The Instant, Morbid Reaction to the "Worst Debate in History"
The internet didn't just hate the debate. It responded with a level of morbidity no-one expected.
I wrote an article before, during and after the presidential debate. This is a brief extract. You should read that article to learn how the numbers were computed, and how you can contribute to analysis of the next debate by writing an algorithm that predicts emoji counts.
As noted, I was completely overwhelmed by what happened the other night. I don't feel so bad because arguably, moderator Chris Wallace was also caught off guard. However the servers were not and they instantly provided an assessment of what millions of people on Twitter thought about the debate. They hated it immediately. And not hate in the sense of frowning faces. Hate in the sense of skulls, black hearts and other deathly emojis.
It did rapidly became apparent, to all observers I would venture, that one debate participant was adopting a high risk strategy. The President attempted to dominate speaking time, and even Republican Chris Christie would observe that Trump came in "hot". This was, it has been universally agreed, fairly unusual for a presidential debate, of which many have been acrimonious.
Trump's strategy complicated the analysis. I had been hoping that two minutes of speaking time, alternating between participants, would permit a reasonable guess as to which tweeting emotions were directed at which participant. No such luck.
One plausible hypothesis, which you are welcome to disagree with, is that reactions to this debate might have therefore been closer to a referendum on the debate quality itself, and its departure from the historical norms, rather than more idiosyncratic signals on particular issues. Certainly, time compartmentalized performance assessments and reactions to specific talking points are harder to discern when one person is speaking more or less the entire time.
We didn't need to wait for news outlets to declare this the Worst Debate in History (for example). Almost immediately a flood of negative emotions exceeded their expectations. There were very, very few moments which could in any way be expected to solicit happy responses. I snapped the closest thing to levity I could find in this debate. And behold! For a brief moment, winks and kisses raced up the charts.
But as we know, this was mostly deep into negative territory. When the topic turned to race relations, policing and protests, the there was no winking to be seen. The responses that rose to the top took on much more sombre note. "Heavy Black Heart" and "White Smiling Face" took positions #2 and #3, a rather unfortunate juxtaposition, and naming of emoji's, although they lagged behind the persistently performing "weary face". We can safely label the weary face emoji the emoji of the night. It was exhausting to watch.
When COVID-19 was discussed, deathly tropes arrived. The use of skulls rose well above the usual expected usage (the benchmark background usage of emojis was arrived at by competing prediction algorithms, which were unaware of the debate context). I don't know who uses skulls on Twitter. I think I would rather not know.
We have undoubtedly stumbled onto something, but this is a first experiment. It is perhaps impossible to discuss the purely empirical aspects of this without seeming to take a political stance, or bringing one to the analysis. I realized this almost immediately and decided to record the entire thing. Reach out if you'd like a complete 20Gig video recording of the standardized rankings of the emojis - in real time - side by side with the captioned debate.
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You can make up your own mind about who won the debate, of course. However a somewhat brave interpretation of this data is:
- People hated the debate
- (more speculative, but supported by betting exchange movements) Many attributed the quality degradation to the President
Trump came into the debate a slight underdog and began to slide almost immediately. We can't read a precise absolute probability from venues like Betfair due to time value of money and commission effects, but Trump is roughly 5/6ths s likely to win as he was before the debate - according to those markets. He has fallen from slightly less than 1 in 2 to a probability closer to 1 in 3.
The micropredictions suggesting this were calculated immediately, by definition. However the betting markets took twelve hours to absorb the debate - with only about half of the movement in implied probability occurring during or shortly after the debate. In fairness, time will tell if that is an under or over-reaction.
Regardless of your political persuasion, I hope you find this interesting. I certainly did. I may put more effort into producing micropredictions of sentiment with a little more polish in the next debate ... assuming the concept of a debate survives this one. The algorithms will be better by then as well, and there will be more emojis, we promise.
What's preventing your business from using the same technique?
Nothing.
The same data standardization and data surveillance capability is automatic any time data is published to the prediction network (including obfuscated data streams if you wish).
In the article I provide your technologists, or quants, with everything they need to perform a similar live analysis (on any other source of live data your business produces) using just a few lines of code. I also speculate on why they will be reluctant to do that if you don't hold their feet to the fire.
Nobody wants to believe that their artisan model building activities will be replaced by a prediction network sooner or later - but I imagine librarians didn't expect to be largely replaced by Google search.
Join us at www.microprediction.com when you grok it.
About me
Hi I'm the author of Microprediction: Building an Open AI Network published by MIT Press. I create open-source Python packages such as timemachines, precise and humpday for benchmarking, and I maintain a live prediction exchange at www.microprediction.org which you can participate in (see docs). I also develop portfolio techniques for Intech Investments unifying hierarchical and optimization perspectives.
Economist, Data Scientist + Adjunct Prof @ NYU
4yGreat stuff, Peter Cotton, PhD! The only thing, though 😑 is that after reading this piece... my brain is forced to relitigate whether or not I choose to believe that damn event even happened 😅