We all have our favourite things about Christmas. Sweet mince pies. Dressing everything up in tinsel. Oh, and of course, those terrible festive films to get you in the Christmas mood. So, we here at Verve Search started wondering: what are the most popular Christmas films out there after all?

Whether you like to settle down with a romance like The Holiday, a close-to-the-mark choice like Bad Santa, or a heart-warmer like The Snowman – debating the best Christmas films remains a strong holiday pastime. Nobody’s going to blame you if you secretly like to put on Die Hard (it technically IS a Christmas film).

To get down to the nitty-gritty of it, using data analysis we took the top 33 Christmas films from IMDB and compared how often they’re searched against their average rating to get a rundown of the ultimate list of Christmas films, including this year’s exciting new releases.

So, light a little candle, snuggle up under a blanket and crack open the Celebrations for the most popular Christmas films ever. Please, there will be no Scrooges here today…

What are the most popular Christmas films of today?

Without further ado, take a look at our ultimate Christmas film rundown. These films go as far back as the 1940s, to the latest releases from this year. They are ranked the highest-rated and most widely searched films to date and will make for a cracking binge on the sofa.

Now, there are some festive favourites that we don’t need to tell you about. Home Alone and Love Actually come first and second respectively, with an average global search volume of around 800,000 searches. That’s a lot even for the most popular choices!

Later down the list, you’ll spot family-friendly films like The Christmas Chronicles, Elf and The Snowman, just to name a few. Can’t say we’re surprised with these, with many homes most likely flicking these on for some light-hearted Christmas relief.

But some anti-Christmas classics also appear in the rankings. Coming as high as third is the old 1984 favourite, Gremlins, which some even call a horror. Following just a little behind in fifth is the highly debated Christmas action classic Die Hard with Bruce Willis.

Some interesting (and maybe even surprising) top-rankers in this category.

And some festive breakthroughs for 2024…

Every year sees a wave of new Christmas films released for the season, but they can’t all make it into the annual festive rotation. So, it’s safe to say that these 2024 releases make newsworthy features, with three choices appearing in the rankings.

If you’re shaking in your boots during horror movies, look away now because Terrifier 3 came fourth overall, with over 465,000 global searches. With its controversial reviews, the extremely gory slasher film (which had people walking out of cinemas) somehow makes for a ‘Christmas’ film, where protagonist Art the Clown transitions into quite the Santa Claus.

This one’s leading because it’s made so many waves in searches online for its bone-chilling scenes and gory details. So, even though it’s a total horror, it’s still showing up as a popular Christmas choice. Watch if you dare…

Then, Dwayne Johnson stars alongside Chris Evans for action-packed blockbuster Red One, with a budget of $250 million. Whilst some critics call it a ‘flop’ of the season, it’s got an average 6.9 rating, so it may make for an easy watch where the North Pole’s Head of Security (Johnson) has to team up with a bounty hunter (Evans) on a mission to save Christmas.

There always has to be at least one big blockbuster a year, this year it jut goes to The Rock.

And, last but not least, the most meme-able Christmas film of the season is none other than Hot Frosty. If you’ve watched it already, then you might know why it’s making such a big impact on searches.

Starring Lacey Chabert (Mean Girls) and Dustin Milligan (Schitt’s Creek), the film centres around a snowman who comes to life. Slightly hard to digest, cheesy and completely ridiculous, it’s exactly what you’d expect from a Christmas romance. You may want to wash this one down with a mulled wine due to its cheese-factor…

Which one takes the (Christmas) cake for the highest rated?

Now now, our head data elves know that just because something is highly-searched doesn’t actually make it a good film to watch. If you look closely at the rankings, there is a massive difference between what’s most searched and what’s ranked high in ratings.

Overall, audience and critic ratings end up around the 70% mark or rated at around 7.25 (out of ten) on average on both IMDB and Rotten Tomatoes for many of these Christmas films.

The highest ranked overall is none other than cult classic It’s a Wonderful Life, from as far back as 1946 and also our earliest film on the list. With an average of 8.6, maybe the 40s just did Christmas better?

Other above-average favourites include Die Hard, Klaus, Rudolph the Red-nosed Reindeer, How the Grinch Stole Christmas and The Snowman. We’ll let you decide your favourites from those choices yourselves!

On the other end of the scale with a mere 5.5 average rating is none other than the breakthrough 2024 film, Hot Frosty. Snowmen really should stay in the snow, after all.

And, this shows that whilst Christmas films can be just as cosy as they are cute, that doesn’t always mean that they’re a groundbreaking piece of cinematography – and, that’s okay. Right?

Where can you watch Christmas films on streaming platforms?

There may be nothing worse than sitting in front of the telly and not being able to find a film you want to watch on any platform. Will it be on Netflix? Amazon Prime? Hold on a second – you might even have to rent it.

Nobody wants to be doing that, so to make it easy for you we’ve rounded up where you might find these most-wanted films and on what streaming platform they’re on.

Truth is, your best bet is to get your hands on a Disney+ subscription this Christmas, with eight films being available, followed by Prime Video with six and then Netflix with four.

With that being said, you can purchase or rent all but six films on Amazon Prime Video, so you may not have to get the DVD player out after all…

To wrap up

Christmas movies are more than just entertainment – they’re a part of the whole holiday experience. From the nostalgia of The Snowman to the cult appeal of Die Hard, these choices are just as sweet as the Christmas treats.

With over 800,000 global searches for the most nostalgic Home Alone and ratings reaching averages as high as 8 out of 10, a Christmas film can create an impact. No matter where your favourite may sit in the table rankings, it’s likely that you may find yourself watching at least one of these films throughout December.

While traditional choices might dominate in popularity, newer 2024 releases like Terrifier 3 and Red One show how diverse these Christmas films have become, covering feel-good action to full-blown slasher horror. Meanwhile, Hot Frosty may not win any awards with its 5.5 rating, but it shows that these can be conversation starters (around the dinner table or just in general, that’s your choice).

And, although streaming options may not cover every title, there’s no shortage of ways to bring festive cheer into your home this year. So, happy watching! May your season be merry, bright and full of arguments about what’s the best Christmas film…

Verve Search’s most festive Christmas films

And, because the best way to spread Christmas cheer is singing loud for all to hear, we’re sharing our favourite Christmas films straight from yours truly, Verve Search.

Amber Carnegie – Creative Lead: Muppet’s Christmas Carol

“It’s got to be Muppet’s Christmas Carol. Not only is it the only retelling of A Christmas Carol to quote as close to Dickens as possible but the incredible soundtrack and the idea that Micheal Cain is running around with all those puppets tickles me. Also, the ghost of Christmas present is literally the epitome of Christmas!”

Tonje Odegard – Outreach Director: Love Actually

“Mine has to be Love Actually as I’ve been watching it every Christmas with my family since it came out in 2003, and Christmas can’t kick off without a little cry over Emma Thomson holding back her tears to the soundtrack of Joni Mitchell. After my partner came on the scene, he also learnt to love it and we now watch it together, quoting almost every line off by heart (“8 is a lot of legs, David”). It’s so British and funny and cute and annoying and I love it, love it, love it!”

Ben McNeil – Outreach Specialist: Home Alone

“Home Alone is mine, for the same reason that I’ve been watching it every year since I was very young. It takes me back in time to my childhood and makes me feel very nostalgic. Not to mention the countless hilarious moments. The vibe, atmosphere and music of the film never fails to make me feel Christmassy!”

Danae Stavros – Outreach Executive: The Holiday

“Mine is The Holiday. I’ve been watching it pretty much every year since I was a kid. It’s really funny but emotional at the same time. Hot characters are a bonus, and I like that it’s not overly focused on Xmas, but just enough to get you in the spirit.”

Giovanna Castaneda – Outreach Executive: Home Alone 2

“Home Alone 2 (when he’s lost in New York). I’ve been watching it since I was a child with my cousins, and we even played as if we were in the film! It wouldn’t be Christmas for me if I didn’t watch it. No matter how many times I’ve seen it, it always cracks me up.”

Methodology and Sources

For our ‘Christmas films’ analysis, we picked out the most popular Christmas films on IMDB, searching for those that had over 100k reviews including new releases from this year. IMDB average scores were then compared to Rotten Tomatoes critics and audience scores to create an average rating.

We then checked where the films were available to stream or purchase digitally. The streaming services considered were within the UK libraries on Netflix, Amazon Prime Video and Disney+, as well as purchase options on Amazon Prime Video.

To consider Global Search Volume (GSV), these films were searched through Ahrefs keyword analysis to determine which were most popular. GSV shows how many times per month, on average, people search for the target keyword across all countries in the Ahrefs database.

Data accurate as of 2nd December 2024.

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Data. The not-so-secret weapon of most businesses. It’s far more than just numbers in a spreadsheet – it has the power to reveal incredible stories and newsworthy content – but if you’re at the beginning of your data analysis journey, it can be daunting to know where to begin.

Fret not, though. Let us guide you through the best ways of unlocking the potential of your data.

There are so many ways to streamline and enhance your analysis to ensure your numbers are rock solid. With these processes, your PR campaigns can become storytelling masterpieces that journalists and readers can’t help but engage with.

From understanding what your campaign objectives are to having the best data tools for the job, it’s all about what you know and how you use it.

Here at Verve Search, data is the cornerstone of our digital PR work, creating original and exciting content that is bespoke to each brand we work with. Our award-winning campaigns are proof that good data and analysis are imperative if you want to make an impact. 

Here’s exactly what we do collect, process, and analyse data for our digital PR campaigns.

 

Step 1: Campaign Objectives

Remember those objectives we just mentioned? They should be the first steps towards creating streamlined data campaigns.

But to achieve your campaign objectives you must first navigate what they are. Identifying clear and measurable goals can help focus and guide your analysis more effectively.

This involves asking questions like:

• What problems are you trying to solve? – This depends on the idea as well as client input.
• What metrics are best suited to the analysis? –This will depend on the angles you expect to pull out from the data and what you think will appeal to journalists the most.
• What is the time frame of the project? – This will impact the scope of your campaign.

Once you’ve answered these questions, you’ll be well on your way to understanding what you need to do to get there.

 

Step 2: Data Collection

Once you have laid out your campaign objectives, now is the time to make them come to life with data collection. In data-driven campaigns, there are countless data sources that can be used as the basis of the idea – and they often require different methods of collection.

Clients may offer up their internal data or you may have to go scraping the web to create your dataset. It’s useful to know what criteria make a good quality source while gauging whether they are suitable for your campaign, as well as how you can use them to your advantage and how long it will take to gather the data from them.

Looking for potential data sources that don’t require web scraping? Here are some examples:

Government Websites

Government websites like gov.uk or usa.gov hold extensive amounts of publicly available data across a broad range of topics and industries; all you need to do is hit download on whichever file you need.

For example, we leveraged government housing and land registry data to create Forever Homes and Priced Out Property campaigns. This created relevant, trustworthy, and newsworthy content that generated hundreds of links, whilst also keeping it super relevant for the brand.  

APIs

Many websites offer an ‘API’ (Application Programming Interface) which gives access to their data in a clearly formatted and relatively accessible way.

For example, the Spotify API is free and all you need to do is sign up for a developer account to have access to the API keys that are required.

There are a few different ways to incorporate an API for data collection, but for one example, Python was our tool of choice. With it, we collected data for a music-focused campaign for a language tutoring client.

For them, we produced a lyric study which looked at the number of syllables in rappers’ songs. It relied heavily on Spotify’s API, as it allowed us to access a variety of metrics ranging from general song/artist information to scores of different elements of a song. In this case, “speechiness” was the most important metric to score how wordy each track was.

Freedom of Information Requests

Why not request information from a public authority? Freedom of Information (FOI) requests are a great way to gather credible data by leveraging your right to information. You have the right to ask for recorded information held by public authorities including government departments, the local council, and the police and fire services. All this information is free to access.

Keep in mind that if you’re contacting departments from all over the country, you will need to carefully craft your request so that the data you receive is in a consistent format. This makes it much easier when it comes to analysis.

Surveys

If you want truly original data, there is no better collection method than an organic survey. Carefully crafted questions will yield useful results and easily analysable data.

We leveraged first-hand survey data to create a campaign focused on female health for a fem-tech brand, analysing responses from around 10,000 users. The results yielded many headline-worthy statistics, and the real-life opinions and answers were fundamental to the campaign.

Just keep in mind that collecting first-hand data like this can often be expensive, so make sure to bake that cost into your campaign’s budget.

 

Step 3: Data Processing

Before you can start to analyse the heaps of data you’ve collected, it’s key to first process your data.

Processing is the most important step and often referred to as ‘cleaning your data’. It ensures your analysis will be smooth and produces accurate results by making everything you’ve collected consistent.

The two main tools that will be referenced – and that we use continuously – will be Excel and Pandas (a data analysis module of Python, particularly useful for larger datasets that may sometimes crash Excel).

Remove duplicates

Excel’s built-in ‘remove duplicate’ function can do this for you. Alternatively, the Python library, Pandas has a similar function (drop_duplicates). More on the best data tools later…

Checking for errors or missing values

Pivot tables, built into both Excel and Pandas, are your best friend for getting an overview of your data and easily checking issues before analysis.

They allow you to check for anomalies and outliers within your dataset and summarise key metrics while also allowing you to filter and separate different columns for analysis. Below is an example of raw data for a potential campaign, which as you can see, would be difficult to analyse in its current form…

This is why we put it into a pivot table, as shown below. By selecting the individual columns to analyse, it is in a much more digestible format to pick out insights.

This specific table shows the number of international artists within the singles charts of each country and many more columns can be joined together with this, such as comparing Tempo scores by country to see which one has the most upbeat music.

Credit: Verve Search

Checking for and removing anomalies

Anomalies can disrupt analysis and interfere with useful results. They may occur for all kinds of reasons, but the most common is incorrect data collection. As mentioned above, pivot tables can help to identify anomalies, or even using Excel’s filter function: filter by ascending or descending order and you can see anomalous pieces of data, depending on your dataset.

Transforming

Any unchecked formatting may cause issues down the road, so check that your data is in the same format. Double-check that your columns are the data type you want them to be, including numbers, text and dates.

Sometimes numbers will be imported as text and need to be changed. If working with multiple data sources, they will need to be uniform across the board to prevent having to continuously refer to the original datasets to avoid confusion further along the analysis process.

 

Step 4: Data Analysis

With processing complete, you’re now ready to dive into data analysis.

Firstly, refer to your objectives. Check the initial goals to make sure your analysis still aligns with them and whether they’re still achievable with what you’ve got.

Make sure to explore your data. Most analysis can be done with pivot tables, either in Excel or Python.

Then visualise your data. Once you have dived into the data with pivot tables, you can use them to create charts and graphs that clearly represent the data. Your visualisations should make it obvious to anyone who takes a glance what the data is showing (refer to the data vis catalogue for inspiration).

Finally, it’s time to tell a story. Once trends have been identified and visualisations have been created, crafting a narrative around these findings will be what sets your campaign apart.

Pull out key statistics and create headline-worthy points from your analysis. Focus on making your points digestible for everyone, like opting for percentages rather than raw numbers as this will be clearer to both journalists and readers.

Data Analysis

Just like Batman, data analysts have a variety of tools in their utility belt. Below are the ones that we use the most day-to-day, to help us with steps in processes from planning to data analysis.

Excel

Hate it or love it, Excel is a necessary tool used for viewing, interpreting, and analysing data. Using pivot tables form a large part of the analysis process which is one of many useful functions that Excel contains.

Python Interpreter

To write and run Python scripts, you need a Python interpreter. This is always down to personal preference, but we prefer using Jupyter Notebook/JupyterLab to run code in a more modular fashion as opposed to a whole script at once. Not your cup of tea? Look to alternatives including VS Code and Pycharm.

Python

Python is a powerful programming language that can be used in various situations, specifically the scraping and analysis process for data analysts. By leveraging the power of the many libraries within Python, you can collect and manipulate large datasets easily to create visualisations.

The most-used libraries for data collection are BeautifulSoup, Requests and Selenium – and a combination of these can be used to access and scrape websites.

In the same vein, the most common library for analysis is Pandas, in which you can create data frames to contain and manipulate your data before exporting the results to Excel.

You can create visualisations using the libraries Matplotlib, Seaborn or Plotly, check out the documentation for more detailed information.

SQL

When working with large datasets, an SQL database can be useful in storing and accessing data. You can connect to a database through Python and store any scraped data directly. MySQL or SQLite are good starting points.

Data Visualisation Tool

 

Looking for more a powerful method for creating visualisations? Platforms such as Tableau or PowerBI can be useful to create more interactive charts, which can allow for a different type of storytelling.

AI

Whether you’re ready to accept it or not, AI and Large Language Models such as ChatGPT are here to stay. They’re useful when used correctly, and especially when assisting with code or even its built-in analysis capabilities.

However, be careful: it’s not recommended to directly input your data into these tools as they may contain sensitive information. Instead, use it to create Python code, or give you a more general guide towards the best methods for analysis.

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Interested in our content marketing and digital PR services, including data processing and analysis? Get in touch.

Around two-thirds of the UK population only speak English, and while many say they don’t feel the need to learn another language, multilingual skills can help with a mass of learning and communication skills. The truth is, there are a lot more similar languages than we might initially realise. 

Here at Verve Search, working with a multi-national and multi-lingual team, we know these benefits first-hand. Bringing a huge range of viewpoints when brainstorming concepts for clients – from the most lucrative foreign languages to Spain’s most beautiful road trips – producing campaigns about culture, languages, and linguistics comes naturally.

So, to show off the need for bilingualism and its benefits, we’ve undertaken an analysis into which languages are the closest to English (and hence make them the easiest to learn, too).

Investigating spelling, pronunciation and even using some maths, we can reveal the best languages to start your learning journey with below…

Key findings

In this study, we analysed which languages are closest to English by measuring the similarity of selected language features. The process included a range of natural language processing (NLP) methods to decipher this. 

We found:

• Scandinavian languages (Norwegian, Danish, Swedish) are the most similar languages to English, topping the board with their pronunciation and spelling.
• Finnish is the most different in all three categories, making it the hardest for English speakers to learn.
• Dutch is the closest in terms of phonetical sounds, whilst Turkish is the most different when spoken.
• Looking at the 1,000 most used words, ‘Radio’ has the most consistent spelling and pronunciation across all languages studied.

Methodology

There are three main elements to our data process. To summarise, we:

1. Gathered a list of the 1,000 most common words in the English language.

2. Translated each word into multiple languages using the Google Translate API.

3. Compared each translated word to its English equivalent to measure similarity.

Things to remember:

• We analysed the most widely spoken languages in Europe which use the Latin alphabet, and if a language has some additional characters, these were still included.
• Non-Latin alphabets are not compatible with this type of analysis. Languages such as Greek and Ukrainian have also been removed as they use the Greek and Cyrillic alphabets, respectively.
• Stopwords (e.g. ‘and’, ‘I’, ‘the’…) have been excluded.
• Disclaimer: This study analyses the similarity of individual words within each language, rather than the coherence and fluency of conversational differences. Language features around grammar (e.g. verb conjugations) and sentence structures are not considered.

How we measured the similarities of words

Now, let’s dive into the nitty-gritty of this study. We investigated two key features of words to understand their similarities and differences: their orthography and their phonetics.

First up, we have the orthographic distance between words.

“Orthography differences (spelling of words) measure how different spelling between languages is, considering alphabets, characters, and accents.”

To do this, we analysed the ‘Levenshtein distance’ between each English word in our seed list and their translated versions. Bear with us here.
 
Synonymous with edit distance, the Levenshtein distance calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string (word) into another.
 
To break it down, ‘cat’ and ‘cut’ have a distance of 1, as 1 single-character substitution is required to match each word.
 
Whereas the distance between ‘hello’ and ‘halo’ = 2, as 1 substitution and 1 deletion are required to match each word.

So, with learning languages in mind, we’ve allowed accents with the same character symbols to be considered identical, only for this orthography part of the analysis. For example, ‘Ocean’ and ‘Océan’ will have a Levenshtein distance of 0, as the accented character is considered the same as the original character. Still with us?

Lets move onto the the phonetic differences between words.

“Phonetic differences (verbal) measure the difference in pronunciation between languages. This includes individual phonemes as well as accent and tone emphasis.”

Doing this gets a little bit technical. We use a method called the Double Metaphone algorithm and a few more NLP steps. This method allows us to measure the difference between the original English word and the pronunciation in a different language by comparing the number of sounds in a word.

Firstly, we generated Double Metaphone encodings for both words (the English word and its translated counterpart, for each language) to represent how each word sounds.

Then, we measure the distance between each encoding through Levenshtein and maximum distance calculations. This distance is normalised and used as a similarity score between each word.

And breathe. That’s all for our method, but just a note on our scoring:

When interpreting our phonetic similarity scoring system, our phonetic similarity ranges from 1 to 100:

• High Scores (70-100): The words sound very similar or phonetically close.
• Mid Scores (30-70): Some phonetic characteristics are shared but are not very similar. A score of 50 indicates that the words have a balanced mix of similar and dissimilar phonetical features.
• Low Scores (1-30): The words are quite different, phonetically.

We know, it’s a little bit of a mouthful. But, it may make more sense when we put it into real data analysis. Let’s see what our results found…

Analysis

Overall language similarity: Which languages are the easiest to learn?

We found that Scandinavian languages were the most similar to English, taking all 3 top spots. Norwegian came in first, followed by Danish and Swedish.

English speakers should be able to pick up these languages the easiest, due to their high rate of similar spellings and pronunciations that English speakers are used to.

Wondering why languages from this region register as the most similar?  It goes back to the Vikings!

The Norwegian Viking invasions and settlement in England led to a significant Old Norse influence on Old English, introducing many words and impacting grammar. You’ll see this from words like ‘muck’, ‘skull’, ‘knife’ and ‘die’. Looks like they were having a particularly malicious time during this period…

However, whilst these Scandinavian languages topped the table, another Nordic language actually ranked last: Finnish.

This language differs primarily because it belongs to the Finno-Ugric language family, distinct from the Indo-European family that includes English and most other European languages. To put it simply, Finnish has fundamentally different roots.

Orthography similarity (written): Which languages have the closest written vocabulary to English?

In line with the overall index, Scandinavian languages take the top places for their written similarities too. In fact, Scandinavian dialects took the top four places for this ranking.

With an average Levenshtein distance of 3.85, that means Norwegian words are the closest to their English counterparts – less than four letters different on average. The next two languages here are Danish (3.90) and Swedish (3.94).

This time, written Finnish (once again) as well as Polish take the crown for being the furthest away from English, with a whopping average Levenshtein distance of 5.73 and 5.64 respectively. This means the average word in both languages requires 5.7 single-character edits to match its English translation.

Anyone who does speak Polish will know its vocabulary is largely distinct from English, with far fewer cognates. Although Polish has borrowed some terms from Latin, German, and other languages, its core vocabulary doesn’t align with that seen in English, making it a lot more difficult if you’re trying to learn.

Phonetic similarity (verbal): Which languages sound the closest to English?

Phonetically speaking, we measured Dutch as the closest language to English with an average phonetical similarity score of 48.2 out of 100. Where Old English and Old Dutch were both West Germanic languages, their evolution from these common roots means they retain many phonetic similarities.

On the other side of the table, you’ll find Finnish last once again – but this time followed closely by Turkish, which only scores an average of 21.3 and 23 out of 100, respectively.

What makes Turkish so different to English? That’s down to their sets of phonemes and phonological rules. For example, Turkish has vowel harmony in consideration, where vowels within a word harmonise to be either front or back vowels – a feature that’s not present in English.

 

Which words are the most similar across all studied languages?

Of the 1,000 English words analysed across 13 languages, the top three words with the most consistency in spelling and pronunciation are ‘Radio’, ‘Atom’ and ‘Dollar’.

‘Radio’ comes top with the same spelling across all languages studied, except in Turkish (‘Radyo’). The invention of the radio occurred in the late 19^th century in 1894, a time when technological advancements and global communication were becoming more interconnected. After this, the term ‘radio’ was adopted quickly around the world to describe this new technology making it a lot easier to pick up across languages.

‘Atom’ takes second place, coming from the Greek word ‘atomos’ which means ‘indivisible’. It was adopted into scientific vocabulary in the 19^th century, and with science being a global discipline, the term was retained in its original form across many languages.

‘Dollar’ rounds up the top three. Its consistency across languages is due to its historical origins in the European ‘thaler’, its widespread use in global trade and finance, and the influence of the U.S. dollar as a primary reserve currency.

Conclusion

Our Echoes of English analysis found that Scandinavian languages – particularly Norwegian, Danish, and Swedish – are the most accessible for English speakers to learn due to their high degree of similarity in both vocabulary and pronunciation.

These insights offer guidance for bilingual-curious English speakers to understand which languages will be the easiest to pick up, on an objective scale. This study also emphasises the importance of considering orthographic and phonetic aspects when evaluating language learning difficulty, aiding learners, language learning platforms, and language teachers.

Whilst Scandinavian tongues topped the tables, languages like Finnish and Turkish present the greatest challenges due to their significant linguistic differences in both spelling and pronunciation.

When analysing the 1,000 most common English words with both Levenshtein distance for orthography and Double Metaphone encoding for phonetics, this study offers a robust, comparative analysis of language similarity, particularly for the words ‘Radio’, ‘Atom’ and ‘Dollar’.

This underscores and reveals the historical and linguistic connections that facilitate easier language learning, such as the impact of Old Norse on English and the shared Germanic roots of English and Dutch.

Glossary

Accents: Difference in pronunciation specific to regions or groups within a language, often marked by different intonation and sound patterns.

Cognates: Words in different languages that have a common etymological origin and similar meanings.

Double Metaphone: An algorithm used in natural language processing to encode words by their phonetic pronunciation. Helpful in comparing how words sound across different languages.

Language Similarity Score: A composite measure of how similar a language is to English, based on both orthographic and phonetic analyses.

Latin Alphabet: The writing system originally used by the Romans, which is the basis for the alphabet used in English and many other languages.

Levenshtein Distance: A measure of the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one word into another. Used to assess orthographic similarity between words.

Natural Language Processing (NLP): A field of artificial intelligence focused on the interaction between computers and human (natural) languages, involving the analysis and synthesis of language data.

Old English: The earliest form of the English language, spoken in England from roughly the 5th to the 11th century.

Old Norse: The North Germanic language spoken by the inhabitants of Scandinavia during the Viking Age, which influenced the development of Old English.

Orthography: The conventional spelling system of a language, including alphabet, characters, and accents.

Phonemes: The smallest units of sound in a language that can distinguish words from each other.

Phonetic Similarity: The degree to which words sound alike when pronounced, analysed using the Double Metaphone algorithm.

Similarity Index Score: A composite measure of how similar a language is to English, based on both orthographic and phonetic analyses.

Stopwords: Commonly used words (e.g., ‘and’, ‘the’, ‘I’) that are often filtered out in language processing tasks because they carry less meaningful content.

Xenoglossophobia: The fear of learning or using foreign languages.

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Verve Search provides international targeting for campaigns across the globe. Interested in our content marketing, outreach and digital PR services? Get in touch. 

Producing content built with AI shows that the help of artificial intelligence can open up plenty of new avenues for newsworthy storytelling.

As we’ve seen over the last few years, AI can assist content creators with a number of methodologies, including facial recognition, image generation, voice recordings and even sarcastic chatbots.

With the rise of ChatGPT (if you haven’t used it yet, what have you been doing?), we could even go as far as to say that AI has scared some of us content creators into feeling like jobs are at risk.

Luckily for now, AI hasn’t completely taken over – just yet.

With a report stating that 3 in 4 marketers are using AI for content creation, AI content is certainly on the rise with blogs, publishers and brands seeking out these tools to boost their efficiency and output. Whilst the tools are handy, it can be difficult for consumers to sift out the AI from the authentic. This, however, isn’t necessarily a bad thing.  

Looking at examples of content built with AI, there are a mass of marketing campaigns that have gone on to earn linked coverage from news publishers in various sectors.

1. How bad is your streaming music? by pudding.cool

This project is more about posing as artificial intelligence to roast a topic that many people care enough about to share: personal music taste.

Spotify’s marketing success from their ‘Wrapped’ feature has become an annual event on social media — earning over 1.2 million tweets in a single month, and leading to huge increases in downloads of the app (plus many many more brands trying to replicate it).

The Pudding’s subversion of what makes Spotify Wrapped so popular was a genius way to appeal to the cynical side of music fandom.

Their “faux AI” tool gives the impression that a sophisticated AI bot is judging your prized personal music taste in real time, before returning sharable results that are partially tailored to the user.

well, the "judge my spotify" AI is modeling human behavior pretty accurately pic.twitter.com/Z923YCTO24

— Al Shipley (@alshipley) December 23, 2020

Since launching in late 2020, it has been picked up by more than 1,300 linking root domains. 

More than 1 in 5 of the headlines mention AI or artificial intelligence, suggesting that the AI’s participation in the experience is a key selling point in the story, as well as helping to make the project possible in the first place.

2. Having a pet does make you happier, if selfies are anything to go by by Verve Search

AI image recognition technology has the potential to reveal insights on a scale that the human eye wouldn’t be able to achieve. Truth is, content built with AI doesn’t always have to be copy-based.

Using Microsoft Azure in a case study, we wanted to see whether the many selfies that exist of pet owners and their pets show a happier image than a standard image of someone without a pet.

By comparing an anonymous sample of tens of thousands of pet owner selfies to standard selfies of people, we could compare the average level of emotions displayed in either category of picture.

Combining this AI tool with geotagged image data allowed us to reveal insights related to pet owners on a more international scale.

In other cases, we were also able to use this same process to measure the happiness of the average Instagram #selfie taker and the average #newhomeowner stood outside their front door and flashing their keys.

This campaign by Neomam studios for HouseFresh also used the same tool to identify the presence and strength of smiles in order to rank the happiness of locations in the USA.

3. Are humans better than artificial intelligence at detecting fake imagery? by ID R&D

Facial recognition technology carries plenty of controversy with it, including issues around misidentification, general privacy concerns, and who it’s being targeted at.

This research comes from a company that specialises in biometric authentication software with what is likely to be an attempt at downplaying some public fears over their technology.

While the statistics back up what they would hope to find — that AI isn’t fooled by spoof photos compared to the 30% of humans who do struggle to identify fakes — this story highlights an appetite that journalists have for exploring where humans and AI clash or collaborate in their capabilities around performing certain tasks.

4. Which U.S. sports teams have the biggest ‘fair–weather’ followings? by Verve Search

Sentiment analysis tools can help us to draw insights around attitudes and emotions from large volumes of (usually) text-based data.

At Verve Search, one of our favourite use cases is to analyse the emotions behind different topics that are being talked about within various corners of social media.

In this example, we separated thousands of comments on US sports team’s official Facebook fan pages after wins and after losses to see which fan bases are more likely to remain supportive when the good times go bad and vice versa — also known as fair-weather fandom.

Initially, we would have loved to measure this on metrics such as fluctuating ticket sales or stadium attendances over a longer period of seasons. 

But with that type of data mostly inaccessible and stadium attendance figures often debated for their accuracy, we found online fandom to be a good proxy with the help of SentiStrength, which could measure individual comments on a scale of positivity to negativity.

Other newsworthy examples of this type of analysis include when we found out which household chores cause the most stress or which elements of driving cause American motorists to complain the most.

5. Males are represented four times more than females in literature by The USC Viterbi School of Engineering

This is a great example of content built with AI using machine learning to continue building on a subject of research from previous years.

An analysis of 3,000 English-language books by the USC Viterbi School of Engineering used NLP’s (Natural Language Processing) ability to detect the prevalence of pronouns, and thus how often men and women are represented in literature.

With this type of AI able to process vast quantities of text-based data and return such results, there is clear potential here for building on this method in other forms of media and entertainment where gender representation remains an issue.

6. What emotions do leaders speak with in public? by Verve Search

Public speaking is usually a prerequisite of being one the most powerful people in business or politics. 

So for this campaign we applied AI voice recognition software built on deep learning techniques to judge the emotional profile of famous leaders’ speaking styles.

Pulling together a large seed list of audio files from the public speeches of politicians and famous entrepreneurs, we could look at how certain emotions are more prevalent in certain individuals, political parties and genders of speaker.

Understanding what emotions are being portrayed within a person’s voice would normally have to be studied individually. 

With AI-driven voice recognition, you can analyse large amounts of voiced audio files and retrieve results that are compared against the average emotional levels that the software is trained on. Or you can compare the relative emotional levels from your own dataset (in this case, the average leader) to see which voices rank highest and lowest vs those average scores.

7. Can you guess the famous Christmas movie scene from this AI mock-up? by Neomam Studios

As already noted, artificial intelligence is often talked about as something that clashes with humans — our judgement, our capabilities, or our jobs. 

And although this content is built on AI-generated images, an essential aspect of its appeal requires the input of its audiences to guess what creation the AI tool has conjured up.

For this campaign that leant on a TikTok trend, each image is a famous scene from a Christmas movie that was mocked up in different styles by the app Wombo.

Thanks to this thread, where I found the campaign, you can also see content built with AI to help with other areas of the creative process:

Recently I've been playing around with AI image generation tools and I've realised there's SO many ways we can use them in digital PR

Below I've pulled together a list of 4 ways of how you can use AI image generation in your everyday 👇

— iona 🦎 (@IonaJTownsley) July 15, 2022

There’s a Buzzfeed quiz for countless trends and topics. So, it’s not surprising to see that they also had a stab at the ‘audience vs AI guessing’ quiz format in July 2022, which you can try here.

8. Supercars from a dystopian future by Screaming Frog

AI image generation tools, such as DALL-E and Midjourney can capture our imagination in just a few words and visualise a detailed version of our thoughts much quicker than we would be capable of creating in the same format.

In this content example, the AI also had to capture the imagination of the journalist to whom the content was outreached to.

In our experience, motoring journalists who report on visual content are often an exception. They are used to dealing in data, reviews, previews, and shiny photography of even shinier vehicles. 

Thanks to the creative angle used here by SEO Agency Screaming Frog, the supercars from a dystopian future is a fictional story that still managed to cut through to a sector that would normally be more concerned with stories related to cars that you can actually drive.

9. AI-generated travel posters of destinations by Screaming Frog

According to OpenAI, DALL-E is generating over 2 million images a day. 

While the volume of AI-generated imagery already seems to be saturating the internet, the strategy of defining these image outputs to link them together under one newsworthy theme could still be in its infancy.

These examples of AI-generated imagery, also from the team at Screaming Frog, were fed by the names of countries and their travel slogans to see what Midjourney returned.

The posters are visually beautiful. However, when covering the story, the journalist seems particularly intrigued by what the AI — with no physical travel experience to rely on — chooses to prioritise in its interpretation of an entire country:

“Until you’ve seen a place for yourself, it’s a bit of an abstract idea, so why not ask Artificial Intelligence to generate your travel poster?… Like most travel posters, Midjourney has evoked a fairly sketchy sense of place, sometimes punctuated by notable landmarks or natural features.”

hyperallergic.com (2022)

10. AI reimagines famous horror film posters by Evoluted

Many horror movies can be recognised by their iconic movie posters or from the faces of their terrifying villains.

The speed of AI image generation allows for trialling out different ideas for visual content. And any examples which appear to make the grade with some design touch-ups can also be targeted to a specific, short-term event in the calendar, such as Halloween.

This example by Digital PR Agency Evoluted took some of the most famous horror films of all time to see what even more terrifying versions of their posters could be reimagined by the AI app Wonder.

Check out this Twitter thread for a breakdown of the posters and more information on how they were created:

What if AI was behind our favourite Halloween classics? Using keywords from classic horror plots @vicki_collins and @laurarowleyx used AI to reimagine horror posters partnering with our design guru @willbarron to bring these to life in the style of the original posters 🧵

— Evoluted | Certified B Corp™ (@evolutednm) October 26, 2022

For more AI-generated movie poster goodness (and weirdness), take a look at this series of posters created by artist Vincenzi in his project ROBOMOJI.

Using a similar method as the Evoluted example, Apartment Therapy tells us that the artist inputted “a series of prompts and descriptions about a film’s visuals, titles, and premise into the AI software.”

As noted, the artist didn’t set out to earn linked coverage with his project. They are using it to ask important questions around what role AI will play in the art world going forward.

So, should we be creating content built with AI?

While artists and industries are rightly questioning what the adoption of these new technologies means for the future of creatives, some, like Manas Bhatia, are already acknowledging the part AI can play in quickly helping to visualise early concepts before an artist refines them with their expertise.

Back in 2022, we saw a campaign from Samsung earn widespread coverage after they enlisted the help of a digital designer to reinterpret famous artworks, according to the issues Gen-Z are most concerned about in 2022. 

Relying on insights from a survey to inform the creative direction that an artist took provided a much more human and, therefore, newsworthy angle to this ‘reimagined’ content than what the artificial mind of a tool such as DALL-E would provide.

The extent to which you use AI and its different domains as part of your creative process will vary from one campaign to another. Some ideas will see content built with AI take the role of prototype designer, others will do much of the data processing to then allow your team to find the stories that matter within the data.

On the whole, it would be a mistake to think that the inclusion of AI alone will sell in a story to the press as newsworthy.

Without a defined creative concept to work with, these examples of AI are tools waiting to process whatever we feed them. As part of our role in creating newsworthy content out of AI, we should at the very least be setting the AI’s constraints, ensuring the inputs and outputs make sense, and closely monitoring what the overall direction is of the story that we’re trying to tell.

Further reading:

1. Deep Dive: AI Image Generator DALL-E Is Now Open To All — Why Should PRs and Marketers Care? by Rich Leigh [1]
2. The future of content creation with AI is closer than you might think by David Cohn [2]
3. The lawsuit that could rewrite the rules of AI copyright by James Vincent [3]
4. Messing around with AI and content concepts by Alex Cassidy [4]

Interested in content marketing and digital PR services? Get in touch with us.

Figma is evolving at an incredible rate. With a recent edition of FigJam and updates such as Cursor Chat and Audio, editors and viewers can collaborate even more effectively, saving time and resources.

One of the best things about Figma is the ease with which you can install all sorts of incredibly helpful plugins. If you’re new to Figma, it can be hard to know where to start – so here’s my guide to the best Figma plugins of 2021.

Master

Are you wanting to attach objects to an existing component or merge two main components? No problem. This plugin can help create, clone, and move components without losing overrides. While it might take some time to master this plugin, it’s absolutely worth it. Find out more about Master here.

Convertify Figma to Sketch/XD

In a previous blog, I touched on the differences between Figma and Adobe XD. For those designers that still have to toggle their design work between the two platforms, you might find the Convertify Figma to Sketch/XD plugin very useful. It easily converts and exports your design files from Figma to Sketch, Adobe XD, or After Effects with one click.

Adee Comprehensive Accessibility Tool

Adee is the plugin for you if you need to test your design out for accessibility. Adee is a powerful tool that offers a wide range of functionalities, including the game-changing colour-blind simulator. This feature lets designers select their design frames and preview them in eight colour blind modes within the Figma app.

Find / Focus

Alright, we’ve all been there – the larger the project, the harder it is to find that one layer. The Find / Focus plugin solves the difficulty of manually searching through endless layers in your document with its find, select, and zoom feature. Just type the layer name into the search window and refine your search with additional regex or case sensitive options.

Status Annotations

For those who use Figma’s free Starter Plan or like to keep all their design versions on one page, Status Annotations could be a helpful plugin addition. Although the status labels are quite small, this plugin does the job. It indicates the status of the design process for any selected element – a collaboration feature we needed!

Font Scale

Fonts can be difficult at the best of times, especially for those who are just starting out in design. Font Scale helps designers achieve harmony and consistency in a typographical hierarchy. With several scale factor options to choose from, Font Scale generates font size previews.

Figma Chat

As previously mentioned, Cursor Chat does a great job as an instant messenger within Figma; it’s innovative and super helpful for collaboration work. However, if you are looking for something a bit more old school, the Figma Chat plugin is a great option.

This plugin lets you communicate with other people inside the Figma file. You can also select a frame or an element and attach it to your message so that the recipient can find that element quickly.

Final thoughts

As we’ve discussed in a previous blog post, the design team at Verve Search uses Figma because of the sense of community that the platform encourages with features that allow for enhanced collaboration between creatives. If you’re new to UX and UI design, check out our selected plugins to see how they can work for your workflow and improve the collaboration on your team.

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Interested in our content marketing and digital PR services? Get in touch.

A Freedom of Information request that is constructed out of uniform questions with measurable answers can build content that earns press coverage on a large scale, both geographically and across the various news topics it can cover.

The Freedom of Information Act enables any member of the public to uncover information that otherwise may not have been released to the public. 

The law applies to more than 100,000 public bodies in the United Kingdom, meaning there are lots of stories out there to be told by using the law to access data.

In this post I’ll talk through various aspects of using the UK’s Freedom of Information Act as part of a content and outreach strategy that is built for earning links via press coverage, including:

1. What the FOI Act is
2. Where to find inspiration for stories 
3. Tips on requesting the right information
4. Some pitfalls to avoid

1. What is the Freedom of Information Act?

Introduced in the UK in 2000, it is the right to know information about publicly owned organisations. The Act places two main responsibilities on those public authorities: a) to confirm whether they hold information, and b) to disclose that information to the person who asked for it.

Some bodies that you might expect to be covered by the Act are exempt. These include housing associations for the most part, security bodies such as MI5, and the royal family – so you won’t be able to find out how much of the taxpayer’s money the queen spends on her breakfast anytime soon.

Despite their public status, it’s also tricky to obtain anything especially useful from the BBC, as most of their interesting data seems to be protected internally “for the purposes of journalism, art or literature”.

2. Where to find inspiration for stories 

If you send a quick general enquiry email to the first public organisation that you can think of, you might be disappointed to find that your request was rejected after waiting 20 working days for a response. Take a look at some of the below recommendations, which will provide inspiration for potential stories, and possibly be able to tell you if the information you seek is available at all.

WhatDoTheyKnow

Some organisations, such as the Office for National Statistics, will publish FOI requests that were made to them and responded to directly on their website. For those that don’t, WhatDoTheyKnow is the most useful way of accessing previous requests made to UK organisations. 

Study examples that did and didn’t work by filtering your search by ‘successful’ or ‘unsuccessful’ requests. Doing so will potentially save you a lot of time with having to clarify your FOI request further down the line.

Another useful feature that was recently rolled out on this platform allows users to add any examples of their request being used in a news story as a citation. Look out for these citations, as they can help to inform your outreach strategy to a greater extent by seeing how certain FOI requests convert into news headlines.

WhatDoTheyKnow also allows you to make requests through their platform and includes a guide for beginners on how to request information. Requests can also be made through your own company email address or private email address.

Google News search: “Freedom of information”

When seeking inspiration for stories, sometimes a simple Google search can be just as useful as pouring through the specifics of WhatDoTheyKnow. Type in “freedom of information” and browse through the many ways in which UK and international journalists are utilising the law to produce public interest news stories. 

Taking the time to read these articles from top to bottom will also show how many metrics journalists may report on for a particular type of story, as well as showing the kinds of spokespeople you can seek out within that sector to comment on your findings later on.

Doing this also presents the potential to scale up an interesting local story into something national that can be compared across different parts of the country. Did the Manchester Evening News publish their own FOI-led story about car parking fines? There’s a good chance that if it makes headlines in Manchester, a similar piece will make headlines elsewhere too.

The organisation’s website

If you have an organisation in mind, but you’re not sure whether they hold the information you want, take the time to browse through its website; specifically their services, publications and type of user data they retrieve. By the time you’ve read through these sections you will have a much better idea of what data you could ask for, and how this could convert into an idea for a story.

For example, OFCOM are one organisation covered by the Freedom of Information Act, and if you didn’t know what they do already, it doesn’t take long to see from their website that you can access data about public complaints related to TV, radio and other UK broadcasting services.

The BBC Shared Data Unit

The BBC Shared Data Unit is a nationwide partnership between the BBC and News Media Association that previously won ‘Editorial Innovation of the Year’ at The Drum Online Media Awards. It is dedicated to sharpening the data skills of journalists in local newsrooms around the UK and producing stories that work at scale for various regional and local titles.

Much of their data work is sourced from FOI requests, and, similar to how Digital PR campaigns aim to include angles that appeal to as many newspapers as possible, the Shared Data Unit is an excellent example of how to produce a story that resonates throughout the UK by picking out the angles from a larger dataset to make them work for local readerships. Here is one of my favourite examples of theirs:

A story that found British football matches were being heavily over-policed at a significant financial cost to the taxpayer.

The journalists behind this story compared information that they requested of police constabularies around the country with match attendance data from Opta, allowing them to rank ‘number of fans per officer’. It went on to generate 18 unique pieces of coverage in different newspapers within six days — not bad, considering Digital PR isn’t their game!

Like all good data storytelling, the Shared Data Unit is transparent with its methodologies and data. I would recommend reading through these if you’re just starting out on a larger FOI project for the first time, to see how they go about their process, from research and data interpretation, right through to execution and coverage.

3. Tips on requesting information

Think like a:
marketer
investigative journalist
curious citizen

Understanding what kind of information you can obtain doesn’t mean that you need to be an expert in coming up with ideas for data-led stories. Neither does it mean that you have to be wearing a Pulitzer Prize-winning cap in the hope of exposing the next great national scandal.

Some of the most effective FOI-led stories ask straightforward questions that the average citizen would be concerned with knowing and reading about, and which the person managing your request can easily interpret to collect data from their organisation. 

Simplicity with this in mind is important. While it’s noble to try to expose a brand new category of information from a public organisation that hasn’t been released before, the time constraints of your campaign’s production may risk leading you to spend more time contesting complicated and unsuccessful requests with the Information Commissioner’s Officer (ICO) instead of gathering straightforward, consistent data.

If you’ve already committed to the sign-off and kick-off of your idea without sending any test requests, then general enquiries or ‘fishing’ for information that you’re unsure is held shouldn’t be making up the crux of your FOI request at this point.

Including a speculative question alongside a set of questions that you know will be answered would at least guarantee that the majority of your request will be fruitful.

Whereas tentatively fishing for unknown types of information and expecting completely useable answers can easily end in wasted time and resources on your side and on the organisation’s side. More on this further down in the ‘potential pitfalls’.

Scale it geographically

Typically, FOI-led stories about UK organisations are less likely to appeal to non-UK journalists or publications.

You must consider how many organisations need to be contacted in order to produce a comprehensive story, and whether the work you put in will even deliver a large enough pool of outreach prospects.

A campaign we produced for Admiral in 2019 and 2021 looked at the scale of the empty homes crisis in Britain, and provided us with perhaps the most granular list of UK-based outreach prospects that we could hope for. 

The housing crisis, of which empty homes are a symptom, is engulfing the whole country. Therefore it was necessary to ask every British council for the same information in order to be able to compare the luxury districts of London and holiday home hotspots of Cornwall to other parts of Britain.

You don’t necessarily need to contact every council for every idea that considers information from them. This campaign produced by CompareMyMove looked at where in the UK registered the most noise complaints, and decided to focus on the most populated towns and cities. For an idea that ranked noise, focussing on places where a lot of people live made sense and didn’t necessarily require more rural (and typically quieter) districts to be considered.

You will find some organisations only have one central contact that stores all of its localised data, which means you would only need to ask for the same data once and specify that it should be broken down locally. 

Some of the coverage from our Testing Times campaign for GoCompare leaned on an FOI response to our question on ‘multiple testers’ (i.e. those who need 5 or more attempts to pass), as well as open data from the Dept. for Transport and a survey that revealed demographic and geographic breakdowns of claims and convictions.

To reveal the hardest test centres for passing a driving test, the DVLA (Driver and Vehicle Licensing Agency) held this information centrally, meaning we obtained all of the local data we needed from a single FOI request.

Scale it with data points

A common method in campaigns built for link building and digital PR is to rank different metrics related to locations that are covered by news publishers. This presents the opportunity to outreach to those publishers and acknowledge that their locality ranks particularly high or low on certain measures.

While an FOI-led analysis that compares different locations on certain measures will still reveal the highest and lowest ranks in the same way, you can also build out a comprehensive story that works for all of the locations that you are including in your dataset by considering ‘the state of play’ in those areas.

For example, the TV Tribulations campaign that we created for Buzz Bingo analysed complaints made to OFCOM and revealed which parts of the UK complain about television shows the most.

Even newspapers based in locations that didn’t rank highly on a national level were able to cover the campaign as we were able to provide data on their 10 most complained-about TV shows, plus how many complaints were made by that area over a given time period. It didn’t matter whether or not they were the highest ranking in the UK.

And in terms of outreach prospects, the data points were able to be split in a way that appealed to different sectors as well as different regions. Digging into the raw data of our TV complaints allowed us to reveal ‘the most complained-about radio show’ as well as ‘the most complained-about sports teams’.

“And the most complained about radio show, according to the report, was James O'Brien on LBC, who sparked 223 complaints to Ofcom during lockdown.”
Monstered by @piersmorgan. Must try harder.https://t.co/4OggtziWx5

— James O'Brien (@mrjamesob) October 9, 2020

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Once you’re confident that your data is available, you can ask a number of related questions that will make for a more comprehensive dataset, and which may include outliers that wouldn’t have been discovered by only asking one broad question.

For example, depending on how sensitive the information is, you may be able to ask for more specific street-level figures rather than just a figure for an entire local authority, as we did in this GoCompare campaign called Speed Offences. This told us which roads record the most speeding offences per year (locally and nationally). Again, doing this creates an interesting story to be told for every locality, not just the outliers.

Other angles we built into this campaign included: the highest speeding offences recorded and the worst months for speeding, simply by asking for specific breakdowns.

4. Potential pitfalls

Check for ambiguity

Accurate language is essential when crafting an FOI request. 

If your request isn’t clear, it can be the difference between receiving the information you need in 40 working days rather than 20 days (after you’ve had to provide a clarification), or never. 

If your request is misinterpreted and you receive the wrong kind of data, it may well be unusable and require a second request anyway. 

Word the request exactly how you want the information to be delivered to you. 

Describe how it should be measured, what period of time you would like it to be for, and how that time period should be split (daily, monthly, yearly?). 

Oh, and specify the format. The last thing you need when compiling your responses are more than 400 councils replying with PDFs and Word Docs of data.

Consider the cost and limitations

This might seem slightly contradictory to what I mentioned about scaling up the number of angles you want to include, but you should consider the limitations of the FOI Act.

The cost limit of a request is £600 (equivalent to 24 hours of work) from a central government department, and £450 from local authorities (equivalent to 18 hours of work).

If your request is estimated by the organisation to exceed what is known as the “appropriate limit”, certain sections of the request will be refused. If your request consists of one or two questions that demand substantial work, the request may be refused outright.

If you know the data you are asking for is recorded by them, or better yet, you have done your research and seen evidence of it being published already, then the work involved in retrieving the data should fall within the cost limit.

Plan ahead by sending a tester email

An FOI-led campaign can be quite a time commitment. Planning further ahead by sending an email to an organisation that asks if they have the information available is a useful step for if you want to uncover a more unique story that hasn’t been published before, but which you suspect is available from them. 

Taking a longer term approach to this method of research will also give you an idea of how reliable certain organisations are in responding on time. Regardless of how much you do test in advance, a 100% response rate is unlikely to arrive within the 20-working day period that it’s supposed to.

The example below from the ICO shows that between October 2018 and September 2020, Northamptonshire Police only managed to answer a maximum of 66% of their requests on time in a single calendar month. Response success rates vary among all organisations, and you can expect them to vary from month to month within each organisation.

Sending tester emails further ahead would also help for you to gauge whether the information you seek is consistently measured by similar organisations. An email to a handful of universities, for example, should tell you if they record certain data on their students in the same way, before you embark on a full investigation.

Be proactive in monitoring your responses

You’re probably blessed with more research time than the average online journalist, who is busy producing upwards of eight or more articles per day. This means that your FOI-led story could be something they wouldn’t have had the capacity to produce themselves.

Equally, you’re almost certainly not blessed with as much time and autonomy as an investigative journalist, who can press the ICO for months until they receive their satisfactory response, and it’s important to remember this during the process.

Say you’ve embarked on a round-robin FOI request, and contacted all 130 UK Universities in the UK. There’s a reasonably high chance that at least a handful of them will misinterpret your request, seek clarification, or respond beyond the 20-working day timeframe — in this case, you’d be relying on at least 130 different people to get back to you with the same information in the same format. 

In the interests of deadlines, sometimes you have to cut your losses on the information that didn’t arrive. At the same time, you need to closely monitor the story that is accumulating in front of you, and decide whether there is a story there at all.

Generally speaking, a two-thirds rule of responses with consistent data would be adequate for producing an analysis of UK Universities, with clarification of how many didn’t respond, but it really does depend on what you’re comparing and what the sample size of organisations is. The data that is missing could well be more valuable in constructing a story than the data that you did receive.

Seek comments and anticipate a PR response

By its very definition, the data that you’re dealing with is authoritative and within the public interest, so don’t be surprised when organisations and public figures acknowledge it and often respond to it when it concerns them. Below is one notable example…

Humbling. 🙏 https://t.co/rFU2g6tSvN

— Piers Morgan (@piersmorgan) October 9, 2020

But responses are often concerned with more serious topics of public interest, such as when our empty homes valuation was quoted by a local property agent when commenting on Aberdeen’s local housing issues.

Since releasing FOI-led stories and following the news coverage they receive, we’ve dealt with phone calls from a number of press offices, including NHS Trusts, councils and cultural institutions, all of whom have wanted to find out more about the data they originally sent to us.

If the main headlines that emerge from your data can be attributed to an individual or a small number of organisations, then it’s worth reaching out to them to comment. Even though a journalist will typically contact the public organisations to comment before publishing their story, doing this yourself adds valuable context and commentary to your research.

Why is your brand doing this?

This is a question that should be asked of any potential idea that is going to be published and outreached for your clients.

In the case of FOI requests, as soon as you produce content that is built on information from public bodies and take those stories to the press, the brand you’re outreaching for is influencing the reputation of other people and organisations.

Some topics may seem relevant enough to approach from a storytelling perspective in order to earn coverage for a client, but public data is sensitive and often gets politicised.

The client that you work with should be made aware of potential backlashes to an FOI-led story before an idea is deemed appropriate for them.

Further reading and people to follow:

• How to access information from a public body [1]
• What is public interest journalism? [2]
• Research and where to find data for your campaign by Hana Bednarova [3]
• Twitter.com/FOImanUK: Information Rights Practitioner and Author of The Freedom of Information Officer’s Handbook
• Twitter.com/mattburgess1: Author of the Freedom of Information handbook

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Interested in our content marketing and digital PR services? Get in touch.

I was given the opportunity to attend the inaugural Outlier 2021 conference hosted by the Data Visualization Society. It featured 41 inspirational talks given by people who work across different industries, each with unique and varying levels of experience in their data visualisation specialisms.

There were so many talks to choose from, but I’ve narrowed down five that will help to reframe how you think about the process of creating impactful data visuals.

1. How do we translate cultural experiences into data stories?

Hosted by Isabella Chua & Mick Yang (Kontinentalist)

The talented team at Kontinentalist create engaging data stories that unpack cultural experiences to gain a better understanding of cultural trends.

In their talk, I learnt the following tips to create a compelling data story that translates other cultures:

• Find an angle that is proudly niche
  
  If you are translating your own cultural experience, do it with pride and communicate it with an urgency that suggests if you don’t tell your story about your experiences then other people won’t be able to either.
  
  Explore a particular angle of interest in-depth, rather than being too wide-ranging in exploring a number of angles at surface level.
  
  This can be something as simple as introducing one lesser-known artefact or phenomenon from your culture and communicating it in a way that educates and informs a wider audience from outside of your culture.

• Unpack diversity within your angles to explain how certain phenomena are experienced within that culture.
  
  This can mean helping your audience to understand the ways in which cultural phenomena interact with the lives of different groups in that culture (e.g. What’s the big deal about chilli in Asia?).
  
  In this example, chillis provided an excellent window for exploring Asian cuisines and the influence that chillis have upon many dishes.
  
  The author began his analysis by asking ‘was spicy food popular in Asia?’. But the yes-no nature of the question provided added complications to finding a definitive answer to something not comprehensively documented, so he refined his analysis to explore ‘what ways spiciness – in particular, chillis – were experienced in Asia’, which was more open-ended and allowed for unpacking the answers in a less binary fashion.

• Test conventional wisdom to dispel common myths (e.g. All the shades of Lee Hsien Loong).
  
  It’s a common myth that the Singaporean prime minister Lee Hsien Loong mostly wears pink shirts. After collecting data on all the shirt colours he’d worn during PM speeches it was revealed that his most commonly worn colour was actually white.

• Quantify the intangible
  
  Some cultural phenomena might have a concept that is quantifiable (e.g. the popularity of different noodle brands).
  
  But even if there isn’t an obvious quantifiable metric, you can translate the qualitative stuff by providing a rich visual experience via maps, audio or illustrations to convey the theme, atmosphere and cultural significance of your story’s topic.
  
  In the below example, colours were used to convey the different dimensions of flavour used in Asian cuisine. Additionally a packed circle chart was used to visualise common ingredients in chilli dishes with chords connecting circled ingredients that go well together.

• Balance accuracy and understanding to ensure that the data is well presented and easy to understand.
  
  The above visualisation of ‘ingredients that go with chilli’ is actually a condensed version of more than 100 different bubbles that had to be indexed on a scale of between 1 to 9 flavours (such as ‘sweet and sour’).
  
  While this is a less accurate representation of the very distinct flavours that exist within these many ingredient combinations, the authors felt this struck the right balance between beauty and simplicity. They were able to provide more detail through the illustrations and text boxes that more curious readers could explore.

• Find a common ground
  
  It can be easy to over-explain when trying to tell a story about one culture to an audience outside of that culture.
  
  Here, they recommend anchoring the angle of the cultural experience that you’re trying to analyse to a more universal sentiment.
  
  In the talk, they used an example of relating the cultural tradition of new year fortune telling to people’s universal anxiety about the future and our well wishes for loved ones, or of the popularity of instant noodles in Asia to every culture’s respective love for certain comfort foods.

2. 3D Geo DataViz: From Insight to Data-Art

Hosted by Craig Taylor (Senior Data Visualisation Design Manager, Ito World)

Craig and his team at Ito World create narrative-driven and cinematic-looking 3D visualisations.

Craig’s talk focused on how he and his team create insight-driven visualisations that reveal how the systems we interact with impact our lives. In his talk, he explained that producing this type of visualisation requires that you:

• Include granular data, since it yields more interesting results. For example, for Ito World’s project Transit In Motion, the dataset for New York City included 14.8 million locations recorded per day, 4,488 unique bus trips, and 2GB of CSV files.

• Focus on the patterns that your data is creating over time. For Transit in Emotion, this involved analysing the volume of transit usage over the period of one month.

• Make your visualisation’s design abstract to highlight the rhythm of your data over time. In the past, Craig has used a variety of spheres, cuboids, and meshes to portray what city-wide transit in motion looks like. 

3. DataViz, the Unempathetic Art

Hosted by Mushon Zer Aviv

Mushon is a Tel Aviv based designer, researcher, educator, and media activist. His talk highlighted how data viz can lack empathy, and takes inspiration from the following quote:

To ensure that your work is empathetic, Mushin says you must be aware of:

• Dark data viz, which risks tone-deafness and minimising important topics.
  
  In 2015, Mathew Lucas produced a series of infographics showing the impact of the atomic bombing in Hiroshima. Although the graphics were visually pleasing, this data viz also sparked debate, with some questioning how design should be used to aestheticise a horrific event.

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• How an appeal to empathy can be misleading
  
  Mushon cites Professor of Psychology Paul Bloom who says empathy often shines a spotlight on the individual and can be biased towards those who look like us. We find it easier to empathise with individuals, not with the masses.
  
  He also references a study from Paul Slovic in the talk, which further illustrates this idea with what he calls ‘statistical numbing’ whereby audiences seem to empathise more with individuals than with larger groups.
  
  In Slovic’s research he found that charity donations in response to descriptions about identifiable individuals earned more than double the donation value in response to descriptions about statistical lives (i.e. groups of individuals that weren’t personally identifiable). Sadly, the value of donations even decreased when statistics were presented alongside individual descriptions in the story.

• Affective empathy vs cognitive empathy
  
  According to Simon Baron-Cohen, affective empathy, which is rooted in emotion, means that you’re able to feel the same emotion or feel your own distress in response to another’s pain.
  
  Cognitive empathy, which is more rational, means that you’re able to understand someone’s perspective or imagine what it’s like in another person’s shoes.
  
  Muson relates these two types of empathy to Daniel Kahneman’s distinction between two ways of thinking:
  
  Tier 1 thinking: thinking automatically, quickly, with little or no effort and sense of voluntary control.
  Tier 2 thinking: allocates attention to the effortful mental activities that demand it. These type of operations are often associated with the subjective experience of agency, choice and concentration.
  
  It is said that Tier 2 often contextualises the thinking of Tier 1 to inform a person’s decision-making. In visualisation, the pre-attentive attributes (below) are how we use vision to communicate between Tier 1 and Tier 2. So here Mushon asks ‘can we think of empathy as an additional pre-attentive attribute for visualisations?’ because we do not get to control or rationalise it, but it can inform our more deliberate decisions.

• How to scale compassion by appealing to both types of empathy

The above image is a powerful visualisation of gun deaths in America during a single year. It begins by illustrating the life arc of one person being cut in the middle vs how many more years they could have lived for.

Focussing on a single individual’s life being cut short appeals to the viewer’s affective empathy or tier 1 thinking, aka the more emotional response, before the impact of another 11,422 deaths are visualised in the same manner as below.

• Raising awareness is not enough
  
  Data visualisations have the power to explore and explain important stories about the world.
  
  However, it’s not enough to just say something is wrong with the world. If we have built that message well, then we should also direct that message towards the path of change and actionable insights.

4. Data points are people too

Hosted by Bronwen Robertson, Joachim Mangilima, Saja Othman, Zdeněk Hynek

Data4Change is a non-profit organisation based in London that connects social change organisations with designers, journalists, and technologists to collaboratively create data-driven solutions for some of the world’s most pressing problems. This talk focused on many of their projects which have helped to deliver change in countries around the world.

An example of this is ‘A Bride With A Doll‘, which focused on the issue of child marriage. The team designed a workshop kit and a storybook that could be read from both directions, reflecting emotional experiences, based on data insights from the community.

5. Mind Games: The psychology behind designing beautiful, effective, and impactful data viz

Hosted by Amy Alberts (Senior Director, User Research, Tableau)

This talk outlined practical guidelines which can help you predict where people look at certain parts of data viz – for example, jagged lines and bar graphs are effective at drawing the user’s attention.

Amy’s team at Tableau have previously employed eye-tracking software to discover where people were focusing, gaze plots to qualitatively and quantitatively show where the eye is fixated, heatmaps to show areas of high visual tension, and gaze opacity maps to highlight areas that people give less attention.

According to their findings, the biggest attention grabbers in data visualisation are:

• BANS (Big Ass Numbers) – Our eyes are drawn to large visual elements such as big text. Below is a gaze opacity map of a dashboard with big numbers.

• Colour ­– Visual contrast relative to other areas generates attention.

• Humans and maps – Our brains are hardwired to notice other humans, so when we see human-like figures in visualisations, we are automatically drawn to them. If maps and humans are relevant to your data, it is worth capitalising on this to draw attention.

Design with intent and be mindful of the context that you control. Use clear titles and high contrast elements, ethically making use of the psychological phenomenon known as the priming effect. This will help to ensure that your audience clearly understand the story that you are trying to tell with your data.

Final thoughts

The Outlier conference was incredibly informative and packed with so much knowledge about how to create culturally relevant, socially aware content that’s also visually impressive and effective in communicating concepts.

If you are interested in learning more, you can watch a playlist of video recordings with all of the talks from Outlier 2021.

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