Major polling organizations took a drubbing in the press after they failed to predict the outcome in last week’s U.S. elections. At least one AI-powered model fared much better.

What’s new: Several companies that offer analytics services used machine learning to predict the next U.S. president. Their results ranged from dead-on to way-off, as reported by VentureBeat.

How they work: The companies analyzed social media posts to determine how large groups of people feel about a particular candidate.

  • Expert.AI came closest. It analyzed 500,000 posts and found that challenger Joe Biden was more closely associated with words like “hope” and “success,” while incumbent Donald Trump was often mentioned alongside words like “fear” and “hatred.” Ranking these words according to their emotional intensity and frequency, the system predicted that Biden would win the popular vote by 2.9 percentage points. As of November 11, Biden’s actual margin was 3.4 percent according to The New York Times.
  • KCore Analytics drew on a pool of 1 billion Twitter posts by influential users and those containing influential hashtags. It used the popularity of a given user or hashtag as a proxy for a subset of the voting population and scored positive or negative sentiment using an LSTM-based model to predict each candidate’s chance of victory. In July, it predicted Biden would win the popular vote by 8 to 9 percent — nearly triple the actual measure as of November 11 — and wrongly predicted the outcome in several swing states.
  • Advanced Symbolics parsed public data from Facebook and Twitter to create a list of 288,659 users it considered a representative sample of U.S. voters. Its method relied on linking the way people talked about certain issues, like crime or Covid-19, to a certain candidate. The company predicted that Biden would sweep the electoral college with 372 electoral votes. The democratic nominee has gained 279 electoral votes as of November 11.

Behind the news: AI systems have made more accurate political predictions in the past. In 2017, Unanimous.AI correctly forecasted that Trump’s public approval rating would be 42 percent on his 100th day in office. KCore last year successfully predicted election results in Argentina, while Advance Symbolics claims to have accurately predicted 20 previous elections.

Why it matters: Human pollsters arguably performed poorly this year. But their jobs aren’t threatened by AI — yet.

We’re thinking: There’s plenty of room for improvement in predictive modeling of elections. But, as we said in last week’s letter, probabilistic predictions — whether they’re calculated by a human or a machine — are intended to convey uncertainty. The better people understand probabilities and how they’re modeled, the more comfortable they’ll be when events don’t match the most likely outcome according to public polls.

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