It has been more than a year under Donald Trump’s administration. Trump has made a lot of groundless claims and job promises which happen arbitrarily. Some newsagents keep a record of it and visualize the frequency. To criticise the act of the president in a more grounded way, The New York Times compares the lies frequency between Trump’s and the former president Barack Obama. We also dug deeper into one of his promises in his election platform which is job creation and employment enhancement. From which, we adopted a project from ProPublica. The following will discuss the visualization, the use of words and the effects of the two projects. 

1. Graphs

1.1 Comparison of Trump and Obama’s number of lies in broken line graph

The horizontal axis represents time since inauguration and the vertical axis represents the number of lies. As we can see, the curve that represents Obama is stable but another curve which represents Trump rises sharply.

1.2 Comparision of Trump and Obama’s number of lies in dots plot graph

In Trump’s Lies vs. Obama’s, there is a dot graph which represents the number of blatant falsehood. The quantity of the dots represents the number of lies and their density represents the frequency they lied. Trump’s dots are most densely packed while that of Obama’s are much more sparse. With the help of these two graphs, audiences can get their answer even if they don’t read the text.

There is no doubt that this is a good visualized graph, but actually, it repeats the information taken from the previous graph. It doesn’t show anything except the number of lies.

The work What Happened to All the Jobs Trump Promised also talks about Trump’s fake claims. Its graph is very unique. It uses the silhouette to represent the number of jobs instead of just using the area of the circle. It appears to the audience that some people do get jobs because of the Trump’s claims. It uses graphics model rather than the number, which can help create mental pictures. The gradient of color gives readers an idea of the size of the employed population each graph refers to.

It is recommended to disperse the people who should be highlighted instead of getting them together. In my opinion, a circle cannot show the degree of dispersion clearly.

2. Text

The use of words in these two publications are easy to understand. The What Happened to All the Jobs Trump Promised one uses fewer words in an attempt to emphasize their graph. They both can inform readers about the context of the stories effectively. The following will be in the text that in accordance with the visualizations.

For Trump’s lies vs Obama’s, the graph presents the difference between the number of lies that Trump and Obama created during their presidency in a clear-cut way. The small chunk of text next to the broken line graph contains key terms such as “10 months” and “six times” so as to save readers’ time on comparing their difference. The whole publication has also cleverly utilized red and blue color to differentiate the two which helps readers process their difference within a short time.

The list below from the article compares the quotations of Trump and Obama’s public lying. There is an elaboration on the reality after each lies being announced with the corresponding dates marked in red. Keeping the raw text helps bring up the story because it allows the readers to read through the actual lies that the two POTUS (presidents of the United States of America) said during their administration. Readers can not only compare the frequency of the false claims but also to take a look at the content of the claims in terms of the use of language. It was shocking to see the amount of lies of Trump has already outnumbered that of Obama who has finished his eight terms in the office.

What Happened to All the Jobs Trump Promised puts more focus on the data visuals and does not have long trunks of text. The text beside each circle-shaped graph acts as a caption of the graph yet they provide a crucial and adequate explanation to the graphs such as the number and the legend.

In addition, only one textual paragraph in the What article provides statistics. The readers can process the information immediately. It caters to the fast reading speed nowadays.

The last part of the investigative story is a tremendously long list of occupation status of individual companies that Trump had mentioned in his promises of creating jobs with. Similar to how the Trump’s article deals with Trump’s quotations, What’s article lists out the claims word by word and put down the real situation next to it using definite figures and possible quotes from the particular companies.

1.4 Interaction between the visual elements and text

In both projects, we can see the interactive data analysis working in a loop manner, starting from the listing of the problem, interpretation of the situation, data gathering and transformation, querying and modeling results to visual mapping. The findings of the analysis, which is how Donald Trump unable to keep his words of helping the unemployed population, is further reinforced by the interaction between the text and graphics.

What happened to All the Jobs Trump promised makes sense of the story through the interaction between the graph of American population circle and the text. The size of the employed population, which is represented by the number of orange silhouettes (one sihouette represents 1000 jobs), shrinks and the color distribution changes as the text goes down. There is also text on the right which help readers evaluate as they make their way through the graphics.

The issue of Donald Trump making fake claims adding and saving American jobs in the article, is first successfully demonstrated by the contrast between two visual graphics, with the former depicts a huge circle of 2.4 million American population having their jobs secured by showing all the orange silhouettes and the latter, which shrinks to a tiny circle of 206,000 employed population.

As the visualization goes further down, the situation of Trump lied about creating enough job positions is further interpreted through gathered data. As the American population circle on the left shrinks much further, the text also reveals that the number of jobs created, which is potentially attributable to Trump, is much less than the number that Trump has promised. Among the 136,000 new positions, only 63,000 of them can be related to Trump, as estimated by the companies that did the hiring.

Visualisation of Trump lying about the number of job positions he would fulfilled is further reinforced by the listing some of his fake claims, ranging from the number of positions in electronic companies Carrier being slashed, not having the intention to hire more people through corporation with Chinese e-commerce company Alibaba and making groundless statement that his visit to Saudi Arabia and importing more Boeing jets into America would create more jobs. Graphs on the right help elaborate these claims by showing the changing color schemes and the shifting of the size of the employed American population

The table at the end of the article gives a concrete picture of all the fake claims Trump has created by highlighting the quotes on Trump’s promises, the actual hiring status of some companies and the comparison of the number of jobs Trump promised and the actual number of jobs have been created.

For instance, telecommunication corporation Softbank could only provide 100000 jobs, contradictory to Trump’s promise of creating 500000 jobs. In addition, it was actually funded by SoftBank launching with Saudi Arabia even before Trump’s presidential election. Trump does not play a role in enhancing the employment population.

In Trump’s lies and vs Obama’s, the main idea that the current American president Trump lies more frequently than ex-president Obama is mainly reinforced by the textual elaboration, with less focus on the graphic visualization. There are only three graphs in the article, but they are the main threads that the text is based on.

Take one of the graphs in the article as an example. Apart from more densely distributed dots appearing on the row of Trump’s falsehood, the author also goes deeper into the underlying reasons behind such phenomenon.

One of the factors which contribute to his falsehood is his pathological tendency to say whatever helps shape his reputation, which makes him virtually indifferent to reality. For instance, the author explains that he makes misleading statements and mild exaggerations about economic statistics and making groundless accusations against political opponents way beyond Obama. In addition, American people are left with nothing that could be defended even if the majority disagrees with his imprecision.

The author further points out that using the word “lies” is unfair to previous presidents Obama. When Obama was being aimed with a spear regarding his statement that Obamacare can keep the existing health insurance of Americans, he took back his words. His falsehoods were used on selling his policies or to show the effort he instilled into solving an issue.

In contrast, Trump often tries to degrade people telling the truth when he is caught lying, be they government officials, lawmakers or his assistants. To him, the truth is not relevant or important, which is the demonstration of his political strategy.

According to the author, by calculation, Trump has told 103 separate, repetitive lies within less than a year in office. In contrast, Obama only told 18 over his entire eight-year career. On average, Obama told two lies yearly and Trump told nearly 200 lies a year. This situation is echoed by the “list of lies” below, where Trump’s falsehoods within a year are about seven times that of Obama.

The frequency of Trump lying is about one to two days on average. However, Obama only lied once every one to three months on a yearly basis.

3. Conclusion

To conclude, the data visualization of both projects can effectively reinforce the message that current American president Donald Trump has the tendency to lie pathologically in order to shape the person he wants to be. What Happened to All the Jobs Trump Promised focuses more on graphical interpretation while Trump’s lies vs Obama’s puts more emphasis on textual elaboration.

For What Happened to All the Jobs Trump Promised? , as a work of communication, it shouts out its main idea successfully through analyzing Trump’s falsehoods in a progressive order. The orange graph with silhouettes, which represents the employed American population, has shrunk progressively as layers and layers of Trump’s lies in creating more jobs being unveiled through the text.

For Trump’s lies vs Obama’s, even though there are only three graphs, the emphasis on text has helped elaborated the trend shown in the graphs. Readers can get a clearer picture of various reasons that contribute to Trump’s shocking large number of lies.

As a work of communication, both projects have successfully helped readers understand and connect with the context of the article.

 

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Author/ Celia Lai, Deng Xiaohang and Erin Chan ( JOUR2106 Data Visualisation (2018) – Group 1 )

Editor/ Jessie Pang

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Data News of the Week (DNW) is a weekly issue of news summaries hand picked by our editors. It features a GLOCAL (global+local) perspective for the topic of concern. It tracks the latest developments from the industry and academics for methodology, tools, datasets and news agenda.