Sometimes when people go to one of the Blue America pages to contribute to a candidate, they also leave some money for the Blue America PAC. That money usually goes for advertising. Over the years, we’ve tried TV, radio, billboards (stationary and mobile), robocalls and, most recently, social media. Right nowhere running ads on Facebook and Instagram for Angelica Dueñas in California (above), Matt Cartwright and Summer Lee in Pennsylvania (all with musical beds by The Units). Here’s an oldie from 2018, with music by Matthew Grimm:
This cycle we’ve been very serious about our social media ads and we work with an L.A.-based firm, Mycorrhiza Digital and we’ve been learning more and more about targeting. This morning, NY Times technology reporter Natasha Singer looked at how voter targeting works. She notes that there is now “a vast voter data-mining ecosystem in the United States involving dozens of political consulting, analytics, media, marketing and advertising software companies. In the run-up to the midterms next month, campaigns are tapping a host of different scores and using them to create castes of their most desirable voters. There are ‘gun owner,’ ‘pro-choice’ and ‘Trump 2024’ scores, which cover everyday politics. There are also voter rankings on hot-button issues— a ‘racial resentment’ score, for example, and a ‘trans athletes should not participate’ score. There’s even a ‘U.F.O.s distrust government’ score. Campaign and media consultants say such political-issue scores make it easier for candidates to surgically target messages to, and mobilize, the most receptive voters.”
Blue America scrupulously avoids the most invasive aspects of these techniques and avoids anything that smacks of commercial profiling. Spencer Slovic, the founder of Mycorrhiza Digit and the creator of this cycle's Blue America ads, told me this afternoon that "Voter scoring, and other consumer predictive metrics like Facebook’s own targeting algorithms, can be incredibly powerful tools for narrowing the potential audience of a campaign’s ads—saving them money and potentially preventing ad-backfire by energizing the opponent’s base. For example, if every Democrat was bombarded by Trump ads in 2024, that would probably hurt the Trump campaign (by increasing Democrat turnout) more than if Dems never saw a Trump ad and didn’t realize the full extent of the threat he posed. It’s a deepening of the social decay targeting algorithms have already had on society over the past ten years, segmenting us into boxes and preventing civic engagement across groups. Using them for an election win might be a short-term battle victory, but in this ongoing war, all of us are paying the price."
Consumer are subject to a host of predictive scoring systems— hidden rankings based on factors like their demographic profile, socioeconomic status, online activities and offline interests.
Retailers and other services often use “customer lifetime value” scores to try to predict how much money individual clients might spend over time. Universities use “retention” scores to identify students at risk of dropping out.
Voter scores work similarly. They are intended to predict the likelihood that an individual agrees or disagrees with a particular party or political stance, like a belief in gun control. They are also used to predict a person’s likelihood of voting.
Ad tech firms often use the scores to help political campaigns narrowly target audiences on streaming video services, podcasts, websites and apps. Candidates, political party committees and advocacy groups also use the scores to help create lists of specific voters to call, text or canvas in person.
But researchers and privacy experts say that the scores are speculative and invasive, and that they could cause harm if they leaked to hackers or employers.
The process can involve classifying more than 150 million voters— using ratings like “gay marriage” scores or “non-Christian” scores— on personal beliefs they might have assumed were private. The scoring systems can also enable campaigns to quietly aim different, and perhaps contradictory, messages at different voters with little public accountability or oversight.
…To calculate the scores, voter-profiling firms typically use commercially available dossiers thick with data on the election participation, demographics and consumer habits of millions of adults in the United States.
The files contain public information, obtained from state voter registration databases, like a person’s name, date of birth and address, as well as the election years in which the person has voted. They may also include a phone number, political party registration and race or ethnicity.
The voters profiles are often enhanced with commercially available details on consumers like: net worth, education level, occupation, home value, number of children in one’s household, gun ownership, pet ownership, political donations and hobbies or habits such as cooking, woodworking, gambling or smoking. Such details can be purchased from data aggregators that acquire information from customers’ loyalty-card records and other sources.
Next, profiling firms survey a representative sample of voters, scoring respondents according to their stances on issues like marijuana legalization. Firms then use machine learning to identity common characteristics across the dossiers— like low-income households, say, or a preference for low-fat foods— that correlate with voters’ stances.
The characteristics enable profiling firms to find “look-alike” voters in their files. Then they often calculate scores on issues like climate change for all the voters in their files.
…[W]hen Cambridge Analytica— a voter-profiling firm that covertly data-mined and scored millions of Facebook users— became front-page news, many national political campaigns were already using voter scores. Now, even local candidates use them.
This spring, the Government Accountability Office issued a report warning that the practice of consumer scoring lacked transparency and could cause harm. Although the report did not specifically examine voter scores, it urged Congress to consider enacting consumer protections around scoring.