This article is a data visualisation article on whether the Queen is more of a celebrity or a head of state in the eyes of general public in 2017.

There seems to be a consensus among people whom I have encountered that the Queen of England is a mysterious figure. However, no one really seems to know what she actually does – other than that she’s the Queen of England. As an International student who currently lives in London and loves The Crown (check it out on Netflix!), I feel obliged to use Quid – a visualisation tool I acquired access to in the summer – to explore whether the Queen is more of a celebrity or head of state in the eyes of general public in 2017.

The Search Phase

The search that I based the network analysis on was: (Queen of England* AND England* AND Popularity) OR (The Queen of England* AND Britain* AND Popularity). I avoided adding more related terms to keep the search broad, as the aim of the search was to find all published information related to the Queen.

I filtered the sources to only include published articles within the past 1 year (at the time this was written, the dates included are revised from October 2016 to October 2017). All articles published in different countries are included, but only from top-tier sources.

At the end of the search, 2,069 unique stories were found, with published count of 5,696 and social engagement count of 1,911,395. Thus, N = 2,069 published news articles or texts.

The Explore Phase

During the explore phase, I updated my network to ignore unrelated terms (e.g: celebrity, queen artist, rapper, etc.) that might lead to incorrect usage of the term “Queen” and boost terms related to the Queen of England (e.g: Elizabeth, Prince Philip, Windsor, etc.). I also specified the clusters to be focused and specific.

I then merged and categorised the different sub-clusters into a few relatable clusters – given that they are covering about the same topics. I deleted sub-clusters that are not related to the current Queen of England (Queen Elizabeth II).

I found that there are 8 large clusters on the topic (listed according to percentage):

Diagram 1. Network Analysis of Clusters on topics related to the Queen of England

1. Queen’s Ruling Period (19%) – anything related to examinations, opinions, or news on the Queen’s fitness to rule due to her age and health conditions.

2. Encounters with Head of States (16%) – the Queen’s meetings with various head of states, both aboard and in England.

3. Opinions on Royal Families (15%) – news related to any member of the royal families, mostly dominated by Kate Middleton, Prince William, Duchess of Cornwall, and Prince Harry.

4. Queen Movie Portrayal (10%) – media portrayal of the Queen (including The Crown on Netflix, of course!).

5. Influences on General Opinions (10%) – Queen Elizabeth’s ability to influence the general public’s opinions on domestic matters or public policy through speeches and statements.

6. Domestics Matters/Turbulences (10%) – Queen Elizabeth’s visit or actions when overcome domestic crisis such as terrorist attacks, epidemic, and economic crisis.

7. Foreign Diplomatic Influences (9.4%) – anything related to the Queen’s opinions and influences on foreign affairs.

8. Lifestyle (7.8%) – news related to the Queen’s lifestyle choices.

The Analysis Phase

From the network analysis above, the Queen’s celebrity status is shown through several clusters: Opinions on Royal Families (15%), Queen Movie Portrayal (10%), and Lifestyles (7.8%) – amounting to 32.8% of all of the articles posted.

Meanwhile, the Queen’s status as Head of States are shown through Foreign Diplomatic Influences (10%), Encounters with Head of States (16%), Domestic Matters/Turbulences (10%) – amounting to 36% of all the articles posted.

Remaining clusters – amounting 31.2% of the posts – do not indicate whether the Queen is seen more as a celebrity or a head of state, and are therefore neutral.

I decided to analyse the sentiments towards each cluster:

Diagram 2. Network Analysis of Sentiment towards Clusters on topics related to the Queen of England

There are three different colours in the graph: red equals negative sentiment, green represents positive sentiment, and finally, yellow shows texts with neutral sentiment. The graph indicated that there is strong negative sentiment towards the Domestic Matters/Turbulences and Queen’s Ruling Period clusters.In the Domestic Matter/Turbulences cluster, this is mainly caused by Manchaster Bombing incidents earlier in the year while in the Queen’s Ruling Period, the negative sentiment is instigated by worries over the Queen’s and Prince Philip’s frail health conditions.

The findings encouraged me to identify the evolution of these negative sentiments further, and so, I ran a timeline diagram for the time period with Social Engagament on the y-axis and combined sentiment score as the bar value:

Diagram 3. Timeline Diagram showing Social Engagement on y-axis and Bar representing Sentiment Combined Score

The first peak of social engagement and negative sentiment towards articles written about the Queen happened in May 2017 where Manchester Bombing happened. The articles written mostly cover about the tragedy and how Queen Elizabeth visited and cared for the bombing victims; hence, the negative sentiment was not targetted towards the Queen. The second peak occurred in July 2017 where articles about the Queen’s lifestyle suddenly spiked – perhaps due to her returns from short break due to flu in June (shown as a small rise in negative sentiment). So far, the evidence leans towards the Queen viewed as head of state more than a celebrity.

To ensure that the sentiments are geared towards the Queen, I selected only nodes in which primary mentions are Queen Elizabeth II. I then viewed which clusters contain these nodes in a timeline graph to see the evolution of sentiment scores of Queen Elizabeth throughout the year.

Diagram 4. Sentiment Combined Score Scatterplot on Queen Elizabeth II for October 2016-2017

In general, the sentiment score is mostly positive. As mirrorred by other graphs, the two clusters that generate most negative sentiments are Queen’s Ruling Period (whenever the Queen or Prince Philip get sick) or Domestic Matters/Turbulences (whenever a domestic attack occurs).

It is also notable that there is a balance between articles that portray the Queen as head of state and as a celebrity. The nodes that generate most sentiment values are either about the Queen’s lifestyles, Queen’s Ruling Period, or Domestic Matters/Turbulences. As the public demonstrates as much reactions towards the Queen’s lifestyles as the Queen’s role as a public leader, this graph does not show conclusive evidence towards our hypothesis.

It is also worth investigating the amount of word counts each clusters contain to measure the number articles and details are written on each cluster:

Diagram 5. Word Count of Each Cluster

The top two clusters, Encounters with Head of States and Queen’s Ruling Period depict the Queen as a head of state. However, it is also apparent that the third and fourth top clusters are Opinions on Royal Families and Queen Movie Portrayal – clusters which depict the Queen as a celebrity.

To investigate further, social engagement on these written articles are measured:

Diagram 6. Social Engagement Count of Each Cluster

From this graph, it can be seen that despite high word counts on Opinions on Royal Families and Queen Movie Portrayal clusters, they have low social engagement counts when compared to Queen’s Ruling Period and Domestic Matters/Turbulences clusters. It seems that the number of word count towards clusters that depict the Queen as celebrity might be skewed as media tries to monetise on Royal family gossips – even though the public is not engaging with these contents.

From graph 5 and 6, there is an evidence that the public is most engaged with articles that write about the Queen when she upholds her duty as a head of state.

To conclude, the data from Quid indicates that the public views the Queen more as a head of state than a celebrity. I acknowledge that this finding needs further investigation. The Queen’s status as head of state also generates celebrity status, and so, more data is needed to separate these correlations. Furthermore, more data is also needed to isolate social engagement on the Queen’s role in certain events as certain engagement (e.g: Manchester Terrorist Bombing) could exaggerate article engagements as the event itself is already viral.