Michelangelo D’Agostino: Physics and presidential politics

Background

• harvard undergrad, berkeley PhD, worked on Icecube (neutrino detection)

Approach

• neural networks and support vector machines to score liklihood of particular outcomes (neutrinos or voter stats)
• Data collection — mass profile data from Facebook / Twitter.

Campaign organization

Tech department

• voter calling tool from web interface
• dashboard – online field office to match up activists
• database maintenance

digital team

• email fundraising
• social media pages for the president
• digital advertising

Analytics

• cave “learned from physics how to be in a closed windowless room with 40 people”
• modeling / polling / experiments

Building models

• using historical data make models for predictions.
• how likely is someone to donate / change vote in response to door knock / volunteer?
• use time and money to target persuadable people and infrequent voters.
• all individuals in battle ground states get support, turnout, persuasion, and getout-the-vote.

Information

• states provide pubically for all registered voters how much they vote, age, etc (not who they voted for)
• political parties also collect similar data.
• Match this data to 5-10K people you’ve called on the phone. Use to train models.
• have statistical models of liklihood of effect, rather than gut feeling and attack ads.
• cable TV collects data on what channels people are watching. correlate with data files. Intermediary controls so that no individual has all the data on a given individual (what person X watches and how X votes).
• can advertize to persuadable voters on cheap niche channels rather than big public channels — make money go further.

Fund raising

• 504 million of over 1 billion raised came from digital: data driven analysis of email campaign.
• randomized control experiments. Starts as a dozen different version sent out to small random control group. Analyze returns (donations/clicks/unsubscription). Pick winning email send to list.
• difference in returns millions of dollars per email, 4-10 fold difference in return rate.
• How much to ask for? different emails for new vs. repeat contacts?
• people respond better when you spam them then if you show restraint.
• optimize form appearance: fast loading helps. ask people to enter 1 thing at a time rather than a whole form.

Monte Carlo simulations

• How big should a test group be? (optimization of selecting the best and not burning through too much of the list)

social media

• 50% of 18-29 yr olds are unreachable on phone: no number or refuse to pick up. Only getting worse.
• 85% of them are on Facebook.
• 24 million Twitter followers
• Targeted sharing on facebook. Authorize campaign’s app: gives access to email/name/etc + photos + friends birthdays, likes and locations.
• write to you to reach out to your friends in battleground states with a personal message to ask them to vote, (in particular if model predicts they are unlikely to get out to vote).
• high response rate on people sending messages (10% clickback). 1 million users authorized app, 100 million people get access.
• Twitter
• sent targeted direct messages from the president’s account / first lady’s account, asking people to contact their followers.
• people really liked getting messages directly from VP/pres etc.
  • use peoples tweet words + SVM to ID if individual is conservative or progressive.
  • conservatives tweeted more about Obama than progressives. Both tweeted about Romney at similar rates.
  • don’t use known info about contacts to assign political connections.

Data Science

Questions

• predictions – well predicted but well sealed, don’t let predictions alter response.