher classes. The architecture we most identify with is the larger Victorian homes - a little more imposing and somewhat elaborate.

How high are Victorian ceilings?

In many Victorian homes ceiling height averaged around nine feet. High ceilings in a Victorian home were, on some level, a way to display wealth to visitors.

In the middle and upper classes, the idea was to provide a feeling of spaciousness to oppose the smaller cottages and lower square footage of more modest houses.

How many floors do Victorian houses have?

A typical Victorian house usually includes two to three stories. Each floor housed a number of rooms with definitive functions - more on that further into this post.

Two to three stories was fairly standard, with a basement or cellar as well.

It's worth pointing out that homes from the Victorian era still included small single roomed cottages and much less grandeur houses than the classic Victorian style we're mostly referring to here.

What colours were Victorian houses painted?

The interior of Victorian homes favoured rich dark colours. Ornate and detailed decoration was prevalent and included patterned wallpaper, velvets and texture.

Interior walls were painted with deep shades of burgundy, red and maroon. Brown, green and blue hues were also present. Rooms tended to be on the darker side with busy decoration.

The exterior of the homes tended to be a trial for colour. Victorians weren't shy to try out brighter and sometimes pastel shades. That said there were still subdued tones present.

What rooms did Victorian houses have?

For a grander three storied home, each story would have a set of rooms reserved for certain functions.

1. The top floor would typically house the servants quarters and the children's rooms with a nursery.

2. The second floor would include the master bedroom and the second bathroom.

3. On the first floor you'd find the drawing room or parlour, which acted as a receiving room for guests and visitors.

4. The basement, or cellar, or sometimes, the back of the first floor would house the scullery, where the cleaning would take place. This room would accommodate a big copper pot for boiling clothes and other cleaning equipment.

Rooms would be closed off and only those that guests would need to use, would be visible. The interior of the homes don't tend to function for the way we currently live today. Restored Victorian house interiors will be opened up more to account for our open plan and more interactive living.

Why do Victorian houses have turrets?

Victorian architecture often included a turret - smallish, cylindrical towers attached to the corner of the home. It's a hallmark feature of these homes that distinguishes them from other British styles.

Originally, turrets were used in castles as a defense mechanism. They were built with thin slits so an archer could shoot arrows at approaching enemies.

They fell out of favour as warfare moved on, but a revival during Victorian times had them back in fashion during the 19th century.

How were Victorian homes decorated?

The Victorian style was an abundance of rich, dark colours and heavily patterned wallpapers. House plants and garlands featured heavily as did ornate tapestries and fleur-de-lys.

Other features of the interior decor included:

Embossed and textured wall paper

Highly intricate wood-worked furniture and decorative frames were strong features

Gold coloured tassles on opulent soft furnishings

Heavy fabrics with deep colour-ways

Gothic influence was present throughout

An abundance of accessories and knick knacks were popular.

In fact it would be fair to say that 'more was more' and it's essentially the opposite of today's more minimalist interior approach. The aesthetic was of a somewhat cluttered look and feel.

Where are Victorian houses most popular?

Britain, of course, is where the architectural style of these homes originated, therefore it makes sense that they're found throughout England, and also within British-settled colonies around the world.

In America, Victorian house styles are found in San Francisco - we would be remiss not to talk about the Painted Ladies that depict the Queen Anne style of Victorian home. This narrow and tall design is quintessential of the Victorian styles that we're most familiar with.

Parts of Kentucky in the United States contain the highest volume of restored Victorian style homes today.

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A Close Look at the Victorian Style House

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A Victorian house style emerged during the reign of Queen Victoria (1837 to 1901) during the 19th century.

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What is the most environmentally friendly choice: a real or an artificial Christmas tree? Or is there an even more sustainable festive option?

Christmas trees are a European tradition stretching back hundreds of years. But in this century, questions have arisen about whether this tradition is truly sustainable. Cutting down trees is bad, right? And using artificial trees can surely not be good for the environment? So what's the answer?

For many years, the chopping down of real Christmas trees was considered detrimental to the environment. But the trees used for Christmas - typically an evergreen conifer, such as a fir, spruce or pine tree - are usually sustainable crops. Typically, for every tree harvested for Christmas, one or more will be planted in its place.

Today, the artificial tree is most likely to painted as the bad guy. It is mostly made from a plastic called PVC that creates emissions in its manufacture and, unlike a real tree, is not biodegradable. There is also the cardboard required for packaging and the emissions generated as they are shipped across the globe from factory to your home.

The carbon footprint of a Christmas tree

According to the Carbon Trust, artificial trees also have a higher carbon footprint. It estimates a two-meter-tall artificial tree produces about 40kg of carbon dioxide equivalent (CO2e), while a similar sized real Christmas tree, with no roots, creates 3.5kg CO2e - more than 10 times less.

But there's a catch. You must dispose of the real tree by burning it on a bonfire or by shredding and spreading it over the garden. If the real tree ends up in landfill, its carbon footprint jumps to 16kg CO2e as it will decompose anaerobically and produce methane gas, which around 30 times more potent as a greenhouse gas than carbon dioxide.

Artificial trees are not all bad. They are often cheaper and are flame retardant, which is important for trees used in public places. And, if you have an artificial tree and re-use it for at least 10 Christmases, then the carbon footprint over its lifetime will likely be lower than that of a real tree.

Are potted trees the most sustainable Christmas tree?

There is an alternative: potted trees, which can be replanted after the festive season and re-used year-after-year.

"The best thing you can do at Christmas is to keep your tree alive and breathing as it will naturally absorb CO2 and release oxygen. So potted trees are the most sustainable option, but they need looking after," says Anja-Lea Fischer, Group Head Environmental Performance at Zurich Insurance Group.

If you don't have a garden to replant your potted tree, then there's another alternative: hire a real Christmas tree. Many garden centers and plant nurseries offer this service, they then replant the tree and re-hire it for the next Christmas.

"The debate over what type of Christmas tree is the friendliest to the environment comes up every year," says Fischer. "But there are other aspects of Christmas with more damaging impacts on the planet, such as the increase in flight travel, the resources required for gifts and their packaging, the mountains of waste, and even the holiday dinner.

"When it comes to your Christmas tree, focus on what best suits your lifestyle, meets your family traditions and, most importantly after this difficult year, makes you happy."

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Real vs. artificial: What is the most sustainable type of Christmas tree?

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What is the most environmentally friendly choice: a real or an artificial Christmas tree? Or is there an even more sustainable festive option? Christmas trees are a European tradition stretching back hundreds of years. But in this century, questions have arisen about whether this tradition is truly sustainable.

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Forestry England's guide to choosing the perfect Christmas tree

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Christmas comes but once a year and choosing your Christmas tree is undoubtedly one of the highlights of the festive season.

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Christmas Tree Statistics UK 2022 - When Do We Put Them Up?

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From crowning the UK’s most festive city to discovering tree colour preferences & which date to put it up, we share all of 2022’s latest Christmas tree statistics!

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Some of the figures mentioned in this video have changed since the video was made. The following figures are the rates and thresholds for 2022:

The maximum maintenance loan is now £12,667

Graduates repay 9% of everything they earn over £27,295

The rate of inflation is currently 10%

Due to high inflation, the rate of interest was capped at 6.5% in December 2022.

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AQA 2A AMSP Core Maths platform resource on Social Media Influencers

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AQA 2A AMSP Core Maths platform resource on Social Media Influencers

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integral mathematics resources: log in to the site

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Integral Mathematics Resources: Log in to the site

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AQA 2B AMSP Core Maths platform resource on Social Media Influencers

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AQA 2B AMSP Core Maths platform resource on Social Media Influencers

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Integral Mathematics Resources: Log in to the site

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AQA 2C AMSP Core Maths platform resource on Social Media Influencers

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AQA 2C AMSP Core Maths platform resource on Social Media Influencers

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Integral Mathematics Resources: Log in to the site

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Slow reveal graph on social media and depression

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Slow reveal graph on social media and depression

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Social Media and Depression - Slow Reveal Graphs

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Is there a link between social media and depression? Individuals who reported having high depression symptoms also reported using social media more often. Click to access the slide deck, and learn more about math & content connections.

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Slow reveal graph on social media platform popularity

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Slow reveal graph on social media platform popularity

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TikTok is Hitting Revenue Milestones at Lightning Speed graph from Chartr; slow reveal by Jenna Laib (@jennalaib)

( Slide Deck includes notes and questions to elicit discourse)

Type of Graph: line graph

Source: Chartr Daily, "TikTok is Hitting Revenue Milestones At Lightning Speed." Instagram: July 6, 2022.

Paired Texts: Ortiz-Ospina, Esteban. " The Rise of Social Media." Our World in Data: September 18, 2019.

Potential Math Content: line graph, slope, rate of change, piecewise functions, linear growth, exponential growth

Potential Content Connections: culture, pop culture, social media

K-8 Math Specialist near Boston, MA View all posts by Jenna Laib

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Slow Reveal Graphs: Social Media Revenue

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TikTok is Hitting Revenue Milestones at Lightning Speed graph from Chartr; slow reveal by Jenna Laib (@jennalaib) ( Slide Deck includes notes and questions to elicit discourse) Type of Graph: line graph Source: Chartr Daily, "TikTok is Hitting Revenue Milestones At Lightning Speed." Instagram: July 6, 2022. Paired Texts: Ortiz-Ospina, Esteban.

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Our world in data - the rise of social media

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Our world in data - the rise of social media

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Social media sites are used by more than two-thirds of internet users. How has social media grown over time?

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Using Social Media Data to Capture Emotions Before and During COVID-19

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Using Social Media Data to Capture Emotions Before and During COVID-19

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Introduction

Most people now use social media platforms to interact with others, get informed, or simply be entertained. During the COVID-19 pandemic, social lives moved online to a larger extent than ever before, as opportunities for face-to-face social contact in daily life were limited.

In this chapter, we focus on what can be learned about people's emotional experiences and well-being from analyzing text data on social media. Such data is relevant for emotion research, because emotions are not only internal experiences, but often social in nature: Humans communicate their emotions in either verbal or nonverbal ways, including spoken and written language, tone of voice, facial expressions, body postures and other behaviors. Emotions are often triggered by social events: we are sad when we miss someone, happy when we meet loved ones, or angry when someone disappoints us. Emotions also provide important social signals for others, informing them of adaptive ways to interact given their own motivation and goals. Given their valuable social function, emotions are regularly shared with other people and thereby influence other people's emotions. For instance, happiness may spread through social networks, and give rise to clusters of happy and unhappy people.

Social media continuously captures communication between millions of individuals and large groups over long periods of time. Data from these platforms provide new opportunities to trace emotions and well-being of individuals and societies at new scales and resolutions. This has motivated researchers to use social media data to investigate questions around mental health, emotional well-being, anxiety, collective emotions, or emotion regulation.

In the following, we provide an introduction to how emotional trends in society at-large can be measured using text data from social media. We describe two studies assessing whether this social media approach in the United Kingdom (UK) and Austria agrees with surveys on short-lived emotional experiences. We also briefly illustrate their application to long-term experiences like well-being or life satisfaction. We then provide an example from the COVID-19 outbreak to illustrate how social media text analysis can be used to track emotions around the globe. Finally, we discuss the advantages and disadvantages of social media emotion measures as compared to self-report surveys.

The language that people use to talk about their own and others' emotions on social media provides a possible window into their experiences. In this section, we discuss different methods for assessing emotional expressions from text, including their most important strengths and weaknesses.

Dictionary-based methods

One simple way of assessing emotional expressions developed by psychologists are emotion dictionaries, that is, lists of words that are usually associated with a particular emotion or emotional dimension. For example, a dictionary of positive emotions could include words like accept, beautiful, carefree, easiness, trust, and hope. In contrast, a dictionary for sadness may contain expressions like dull, cried, gloomy, heartbreak and miss. The dictionary approach is based on simple word counting: the higher the percentage of words associated with an emotion, the more a text is thought to express this emotion. In this so-called "bag-of-words" approach, the order or context of words is largely ignored.

Researchers have developed dictionaries for discrete emotions (e.g. anxiety, sadness, anger), as well as dictionaries for dimensions of emotions such as valence, arousal and dominance. The expert word lists of LIWC, in particular, have been manually translated and evaluated in many different languages, such as Chinese, French, Spanish, or German, making them particularly suitable to investigate emotions around the globe. Other approaches that only distinguish between negative vs. positive sentiment are SentiStrength and VADER. They also use counts of emotional words, but additionally assign weights to words to indicate the strength of sentiment, and further apply rules to account for other text features like exclamation marks, modifiers like "very" or negation of emotional words such as "not happy". These additional strategies make SentiStrength and VADER less sensitive to word ambiguities. Lexicon- and rule-based approaches are referred to as unsupervised methods, because they do not require training on datasets of text examples with emotion labels.

Machine learning methods

Machine learning methods for emotion classification originated from Natural Language Processing research in Computer Science. Among them, the so-called deep learning models or neural networks have the advantage of being able to consider not only word frequencies, but also information such as word order and other features of the context. The usual approach for emotion classification in machine learning relies on supervised methods, which require datasets of annotated texts with emotion labels for model training. These text labels are referred to as "ground truth", and try to capture how humans would most likely interpret or express emotions in text. To train machine learning models with such a dataset, the texts contained in it need to be transformed to a numerical representation. This can be done through word embeddings or be constructed from unweighted or weighted frequencies for single words or short sequences of words (n-grams) or from index positions of words in vocabulary lists. Current state-of-the-art machine learning models for emotion classification are deep learning models: These models include an unsupervised first training step, during which they learn contextual embeddings, that is, information about word order and context, on large bodies of text without labels from general sources such as news or Wikipedia. This general training step involves, for example, learning to predict words that have been masked in sequences or predicting if a sentence follows a previous sentence (e.g. the models BERT or RoBERTa). In a second supervised training step, these models are adapted ("fine-tuned") to the particular data source and classification task by running the word embeddings of the training data set through the pre-trained model and only tuning the final layer to predict the labeled classes for all text items (e.g., Twitter postings with emotion labels).

While these deep learning models have the advantage of using most of the information available in text, they have the disadvantage of being black boxes that make it hard to explain why they predict a particular emotion for a particular text. This makes it difficult to check for systematic errors. This, in contrast, is very easy with dictionary-based methods as well as simpler machine learning models based on word frequencies as numerical representations. Yet, such approaches can often catch words in contexts where they do not express an emotion and fail to distinguish between ambiguous meanings of words. However, under the right conditions, when these errors are not systematic and there is enough data (e.g. for population-level emotions), or after removing ambiguous words, dictionary- and frequency-based methods can still lead to satisfying results.

We assessed how social media measures for emotions at the level of societies are related to self-reported emotions and life satisfaction in three case studies. They analyzed Twitter and survey data, collected at a weekly and daily frequency in the UK and Austria, respectively.

Weekly emotion measures from the United Kingdom

In our study, we correlated weekly emotion reports with both dictionary and machine-learning emotion measures based on the text of 1.54 billion tweets from users in the UK. We chose social media emotion measures that correspond to three emotions assessed in the survey: sad, scared and happy. We used both the English LIWC dictionaries for sadness, anxiety and positive emotions, and the most closely related emotion labels from a supervised classifier based on RoBERTa (sadness, fear and joy). We trained the RoBERTa model to categorize emotions in a dataset of affective tweets from the SemEval'18 emotion classification competition before predicting emotion labels on our dataset. For more details on model training and accuracy, refer to the Supplementary Information (SI) of our manuscript.

The correlation of social media and survey emotions seems particularly high for the negative emotions of sadness and anxiety.

Figure 4.2 shows the time-series of emotion reports in the survey and emotion scores calculated based on Twitter data, for both the dictionary and the machine-learning approach. It depicts our analysis separated into the historical period, for which data already existed when we pre-registered our analysis, and the prediction period, for which data did not yet exist at that moment. The x-axis depicts the gender-rescaled proportion of emotion reports in the survey, as well as the proportion of all tweets per week containing emotional terms, or labeled as emotional by our model. To make the time-series visually comparable, the figure presents a z-score for each proportion, calculated by subtracting the mean and dividing by the standard deviation of each time-series. Both social media and survey measures of sadness and anxiety clearly increased during the first COVID-19 outbreak for a relatively long time period. The proportion of tweets with positive emotional expressions on social media changed less, whereas tweets labeled as joy by our machine-learning model, as well as self-reports of being happy, experienced some sharp drops during the outbreak. We discuss emotional responses to the COVID-19 pandemic in detail in the case example later in this chapter.

Importantly, we observed high correlations between self-reported sadness and anxiety with Twitter emotion scores for both the historical and the prediction time period (see Figure 4.2). These correlations were particularly high for time periods that included large variations of emotions, such as the historical period that included the start of the COVID-19 pandemic. In most cases, the correlations were similar for dictionary and machine-learning based emotion scores. One notable exception, however, was for the happiness self-reports, which correlate more strongly with the machine-learning score for joy, than the LIWC dictionary-score for positive emotions. In the prediction period, the correlation with the positive emotions dictionary-score was non-significant.

In most cases, correlations were very similar when not re-scaled for gender. Yet, especially in cases where correlations were weaker (i.e., in the prediction period for the scores LIWC anxiety, supervised fear and LIWC positive emotions), rescaling for gender improved the correlation. Rescaling for gender may make the measures more representative, and remove the gender bias present on Twitter, since tweets posted by male users account for more than 60% of tweets with gender detected in our sample.

The degree of association that we observed between self-report and Twitter data is comparable to correlations among subjective variables detected in past research, such as surveys of political attitudes. While social media measures of emotions are not perfect, this analysis demonstrates that they provide a useful complementary source of information about the emotional state of a population. The relationship between social media and survey emotion measures becomes most visible in times of large variations of emotions, such as during the COVID-19 outbreak.

The correlation of social media and survey emotions seems particularly high for the negative emotions of sadness and anxiety. The supervised emotion classifier for joy also revealed good results, while the LIWC dictionary for positive emotions did not. This could be attributed to the LIWC positive emotion dictionary not being specific to a particular emotion, but including a very broad range of positive terms (ranging from handsome to heroic, yummy, intelligent, value or bonus). In contrast to this dictionary, the classifier label "joy" maps directly to the emotion assessed in the survey, which likely explains the higher correlation. Additionally, this could also reflect a dissociation between positive verbal expressions and subjective states: People may use positive words as a way to bond with others or reassure them rather than to express their emotions, perhaps particularly so in negative situations. A similar deviation between positive emotional expressions on Twitter and self-reports has also been found in previous research on population-level life satisfaction and affective well-being.

In addition to the above analyses focused on correlating social media with survey emotions, we investigated if social media emotion levels would reveal potential gender-differences in response to COVID-19. Gallup World Poll data show that women experienced worry and sadness more often than men in the years before the COVID-19 outbreak. The proportionate increases under COVID-19 were significant for both genders, and slightly larger for females. We analyzed social media emotion levels and changes to test if they replicate these patterns. To do this, we first calculated the proportion of tweets by women and men that expressed anxiety or sadness on Twitter in a pre and post COVID time period. Given that attention on Twitter quickly shifts to novel topics, we used a short COVID-specific time period instead of yearly emotion levels reported for the Gallup World Poll: We compared the first ten weeks after the COVID outbreak in the UK in 2020 to a baseline period at exactly the same time in the year 2019. These ten weeks start with the day with 30 confirmed COVID-cases, namely March 1st, and end with May 10th, thereby excluding tweets linked to the Black Lives Matter protests toward the end of May 2020.

Time periods include data from 1 March to 10 May in 2019 for the pre-COVID period and in 2020 for the period during COVID. LIWC denotes the dictionaries with anxiety and sadness words, and RoBERTa the deep learning model used to predict emotion labels.

Table 4.1 summarizes the results. The proportion of male and female Twitter users in the UK who expressed anxiety-terms was similar during the baseline, and increased slightly more in women (by 23%) than in men (by 14%) during COVID-19. The percent of users expressing fear, according to the RoBERTa model, was slightly higher pre-COVID among women (6.46 vs. 6.04%), but then increased similarly in both genders (by 20-21%). For sadness, both methods (LIWC and RoBERTa) showed that sadness expression was more prevalent in women before COVID, and increased more strongly among women during COVID-19 (by about 13-18% vs. around 10% in men). In summary, our Twitter data thus confirm the higher prevalence of sadness pre-COVID in women than men. This gender difference is also slightly visible for fear, but not necessarily for anxiety. During COVID-19, the increases in anxiety and sadness are larger for women than men in both types of data, to a greater extent in the Twitter data than in the Gallup survey.

Daily negative and positive sentiment measures from Austria

In a similar study using data from Austria, we compared daily self-reports of negative and positive emotions collected in a survey with sentiment based on postings from two social media platforms. We used data from a daily emotion survey conducted on the website of an Austrian online newspaper (Der Standard) for three weeks in November 2021, and text data from the discussion forum on the same website with around 25 thousand posts per day on average, as well as from Twitter users in Austria.

We found a very strong and robust positive correlation between the survey and the Der Standard aggregate sentiment (see Figure 4.3 A, r=0.93, 95% CI [^0.82,0.97], p<10^-8^). The text sentiment aggregate explained 85% of the variance in the daily proportion of positive emotions (see Figure 4.3B). Similarly, when comparing changes in the proportion of positive emotions between one day and the next, the text sentiment aggregate explained 70% of the variance in changes in reported emotions (Figure 4.3C).

Figure 4.3. Time-series and correlation of reported emotions and text sentiment in the Der Standard online forum
Note: Panel A: Time series of the daily percentage of positive emotions reported in the survey and the aggregated sentiment of user-generated text on derstandard.at. The shaded blue area corresponds to 95% bootstrapped confidence intervals. Panel B: Scatterplot of text sentiment and survey responses with regression line. Panel C: Scatterplot of the daily changes in both text sentiment and survey responses compared to the previous day, with regression line

Comparing dictionary-based (LIWC) and machine-learning based methods (GS) in this study with German text data revealed that both methods contribute to explaining self-reported emotions (see Table 4.2). Positive GS measures correlated more strongly with survey emotions, although positive LIWC also performed well. Yet, for negative emotions, the best method depended on the platform (GS for Der Standard data, LIWC for Twitter data). Overall, both of these German negative sentiment measures performed worse than the positive ones, suggesting some room for improvement.

Combining negative and positive emotion components into one aggregate score proved to result in the highest correlations with self-reported emotions in the GS case, and for LIWC with data from one of the two social media platforms. Table 4.2 reports results for each component (positive, negative) and each method (LIWC, GS) separately.

Note: The table includes sentiment aggregates (positive minus negative emotions), as well as positive and negative components separately. Shift 1 denotes a shift of one day. Brackets indicate 95% bootstrapped confidence intervals.

In conclusion, this second study finds that measures of sentiment based on text from the online forum of a newspaper track daily emotions reported by readers of that newspaper. These results also generalize to text sentiment on a second and separate social media platform. We find strong positive correlations with both levels and changes of daily sentiment. When comparing machine-learning and dictionary-based methods, the supervised classifier shows more consistent performance and generally higher point estimates (although with overlapping confidence intervals and not for LIWC negative on Twitter). Combining both methods for Der Standard adds a small increase to the already strong correlations of the supervised classifier alone.

Longer-term well-being: satisfaction with life in the United Kingdom

We explored whether social media posts can be used to predict changes in life satisfaction using YouGov's UK weekly life satisfaction survey in which respondents are asked: "Overall, how satisfied are you with your life nowadays?" To approximate the answer to this question with text from social media, we used Twitter data from the above study in the UK. We calculated a gender-rescaled daily score as dictionary-based positive minus negative emotions, as in previous research. We used LIWC to measure the frequency of tweets expressing positive and negative emotion. We applied a rolling window of four weeks to encode the longer timescale of the question and compared the answers to the survey in a given week to the Twitter data from the same week and the previous three weeks.

Figure 4.4 shows the time series of life satisfaction and Twitter sentiment for the historical and prediction period defined in the UK study. The correlation between the Twitter signal and satisfaction with life is 0.38 (95% CI [^0.14, 0.57], p<.01) in the historical period and 0.56 (95% CI [^0.27, 0.75], p<.001) in the prediction period, suggesting that emotional expression on Twitter might be partially informative of population-level changes in subjective well-being. As anticipated, these correlations are smaller than those seen between Twitter sentiment and daily or weekly emotion measures reported above, which aligns with past research on the relative sensitivity of affective state measures and stability of life evaluation measures. That social media measures relate to life satisfaction in similar ways as self-reported emotions further increases the confidence in the validity of social media emotion measures.

Given that life evaluation measures encompass so much information, the medium-sized correlations with social media emotions we observed are impressive, and suggest that further developing social media measures for long-term well-being is promising. However, we must note that the time span for which the YouGov survey is currently available is too short to draw strong conclusions about such slowly-changing time series. This also calls for future research when more data are available.

Given that digital traces from social media seem to be valid indicators for the emotional state of populations, we will now demonstrate how social media data can be used to investigate the evolution of emotions around the globe during the early outbreak of the COVID-19 pandemic in 2020.

A case example: Emotional expressions on Twitter during the COVID-19 outbreak

The COVID-19 pandemic exposed people from all over the world to unexpected and unprecedented health threats and drastic changes to their social lives. Using social media data, we tracked people's emotional well-being in countries around the world as a new dangerous virus spread, and increasingly stricter protection measures were implemented. During the first 5 weeks of the COVID-19 outbreak, we analyzed data on 8.3 billion public tweets in six languages, (English, Italian, Spanish, German, Dutch and French) from 18 countries. These countries included ten from Western Europe (Austria, Belgium, France, Germany, Ireland, Italy, the Netherlands, Spain, Switzerland and the United Kingdom) four from Latin America (Chile, Ecuador, Mexico and Peru) and four other western industrial countries (Australia, Canada, New Zealand and the United States). We focused on evolutions of anxiety, anger, sadness and positive emotions, because we expected the pandemic events to impact these emotions, and because all of them may be relevant to the management of a pandemic outbreak. Anxiety, for instance, develops when people lack clear explanations and feel unable to cope with a threat, and impacts risk perception, active information seeking, and compliance with recommendations.

Following the methodological approach in earlier studies of emotional responses to catastrophic events, we measured the proportion of emotional tweets expressing either anxiety, sadness, anger or positive emotions using LIWC, an emotion-dictionary that exists in all of these six languages. We matched the text of tweets to the word lists from the dictionary, and then calculated the daily number of tweets that contained at least one of the emotional terms for the time period between 1 January 2019 and 15 April 2020. In order to allow for comparisons between countries, we baseline-corrected the proportion of emotional tweets for the average level in 2019 (subtracting and dividing by this baseline). In addition to investigating the evolution of emotional expressions over time, we analyzed associations with real world events, including the number of confirmed COVID-19 cases and the stringency of measures against the spread of the virus.

Anxiety

At the start of the COVID-19 outbreak, we observed large increases in the percent of tweets containing anxiety terms in all countries. Figure 4.5 illustrates this change for anxiety in four example countries with different native languages. It shows for instance that anxiety-terms increased for the first time by more than 40% exactly at the time the first case of COVID-19 was diagnosed in Italy. They then increased to their highest peak of 96% when cases began to rise, shortly before stringent measures against the spread of the virus were implemented for the first time. The highest anxiety peaks in the 18 countries were in between 20% and 96% increases from the baseline. The brief anxiety peak just before the outbreak in Germany is a good example for how emotional expressions on Twitter usually change in response to one-off catastrophic events, here a terrorist attack in Hanau. The higher use of anxiety-terms during COVID-19 was much more sustained. Figure 4.5 further indicates the time periods used for most statistical analysis with gray rectangles at the bottom of each panel: A control period from mid-January to mid-February, and the first five weeks after the outbreak (the day with 30 cases) in each country. Using the average across these five weeks, Figure 4.6a shows that the increase in anxiety-terms could be observed in all 18 countries in our sample. More specifically, during the first five weeks after the outbreak, anxiety-related terms were on average between 5 and 40% higher than during the baseline period (the year 2019).

In the first week after the outbreak (defined as the day where COVID-cases reached 30 cases in a country), the extent of the anxiety increase clearly correlated with the growth in COVID-19 cases across countries (r=0.52, p=.023, Figure 4.7a). Most of the countries with the highest anxiety levels were also those with the strongest growth in confirmed COVID-cases in the first week, including for example Ireland and New Zealand. Italy is one exception with a lot of anxiety expressions but lower case growth; anxiety in Italy was likely influenced by Italy being the first country in Europe where cases were diagnosed.

Anxiety seems not only related to cases, but also to the increase in the stringency of measures.

Anxiety seems not only related to cases, but also to the increase in the stringency of measures that governments implemented to reduce the spread of the virus. As the timelines of Italy, Spain and the US in Figure 4.5 illustrate, anxiety and the stringency increase happen almost in parallel. The increase of anxiety-related terms on Twitter occurred shortly before or at the same time as more stringent measures were implemented in 15 of 18 countries. As Figure 4.7 shows, anxiety starts to decrease 2-3 weeks later, once stricter measures are in place. This decrease may reflect that people relaxed as they felt that governments were doing something to cope with the threat and to protect them. Words like staying (at home), buying (of groceries), emergency, health, contagion and information were among the most frequent words in anxiety tweets, confirming that a large part of people's worries were directly linked to the spread of the virus as well as the consequences of lockdowns.

Sadness

Sadness-related expressions increased more gradually and later than anxiety-related ones. This is visible in Italy, Spain and the US in Figure 4.5. On average, sadness reached its highest level three weeks after the outbreak, and remained stable for the following two weeks (these weeks are visually indicated with gray rectangles for the four example countries in Figure 4.5). The gradual increase of sadness terms occurred a while after stringency of social distancing measures increased, and remained high about two weeks later (Figure 4.7b). Although sadness increased less than anxiety, peak increases still ranged from 7% to 52% among countries. Figure 4.6b illustrates that the increase in sadness expressions was also quite consistent across countries, with only 2 countries not showing a significant increase. The timing and the duration of the changes in sadness terms suggests that sadness may have been a response to the loss of contact and daily routines during lockdowns. Consistent with this, words related to physical distancing (quarantine, isolation, confinement, social, lockdown, stay at home, going out) were used more often in tweets expressing sadness (and other emotions) than in other tweets (see the SI of our study). In contrast, deaths were in general not mentioned frequently, which makes sense given that there were not that many casualties during the early stage of the pandemic. Altogether, the timing and content of tweets with sadness-related expressions suggest that changes to people's everyday life were a driving factor of the increase in sadness.

Anger

Anger expressions on Twitter decreased during the onset of the pandemic. Similar to sadness, this change also occurred gradually, starting around the time of the first clear increase in measure stringency in most countries. Anger expressions significantly dropped in 14 out of the 18 countries, and remained less frequent until the end of the five weeks we analyzed. The decreases around the onset of stringent measures may indicate that people were generally not opposed to the actions their governments took at this early point in the pandemic. In addition, decreases of anger terms may also be a consequence of discussions on Twitter focusing on COVID-19, and therefore less on the many other controversial and political topics that are usually discussed on this social media platform in many countries. Although media discourse might have created the impression that people were angry about government regulations during the first outbreak, they may actually have been less angry than about previous political decisions. Consistent with this, we observed a shift in topics of conversation from political ones in 2019 to pandemic-related issues after the outbreak across countries and emotions.

Anger expressions on Twitter decreased during the onset of the pandemic.

Positive emotions

In contrast to the three negative emotions discussed above, expressions of positive emotions on Twitter remained relatively stable during the first 5 weeks of the pandemic. Average changes during this time period were between -5 and +5% (Figure 4.6d). In six countries, positive emotion terms dropped slightly just at the moment when public health measures became more strict (Peru, Italy, New Zealand, Mexico, Chile, Spain). This can be observed, for example, in the time series for Italy in Figure 4.5. This decrease was brief, however, possibly because people started to notice positive aspects (e.g., of spending more time at home). This finding could suggest at least a short-term resilience to the challenges during the early phase of the COVID-19 outbreak. Yet, it could also be a consequence of the broad range of terms included in the positive emotions LIWC-dictionaries. Some positive emotions may have actually decreased more, while others may have increased.

Duration of emotional changes

To assess the duration of emotional changes, we counted the number of days in a row during which social media emotion measures remained significantly above or below their median level of the previous year in each country. These time periods were much longer during the pandemic outbreak than what was observed during previous one-off catastrophic events. The maximal duration of sustained changes in all four emotions during COVID-19 were among the longest ones since the beginning of 2019 in all countries, and the single longest one in the majority of countries (see Figure 4.8). Specifically, 16 out of 18 countries had not experienced such long periods of elevated anxiety and sadness before COVID-19. Ten and eleven countries, respectively, also experienced the longest sustained periods of decreased anger and positive emotions during COVID-19. Longer increased anxiety before COVID-19 occurred only in two countries during political protests in 2019 (against social inequality in Chile, and austerity measures in Ecuador). Similarly, longer elevated sadness occurred in Chile during the same protests, and after a political scandal in Austria (the so-called Ibiza affair).

Conclusion on collective emotions during the COVID-19 outbreak

Taken together, our analysis of social media text data during the early COVID-19 outbreak revealed the most enduring changes in emotional expression observed on Twitter since at least the beginning of 2019 in most of the 18 countries. Anxiety-related terms increased early and strongly in all countries, shortly before the onset of lock-downs. The upsurge of anxiety was stronger in countries with larger increases in cases. Sadness terms rose and anger terms decreased around two weeks later, shortly after strict physical distancing measures like lock-downs were implemented. Sadness and anger expressions remained high and low, respectively, until the end of the five weeks we analyzed, suggesting that expressions of these emotions may have been associated with people's experiences during lock-downs. In contrast, anxiety expressions gradually decreased towards baseline a while after the onset of strict measures, possibly indicating that people got used to the new danger and public health measures, or were relieved that measures were taken. Positive emotions remained relatively stable throughout this early phase of the pandemic. Time-sensitive analyses of large-scale samples of emotional expression such as this one could potentially inform mental health support and risk communication during crisis.

Social media indicators for emotions are better suited to assess emotional well-being in some than in other situations. Many features of social media data are not clearly disadvantages or advantages, but have good and bad sides depending on the research question. Although we have assigned each feature to either strength or limitations below, we highlight both sides and compare to survey research where relevant.

Social media data seem to more accurately reflect people's responses in surveys for short-lived emotional experiences, than longer-term well-being.

The above case studies have shown that social media data seem to more accurately reflect people's responses in surveys for short-lived emotional experiences, than longer-term well-being, such as life evaluations. That social media data is better suited for more short-lived phenomena is true beyond emotion research: Long-term analyses of social media data are complicated by drift in who uses social media, in how it is used, and of the platform system itself over time.

Finally, social media can in some circumstances include people that are hard to reach with surveys. For instance, they make it easier to include non-English speakers as no survey translation is necessary, which is especially important when studying low-income countries.

Unlike surveys and experiments, which can be tightly controlled and usually include control groups, it is much harder to draw causal conclusions from observational social media studies (low internal validity). In contrast, social media emotion measures have potentially high ecological validity, and can capture the social nature of emotions, as they trace emotional expressions in real online social interactions.

Social media data can support research questions for which survey data are not available.

Features of social media data

Strengths

Limitations

Low cost and effort for data collection

Incomplete: Not designed for research

High time-resolution (down to minute time-scales)

"Dirty": Include spam and postings by bots

Continuous historical record

Drift in social media users, ways of using them, and in platform design complicates studies of long-term trends

Access to very large samples

Non-representative samples

Non-reactivity of indirect measures: not influenced by reporting biases

Behavior is not "natural": People only talk about certain things on social media, and avoid talking about others.

Provides access to information from people that are hard to reach with surveys (e.g. working population, non-English speakers)

Data are sometimes inaccessible and/or sensitive

Data is algorithmically confounded

Influence on validity of social media research

Strengths

Limitations

High ecological validity: behavior in real online social interactions

Low internal validity: causal conclusions are difficult

Current evidence suggests potential convergent validity for measuring emotions of large groups (e.g. societies). Yet, evidence differs across different emotions and methods.

Evidence for convergent validity of individual-level emotion is weak.

Higher convergent validity for short-lived emotional experiences.

Lower convergent validity for long-term well-being measures like life satisfaction.

Conclusion

Three case studies presented in this chapter provide evidence that emotion measures based on social media postings can track emotions at a society-wide level. These aggregate measures seem to be more accurate for measuring affective experiences at shorter time-scales, with correlations highest for short-lived emotions reported daily, and lowest for more slowly changing measures of well-being like satisfaction with life. In both cases, and especially for slower well-being trends, more research is needed once further data are available. When gender information is available, rescaling for gender can increase the information available from sentiment measures. Dictionary-based as well as machine-learning based methods of assessing emotions in text seem to contribute some information to predict emotions reported in surveys at the population level in our case studies. Regarding the LIWC dictionaries, this works better for anxiety and sadness than positive emotions in English, and better for positive than negative emotions in German. Finally, in English and German, machine-learning measures for positive emotions performed better than dictionary-based measures.

Social media data can support research questions for which survey data are not available, such as retrospective analyses, crisis research, or studies on populations hard to reach with surveys. We have presented one example for crisis research, using indicators of emotional well-being in 18 countries during the COVID-19 outbreak. During the first five weeks of the COVID-19 outbreak, we observed strong initial increases in expressions of anxiety on Twitter, associated with the growth in cases and the stringency of measures. A bit later, social media measures of emotional expressions indicated a gradual increase in sadness and decrease in anger, which began at the time where stringency measures included strict lockdowns. Anxiety gradually relaxed once measures had been implemented, suggesting that people habituated to the new circumstances or felt reassured by their governments' actions. Anger expressions dropped as discourse on social media shifted away from politically polarized discussions and focused on COVID-19. Sadness seemed more strongly associated with effects of social distancing measures on people's personal lives, and only linked to deaths by COVID-19 as these became more prevalent.

The correlation studies presented in the first half of this chapter suggest that social media data reveal information about the emotional well-being of residents of these countries during this early pandemic stage. Taken together, social media emotion data provide added value in addition to representative surveys. The correlations we observed in the UK study were in the range of correlations between surveys, suggesting that social media data are suitable as a complementary source of information on emotions. Potentially, social media and survey data may even contribute some unique information to predict outcomes like suicide hotline calls, hospital visits, police calls, or overdose rates. Future research could explore if combining these two sources of data could help to better predict and respond to such important outcomes.

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Endnotes
Back to the 2022 report

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Using Social Media Data to Capture Emotions Before and During COVID-19

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Most people now use social media platforms to interact with others, get informed, or simply be entertained. During the COVID-19 pandemic, social lives moved online to a larger extent than ever before, as opportunities for face-to-face social contact in daily life were limited.

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Article about What's App age restriction in Europe

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Article about What's App age restriction in Europe

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Age Restrictions on Social Media Services - UK Safer Internet Centre

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This blog looks at what age restrictions are in place across the most popular social media services, why they exist and what our advice is for parents and young people.

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YouTube has been accused of collecting the viewing data of children aged under 13, in breach of a UK data privacy code designed to protect children.

Campaigner Duncan McCann has lodged an official complaint with the Information Commissioner's Office (ICO).

He says the site is gathering data about the videos children watch, where they are watching and what device they are watching it on.

YouTube said it had invested in protecting families.

This included treating all children's content as though children were viewing it, even on an adult's account, it said.

"We remain committed to continuing our engagement with the ICO on this priority work, and with other key stakeholders including children, parents and child-protection experts," a spokesperson said.

The platform is owned by the US tech giant Alphabet, which is also the parent company of Google.

The firm has always said its service was not intended for use by children below the age of 13, and it offers a separate children's app called YouTube Kids as well as a "supervised experience" which requires parental consent.

Mr McCann says plenty of children watch YouTube content on family devices, where this data can be gathered by default because it is not registered as a children's account.

His complaint is believed to be the first test of the ICO children's code, which was introduced in 2020, when tech firms were given one year to comply with it.

Its terms include providing a high level of privacy for children by default and not using design features that encourage them to provide more data.

YouTube said at the time it would turn off default auto-play on videos and block ad targeting and personalisation for all children.

According to the regulator Ofcom, 89% of children in the UK between three and 17 used the video platform in 2021.

Firms found to be in breach of the children's internet code can face large fines, similar to the penalties for breaching data protection laws.

The ICO said it would consider the complaint carefully.

Mr McCann is acting in a personal capacity, but works for the campaign group 5Rights Foundation, which has championed the code.

"My preferred reform that YouTube should make is that actually when you enter YouTube, they don't collect any unnecessary and process any unnecessary information," he said.

"The best way to ensure that they are only collecting the data of adults who are properly consenting would be to have a process where adults can sign in to the tracking, recommendation systems, profiling, targeted ads."

He added that he believed it would be "a minority" of users who would choose to do so.

In 2019, YouTube was fined $170m (£139m) by a US regulator for violating children's privacy laws.

This followed a similar accusation that it was collecting data on children under 13 without parental consent.

YouTube did not admit responsibility but it did pay the fine and also changed its business practices as a result, said Stephanie Hare, author of Technology is Not Neutral.

"It assumed that anyone watching children's content would be potentially under the age of 13. And so it now collects far less data on that type of content, and also doesn't send personalised ads to people watching that content," she said.

"The solution exists, they just need to pull it out of the toolbox."

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YouTube accused of collecting UK children's data

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YouTube has been accused of collecting the viewing data of children aged under 13, in breach of a UK data privacy code designed to protect children. Campaigner Duncan McCann has lodged an official complaint with the Information Commissioner's Office (ICO).

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Internet

A quarter of 3-year-olds in the UK have a social media profile. This is according to a 2021 survey of parents by the UK communications regulator Ofcom. Despite most social media sites setting their recommended minimum age at 13, as much as 80 percent of 12-year-olds have some kind of profile and, as our infographic illustrates, at no age from 3 onwards are the figures below one quarter of all children included in the survey.

In terms of specific platform use, Ofcom states: "Children aged 8-11 were more likely to have profiles on TikTok (34 percent) and YouTube (27 percent). TikTok in particular is growing in popularity, even among the youngest age groups; 16 percent of 3-4 year-olds and 29 percent of 5-7s use the platform." Overall, of all children aged 3 to 17, YouTube is the most popular service (89 percent use it). While for some, the video sharing platform may not be considered a traditional form of social media, TikTok certainly is, and attracted 50 percent of UK 3-17-year-olds in 2021.

The research also showed that only a third of parents were aware of the correct minimum age requirement for social media. On top of that, among those who had a profile that were under the minimum age requirement, "significant minorities" of the younger children claimed to have set up their social media profiles themselves. As we previously covered, most social media platforms have been found to have a net negative effect on young people's mental health.

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This chart shows the share of parents in the UK saying their child has a social media profile in 2021, by age.

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This chart shows the share of parents in the UK saying their child has a social media profile in 2021, by age.

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Research has found that online communities created on social media can help children in many ways. Image: REUTERS/Luke MacGregor

Senior Lecturer, University of Western Sydney

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