2016 RNC vs. DNC Convention: Night and Day

Using Nvivo, a text-analysis software, DASIL compared Clinton and Trump’s convention speeches to demonstrate the stark contrast between the two presidential candidates. The previous post briefly examined key themes in each candidate’s address using word clouds. This analysis expands on the previous post with a more in-depth comparison of the two candidates’ approaches to the following themes:

Immigration:

Table demonstrating the frequency of mention of the word “immigration” or “immigrant(s)” by count

Table demonstrating the frequency of mention of the word “immigration” or “immigrant(s)” by count

Table demonstrating the frequency of mention of the word “immigration” or “immigrant(s)” as percentage of total number of words in each speech.

Table demonstrating the frequency of mention of the word “immigration” or “immigrant(s)” as percentage of total number of words in each speech.

In Donald Trump’s speech, 10 out of 13 times in which “immigration” or “immigrant(s)” is mentioned, it’s accompanied by words with negative connotation such as “illegal”, “radical”, “dangerous”, or “uncontrolled”. According to Trump, immigration is deemed the cause of poverty, violence, drug issues, unemployment, and terrorism.

In contrast, Clinton presented herself as an advocate for comprehensive immigration integration, which is clearly demonstrated in her convention speech: 2 out of 4 times Clinton mentioned these words, “immigration” or “immigrant(s)” is accompanied by positive words and phrases. She described immigrants as “contributing to our economy” and “hardworking”.

Jobs:

Table demonstrating the frequency of mention of the word “job(s)” by count

Table demonstrating the frequency of mention of the word “job(s)” by count

Table demonstrating the frequency of mention of the word “job(s)” as percentage of total number of words in each speech.

Table demonstrating the frequency of mention of the word “job(s)” as percentage of total number of words in each speech.

Given the long-standing lag in job growth, outlining a vision for jobs creation and income gains is among the top priorities on the two candidates’ agenda. As mentioned in a previous post, Trump held a pessimistic outlook on the American economy: 4 out of 13 “job(s)” words mentioned by Trump are surrounded by words with negative connotation. The Republican nominee talked about the prospect of jobs and wages reduction with Clinton administration and consider regulation “one of the greatest job-killers of them all.”

On the other hand, Hillary Clinton chose to deliver a more hopeful view of the matter. She highlighted the prospect of good-paying jobs and the effectiveness of her policy in job creation. None of out of 18 times she touched upon the subject of employment did she make a negative remark on the issue.

Patriotism

Table demonstrating the frequency of mention of the word "America(ns)" by count and as percentage of total word count

Table demonstrating the frequency of mention of the word “America(ns)” by count and as percentage of total word count

The two presidential candidates frequently mentioned “America(ns)” in their speech, and the word clouds visualize the frequency of the use of these words between Clinton and Trump. In fact, Trump mentioned “America(ns) almost three times as often as Clinton did – both in terms of count (number of times “America(ns)” is mentioned) and percentage (number of times “America(ns)” is mentioned as a percentage of total word count).

Even though both Trump and Clinton embraced patriotism in their convention speeches, they did so in two strikingly different ways. The Republican Party and its presidential nominee portrayed America as a country under attack by all things foreign; the country is in a dark place and Trump is the one to “make America great again.” In contrast to Trump’s nationalism, Clinton talks about American in optimistic tones, emphasizing the family values – faith, community, and togetherness – that middle-class Americans adhere to.

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Portraits of Donald Trump and Hillary Clinton

2016 U.S. Presidential Race: Do Convention Speeches Predict the Winner?

After the Republican and Democratic Conventions in July, the 2016 U.S. presidential race is on between Democrat Hillary Clinton, who is making history as the first female presidential nominee from one of the two major political parties, and Republican Donald Trump, the contentious and provocative New York billionaire. The race for the White House this year is undoubtedly one of the most memorable events in the history of American politics, partly because of the stark contrast between the two candidates, from their political and economic agenda to their appeal to voters. Using Nvivo, a text-analysis software, DASIL compared Clinton and Trump’s acceptance speeches at their respective party conventions to further demonstrate these differences.

Main theme and important issues

Word cloud of 30 most frequent words in Donald Trump's speech

30 most frequent words in Donald Trump’s speech

Looking at the 30 most frequent words in Trump’s speech, we can see that the main issues mentioned by the Republican candidate are immigration, national security, and public safety. The most common words in the speech include “violence”, “immigration”, “protect”, “border”, “laws”, “jobs”, and “violence”, highlighting a dark portrait of the current state of America. Trump strongly emphasized that much must be changed in order to fix these issues, and that he, rather than a Democratic leader, will change this grim outcome by restoring law and order.
Word Cloud of 3 most frequent words in Hillary Clinton's speech

30 most frequent words in Hillary Clinton’s speech

Clinton, on the other hand, gave a more optimistic and upbeat speech. While acknowledging the current issues facing America and the work needed to be done, Clinton also highlighted the strengths that the nation brings to overcome these challenges. Some of the most frequent words in her speech are “family”, “people”, “works”, “jobs” and “together”, hinting at some issues that the Democratic presidential candidate wants to tackle. At the same time, these words center around the notion of inclusivity and staying united, which offers stark contrast to Trump’s anti-immigration stance, isolationism, and Americanism.

“We” versus “I”

Using Nvivo, DASIL also compares how often the two presidential candidates used “we” words – such as we, our, ours, and ourselves – versus “I” words – such as I, me, my, mine, and myself in their convention speeches.

Table showing Trump and Clinton's "we" and "I" words

For every time Clinton said “I”, she said “we” 1.83 times, while her Republican opponent said “we” only 1.5 times for each “I”. With a 1.50 “we”-to-“I” ratio, Trump delivered a more self-focused convention speech than his Democratic rival Hillary Clinton, whose speech has a “we”-to-“I” ratio of 1.83. The difference in use of “we” versus “I” words between the two candidates reveals much about their speaking styles, personalities, and even chances of winning the election. A Bloomberg Politics study of convention speeches dating back to 1976 finds that the public tend to favor candidates who use more “we” words relative to “I” words. In nine out of 10 elections since 1976, the general election winners achieved a higher “we”-to-”I”-word score compared to his opponent [Bloomberg]

Bar graph showing the number of "we" words per each "me" words for presidential nominees since 1976

Bloomberg points out that “we” words inspire confidence in others and also reflect the speaker’s self-confidence, which is a key quality in good leadership. Clinton’s “we”-to-“I” victory over Trump in her convention speech suggests that she’s in the lead position to win in November. Trump’s speaking style is more personal; furthermore, his I-word usage reveals feelings of insecurity, perhaps due to a lack of political background and experience on political issues.

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Meet Yujing Cao, DASIL’s new data scientist!

This year, DASIL welcomes a new member of our staff, Yujing Cao, who will be serving as the new data scientist. In her position at DASIL, Yujing will bring her expertise in data analysis and visualization to further expand DASIL’s capability to help students and faculty members integrate data analysis into research and classroom work.  In today’s big data era, enormous quantities of data are available, and Yujing will help Grinnell students and faculty explore them.

Yujing Cao is excited about joining DASIL and bringing a new level of data analysis to faculty research and teaching!

Yujing Cao is excited about joining DASIL and bringing a new level of data analysis to faculty research and teaching!

Originally from China, Yujing got her bachelor degree in Statistics from Anhui University. Her passion for data science led her to a PhD program in Statistics at the University of Texas at Dallas, where she obtained her degree in 2016. Her research was on graphical modeling of biological pathways in genomic studies. She is also interested in network analysis, machine learning, and trying different tools for data visualization. In her spare time, she enjoys reading, hiking, and exercising.

Yujing was excited about the position at Grinnell because of her strong interests in teaching and in data visualization. As she puts it:

“I wanted to look for a position which provides opportunities to create interesting data visualizations along with other data analysis work. I love using graphs to tell stories behind different data sets.

Working environment is another factor that led to my decision to come to Grinnell.  I strongly resonate with the core values of a liberal arts education. At Grinnell College, I can work in an academic environment helping faculty and students while promoting the use of data in research and learning.

Yujing also discusses a number of skills crucial to succeed in the field of data science. Data science is an interdisciplinary field requiring knowledge from mathematics, statistics, data mining and machine learning. Statistical knowledge and knowledge from other fields can help form good questions and seek direction, while programming skills (e.g. joining data sets and visualizing data) are needed for implementing our ideas. To be a good data scientist, you should possess strong programming and analytical skills.”

According to Yujing, “One of the most important qualities for any data scientist is curiosity. Curiosity encourages us to dig in and make interesting discoveries about data. Also, good communication skills can make a great data scientist. You should be able to clearly articulate your results and the implications of your findings to others, including other data scientists and people who don’t share a similar background.”

Her tip for students interested in a career in data science is to keep an open mind to learn from different disciplines and sharpen your programming skills.  In addition, a student who is interested in being a data scientist should take advantage of any opportunities to get hands-on projects that use real data.”

Faculty or students interested in meeting with Yujing should drop by DASIL(ARH 130) or her office (Goodnow 103) or contact her via email at caoyujin@grinnell.edu for an appointment.

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Visualizing the Production Function and Cost Curves

Single, static images of data trends aren’t the most effective way to communicate the ways the different elements of an equation or formula contribute to a trend.  This is especially true for introductory economics concepts such as cost curves or the production function. Dynamic, interactive visualizations that allow users to manipulate the variables contributing to a relationship which enables the audience to better understand how equations express trends.

Krit Petrachaianan ‘17 of DASIL programmed a visualization using R that illustrates cost curves and the production function, two core concepts of introductory economics.  DASIL’s visualization allows users to manipulate the different parts of the equations that define cost curves and the production function. For instance, users can manipulate the costs per input (denoted r and w) and the amount of a particular input (denoted K for capital and L for labor). Users can also define the productivity of the firm’s inputs.

Cost curves visualize the costs of producing different levels of output. The total cost of production for a business can be subdivided into fixed and variable costs.  Some costs, such as raw materials and production supplies, change proportionally as more or less of the good or service is produced and are known as variable costs.  Other costs, such as the annual rent or salary of workers, are independent of the level of goods or services a business produces and are known as fixed costs.

The production function shows the relationship between the output produced by a firm from a given amount of inputs (i.e. labor and capital). The productivity of inputs in producing output can vary in three ways: 1) with constant productivity, the additional output produced by a given amount of input is constant as more of the input is used, 2) with diminishing productivity, the additional output produced by a given amount of input declines as more of the input is used, and 3) with increasing productivity, the additional output produced by a given amount of input increases as more of the input is used.

Explore DASIL’s latest R visualization below, as well as in the Graphs section of the Data Visualizations page and in the Economics tab of the DASIL website.

costex3

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The Common Mistakes Made in Creating a Data Visualization

Oftentimes the best way to learn about how to do something right is by learning what not to do, especially for how to make good data visualizations. WTF Visualizations is a website that compiles poorly crafted data visualizations from across the web and media. Below is a sampling of some of the visualizations featured that illustrate some of the most common data visualization mistakes:

  • Absence of Proper Scaling

Including proper scaling is essential in accurately representing your data. In the example below, the differentiation between values is misrepresented due to the absence of a clear scaling measure. The 52% measure does not appear to be as large as it should be in comparison to the other bars, and the 13% figure appears to be much larger than 3% when compared to the two 10% figures.

datavizmistake1

  • Too Much Information

While the inclination is to include as much information in visualizations as possible, oftentimes including too much information detracts from the clarity and concision that is essential to good data visualization. The example below perfectly illustrates how including a myriad of different categories can muddle your visualization, as well as the importance of clear axis labels and descriptive titles.

Ensure that the data that you do decide to visualize is comprehensible to your audience: recode categories when there are too many; don’t include measures that illustrate the same phenomenon; don’t include 10 different variables when 3 will do. If need be, include more than one visualization to highlight different sub categories or variables.

datavizmistake2

  • Bad Math

Always double check your math before sharing your visualization to the public. You may run the risk of misrepresenting your data, as well as appearing as though you are not capable of simple arithmetic. The example below illustrates this point perfectly: while the creator uses a pie chart, the sections do not add up to 100%, but rather, 128%. The sections of the pie chart also do not accurately reflect the values they supposedly represent: The “51% Today” section, for instance, should be taking up a little more than half of the pie chart.

datavizmistake3

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