Across America, Thanksgiving dinners have turned into sparring matches, and petty conflict often overshadows substantive policy discussion. Political polarization costs all of us: it poisons social relations, increases legislative gridlock, and drives elected officials to prioritize winning over representing their constituents’ interests.
In his op-ed “How Big Data Broke American Politics,” NBC’s Chief Political Analyst Chuck Todd blames this phenomenon on Big Data, the data revolution aided by the Help America Vote Act of 2002. The act, which required states to create digital statewide voter registration lists, resulted in political campaigns utilizing individual voter data, enriched with commercial data, to make predictions about voter’s political affiliation and priority issues.
Todd argues that political strategists used to believe that the best way to win an election was to persuade those in the ideological middle to vote for their candidate. However, “micro-targeting”—delivering personal messaging to individuals based on their voter or demographic information—has changed that strategy.
Big data now allows strategists to easily identify like-minded citizens, enabling them to “make sure five supporters commit to go to the polls” with the same amount of time and effort as it would take to “change the mind of one skeptic.” Thus he concludes that campaigns have stopped trying to pursue the ideological middle.
Eitan Hersh, in Hacking the Electorate: How Campaigns Perceive Voters, agrees, claiming that Big Data causes campaigns to simplify complex voters into “polarized version[s] in which voters are divided into those who are valuable to the campaign and those who are not.” Nevertheless, I argue the opposite: that rather than turning voters one-dimensional, Big Data grants candidates the possibility to shape political messaging in a way that can increase political engagement, as well as the opportunity to court the ideological middle in increasingly effective ways.
Todd argues that Big Data is primarily responsible for perpetuating American political polarization. Although America is undeniably polarized, its causes are so much bigger than just Big Data. To his credit, Todd does partially concede that the source of polarization is complicated, writing, “some might try and make a chicken and egg argument at this point, blaming data and technological advances merely for accelerating an existing trend of polarization.” Yet, he later minimizes his own point, claiming that Big Data makes it impossible to honor Americans’ desire to end polarization. It seems undeniable to me that, independent of Big Data, polarization has been generated and intensified by factors such as political parties becoming more aligned by shared demographics and social media.
As Liliana Mason, SNF Agora Institute Associate Professor of Political Science at the University of Maryland, writes in her book Uncivil Agreement: How Politics Became Our Identity, in recent years, parties have become more socially similar. This is detrimental because, in the past, “cross-cutting social ties,” that is, “attitudes or identities that are not commonly found in the partisan’s party” such as race or religion, have allowed for more partisan compromise by increasing party ambivalence.
As parties have homogenized, partisanship has come to represent a “mega-identity,” which encompasses one’s “religion, race, ethnicity, [and] neighborhood” in addition to their political party. Conflicts are exacerbated as people are fighting for their entire identity rather than just one aspect of it. These social party shifts have been gradually occurring for decades, following the American Political Science Association putting out a report in 1950 calling for the parties to become more distinct to help people make responsible, informed decisions—clearly preceding Big Data.
Additionally, social media pushes hyperpartisan content to users, limiting their exposure to alternative viewpoints and inspiring extremism within partisan bubbles. For instance, in an experiment where participants abstained from using Facebook for a month, their time away from it “significantly reduced polarization of views on policy issues.”
Also, critically, Pew Research finds that the most politically polarized Americans are those who are most actively involved in politics. These already passionate voters make their ideological impact on the country without being pushed to do so by being microtargeted by Big Data. Evidently, there has not been an academic consensus on why American polarization has intensified, so Big Data should not exclusively take the blame.
As Seth Masket, a professor of political science at the University of Denver, writes, “Big Data is just a tool for dealing with the political world in which we already live. It didn’t create that world.” With the understanding that political polarization would still exist regardless, albeit perhaps in a slightly less extreme form, it is essential to analyze Big Data’s merits.
By providing campaign strategists with detailed information about citizens from their voting history, to their partisan affiliation (in thirty-one states), to even their magazine subscriptions and the value of their homes, enhanced voter files allow campaign strategists to conceptualize voters in a much more comprehensive manner.
Traditionally, voters have been judged based upon their precincts, defined voting districts for which vote counts are available. Rather than dismissing an entire county because it is known to vote for a specific party, Big Data allows campaign strategists to judge its members on an individual level and find those who may be more susceptible to their candidate’s views. This micro-targeting often occurs by individualizing advertisements based on topics of interest.
For example, Todd describes how, before Big Data, TV advertisements from both candidates often concerned the same policy matters. For example, during the 2000 Missouri Senate election, Mel Carnahan and John Ashcroft both televised ads discussing healthcare, leading Todd to conclude that both campaigns felt that “in order to win, they had to debate each other over the same issue.”
He contrasts this advertisement with two from the 2014 North Carolina Senate election between Kay Hagan and Thom Tillis, where Hagan’s advertisement focuses on education and Tillis’s upon ISIS. However, this characterization simplifies the situation. Hagan and Tillis did have multiple opportunities to have their viewpoints interact throughout their campaigns. Not only did they participate in two televised debates, but Hagan also aired an advertisement contesting Tillis’s on ISIS, stating that “speaker Tillis should be ashamed for running an attack advertisement that says I would let our soldiers die in vain,” and explaining her viewpoints on ISIS and her family’s involvement with the military.
Regardless, the tendency, enhanced by Big Data, to feed people advertisements concerning issues they care about seems to be beneficial in piquing people’s interest in politics. For example, if I care deeply about education but less so about issues like war or climate policy, I might remain apolitical. Yet, if I am fed Hagan’s advertisement via microtargeting, I would likely be inspired to learn more about the race and her other policies. Presenting issues that people are most passionate about is a great way to encourage them to become more engaged in politics overall.
Hersh writes that some consider it “good for democracy when campaigns use these tools because the data helps campaigns connect with voters in more meaningful ways than through bland television advertisements.” Accordingly, voter turnout has been on a primarily upward trend since the Big Data Revolution began in 2002.
Of course, numerous factors affect turnout, and it has not been directly attributed to the use of Big Data. Regardless, even if Big Data has a minor effect, to return to Todd’s initial example, it is not such a horrible thing to use microtargeting to “make sure five core supporters commit to go to the polls” if that means that more people are exercising their voice.
Furthermore, microtargeting can and has been used as a tool to court the ideological middle. Todd paints the picture that Big Data has wiped out the center of the electorate because they have determined that “they don’t need centrist or swing voters to win.” Yet, in the 2024 Presidential election, despite Big Data being arguably the most advanced that it has ever been, Harris wholeheartedly tried to appeal to moderates—and utilized microtargeting to do so.
Masket explains, “When Democratic campaigns use sophisticated consumer profiles and polling methods to try to identify the suburban women who might vote Republican but don’t want to see Roe v. Wade overturned, that’s microtargeting…What’s more, it’s persuading the folks in the middle — precisely the thing Todd and Dann claim is no longer happening.”
Harris used this ability to understand moderate priorities and was able to more broadly appeal to voters, which was exactly what Todd claims politicians no longer have any interest in doing. She touted broadly appealing policies, such as creating a home care benefit within Medicare that would help families pay for caring for their senior relatives at home instead of having to put them in expensive nursing facilities. Additionally, she promised to appoint a Republican to her cabinet if elected president to further promote moderate interests. Ultimately, her efforts to understand her potential supporters paid off. Despite losing the election, her strategy was effective, demonstrated by her win among independents and moderates (albeit by a smaller margin than Biden). While Todd is rightfully concerned about the polarized state of this country, he incorrectly identifies Big Data as the sole culprit. Polarization is fueled by multiple factors, including social media, the extremism of the most passionate partisans, and our increasingly socially similar political parties. Rather than simplifying voters, Big Data offers campaigns a multi-faceted understanding of them. This information can then be used to increase political participation by ensuring that citizens are learning about issues that they truly care about. In fact, Big Data may offer us a path out of polarization by granting candidates and campaign strategists the precise information to determine which problems and policies are important to a wider spectrum of Americans, allowing them to start “courting the vacillating middle” once again.