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.
Over the past months I have developed a process for understanding elections through the lens of Twitter data. Combining and comparing Twitter metrics, the process provides an ordinal representation of where the candidates stand amongst one another. I am excited to announce the process will be unveiled over the coming weeks as a website, named Twitterwonk. It is my hope Twitterwonk will serve as a tool for better understanding elections as we further immerse ourselves in the age of new media. To kick us off, here is a visualization displaying the Twitterwonk Score for the Republican and Democrat frontrunners. Good luck to all the candidates, and may Twitterwonk serve you.
As of January 27, 2016, Bernie Sanders leads Hillary Clinton in the Iowa and New Hampshire polls (Real Clear Politics). Here we present how Trump hit, Hillary fired back and left the goal open for The Bern.
Displaying a composite score, calculated with Twitter data in a process outlined in my previous post, the visualization presents the decline that allowed Bernie Sanders to rise above Hillary Clinton.
It started on December 8, 2015, when Donald Trump released his statement on preventing muslim immigration. Over the next two weeks, Hillary Clinton would respond to Trump in a fury of statements on foreign policy, ultimately leading to her decline. And while Bernie Sanders also responded to Trump, he did not waver from domestic policy, which better resonates with the compassionate tone of both Democrat candidates. Christmas brought a change in tone for Hillary, but by this point it was too late: Sanders had already gained the lead. Here are the kind of statements on domestic policy that reversed Hillary’s decline.
“There’s no reason for teachers and nurses to ever pay higher tax rates than top CEOS.”
“It shouldn’t be so hard to be a working parent.”
“We need to make it easier for women to get ahead at work while still being there for their families.”
NYMAG just published a profile on Trump supporters and the appeal to “Testicular Fortitude”, i.e. strength. While many pages could be spent on the topic, it rings clear that the Commander-in-Chief must be compassionate at home and ruthless abroad. A word no short of “destroy” is expected in reference to America’s enemies (used by President Obama, Trump, and Sanders), especially during times of uncertainty. Hillary’s gift for empathizing with the other has made her a skillful Secretary of State, and it may have cost her The White House.
Moral Foundations Theory is a social psychological theory intended to explain the origins of and variation in human moral reasoning. The theory proposes moral foundations such as fairness, care, in-group, authority, and purity, and has been popularized by psychologist Jonathan Haidt in his book The Righteous Mind.
Haidt describes human morality as it relates to politics and proposes differences between conservatives and liberals as they relate to the moral foundations (TED Talk). Specifically, whereas conservatives appeal to fairness, care, in-group, authority, and purity equally, liberals appeal to fairness and care more than they appeal to in-group, authority, and purity.
Setting out to observe this phenomenon within Reddit Political Communities, I performed word frequency analyses on the /r/Republican and /r/Democrats corpora, totaling the words for each moral foundation, as defined by the LIWC dictionary. Comparing the totals, I found a trend consistent with Moral Foundations Theory. The visualization shows the moral foundations for /r/Democrats normalized against those for /r/Republican, with each value for /r/Republican set at 100%.