Since the election, many have voiced their perspectives on what led to the surprising results. One of whom is Bernie Sanders, who I believe has a clear view. As he states, Americans are in pain. Millions of workers make less in inflation-adjusted wages than they did 40 years ago. One half of older workers have nothing in the bank as they enter retirement. Young Americans who graduate from college with $100k in debt make $12/hour. The list goes on…
Donald Trump addressed the people’s anger, pain, and frustration. He did it directly, in an emotional way, amplified through social media. On the other hand, Hillary focused on social justice and equality issues, which for the record are important. However, how can one care about another’s needs if she does not feel her own are being addressed? As Maslow’s hierarchy indicates, one’s basic needs must be met before higher order needs, such as those of the collective, can be realized. Hillary’s campaign did not see that its messaging missed this tenet of human psychology.
Yes, there is irony in a Billionaire relating to the common man or woman, but between the two, Trump did the better job resonating, and that’s why he won the election.
4 Days before the 2016 US Presidential Election, what does the Twitterwonk Model show?
Here are monthly, weekly, and daily views starting June 2015 through October 2016.
The model, which is a function of Twitter activity, shows Trump in the lead.
While the model correctly predicted the primaries, jury is still out whether the same dynamic applies to the general election. Regardless, 2016 will be remembered as an election in which social media, on both the left and the right, played a dominant role. I believe social media will play an even greater role in future elections, shaping political communication for decades to come, as well as becoming a primary source for the measurement of public opinion. In the meantime, let’s see how this election plays out in the remaining days…I predict many will be surprised at how close the results turn out to be.
In a way, this election is a referendum on “do actions speak louder than words”, is what people do in the privacy of their internet browsing more reflective of their future behavior than what they tell pollsters? And while I have focused on twitter as a barometer of public opinion, there are other data sources that could signal the private thoughts and future actions of voters. The linked NYT article, “If they Google you, Do you Win?”, mentions using the Google queries “Trump Clinton” vs. “Clinton Trump” as signals of voter interest, with the respective queries reflecting bias towards the candidate listed first, i.e. “Clinton Trump” would reflect bias towards Clinton. Using this methodology, I researched Google trends for Battleground states to see where public opinion may be. The data are displayed below.
For the month of October 2016, “Trump Clinton” leads “Clinton Trump” in every state with the exception of Nevada.
You might say Trump is a polarizing celebrity, and for that reason he may be top of mind even if the individual plans to vote for Clinton. Okay, well then let’s penalize Trump 10%. Even in that case, ‘Factored “Trump Clinton”‘ indicates that, with the exception of Nevada, the three states that are in play are Virginia, Iowa, and Florida.
So while it is unclear in which direction the election will result, I believe we may be surprised at how close the results turn out to be, and that one thing we may remember is the discrepancy between what was reported in the polls leading up to the election and what actually happened online. We only have 4 days left to see which source provides a clearer signal of truth, and until then….Good luck to both candidates!
“Many people will be shocked at how close this election will be.”
1. Twitterwonk examines the leading candidates from both US political parties and conducts analyses using Twitter data for the following categories: Volume, Engagement, and Followers.
2. The categories are combined and weighted to form the Twitterwonk Score.
3. The results are displayed by political party as monthly averages .
Why Does Twitterwonk Matter?
1. Twitterwonk correctly predicted the Republican and Democrat Nominees.
2. The 2016 Presidential election continues to play out on Twitter. Some have called it the “celebrity” election.
3. Twitter has the greatest number of users, 300 million, in the history of the platform.
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To win the mind you must win the heart.
And so less than a month from the election we enter the depths of an emotional swamp.
The founding fathers warned of mob rule. They designed a democratic republic, just in case the people got it wrong, the elected officials could correct course.
But did the founding fathers envision the electorate bombarded with emotion laden messages 24/7? Really, has anyone out there not made up his/her mind, whether to vote and who for? At this point, we are desensitized and perhaps entertained, knowing that we have a few more weeks left of mud slinging, like bearing the last episodes of the Bachelorette before the grand finale.
One thing is clear, however. We begin a new chapter in US politics, one in which social media plays a pivotal role. Yes, emotion has been present since the beginning of politics, it’s central to humanity, but the channel through which the emotion is administered is new and different. This is click-bait, ratings-boosting, reality television at its finest–21st century political campaign strategy.
Over the past month, I have had the opportunity to present the work here on the website to various audiences around New York City. Needless to say, it has been an experience. Audiences are skeptical of Trump and his chances of becoming the Republican nominee, much less the President of the United States. For those willing to entertain the idea, they make allusions to racism as the source of his popularity. After repeatedly confronting this sentiment, I set out to better understand how race influences election results. Full disclosure, this is a touchy subject. However, data science is a tool for gleaning truth, and so here is what I found out.
After reviewing the literature on the rise of Trump, I discovered underlying themes: authoritarianism among whites, and perceived threats from outsiders. So the hypothesis goes, whites who score high on authoritarianism vote Republican. Voters who score high on authoritarianism resist change and rally together against perceived threats from outsiders. So how might we model white resistance to change, especially to outsider groups?
Links to the literature:
I went to US census data to track population change over time, gathering stats on the number of whites over 18 years old for all US states in 2002 and 2012 as well as the total populations for all US states in 2002 and 2012. With these data, I computed a change in white ratio, over the 10 year period. The math looks like this:
For each state:
[(# of whites in 2012 – # of whites in 2002) / (# of whites in 2002)]
[(total population in 2012 – total population in 2002) / (total population in 2002)]
Each result was multiplied by -1, so large declines in the white ratio turned positive, and vice versa.
Here is the data, in descending order (largest decline at top), and color coded by 2012 presidential election outcome (blue = democrat, red = republican).
As the visualization shows, of the 10 states to see the greatest percentage decline in white-ratio between 2002-2012, 9 voted Democrat in the 2012 presidential election.
Of the 10 states to see the lowest percentage decline in white-ratio between 2002-2012, 7 voted Republican in the 2012 presidential election.
We have a strong predictor of election outcome: decline in white-ratio over a 10 year period. A high scoring state has a 90% chance of voting Democrat.
For both groups, it was the case that the denominator, total population, changed faster than number of whites. This is to say, white citizens over 18 years old are not declining; however, the total state populations are growing faster. For the bottom 10 states, the two ratios are more similar, although total population is still growing faster.
Interestingly, for both groups, states are equally likely, 60% likelihood, to have white-ratios below that for the entire US (~0.78).
Thus, the analysis supports the hypothesis: authoritarian whites are more likely to vote republican (70% likelihood among the bottom 10 states) and are likely to perceive threats from outsiders (the below average white ratios) as well as resist change (the low percentage decline over a 10 year period).
Conversely, voters open to change (the high percentage decline over a 10 year period) are more likely to vote democrat (90% likelihood among the top 10 states).
This analysis has proven helpful in preparing for future presentations, and I hope you have found it helpful in your pursuits, or at least interesting!