Invoking Identity? Partisan Polarization in Discussions of Race, Racism, and Gender in 2022 Midterm Advertising in the U.S

CONTEXT

Media messaging matters for public opinion and policy, and analyzing patterns of campaign strategy can provide important windows into policy priorities.

METHODS

We used content analysis supplemented with keyword-based text analysis to assess the volume, proportion and distribution of attention to race-related issues in comparison to gender-related issues during the general election period of the 2022 midterm campaigns for federal office.

FINDINGS

Race-related mentions were overwhelmingly focused on crime and law and order with very little attention to racism, racial injustice, and the structural barriers that lead to widespread inequities. In stark contrast to mentions of gender, racial appeals were less identity focused and were competitively contested between the parties in their messaging, but much more likely to be led by Republicans.

CONCLUSIONS

Our results suggest that discussion of race and gender were highly polarized with consequences for public understanding of and belief in disparities and policies important to population health.

Quantifying the potential persuasive returns to political microtargeting

Much concern has been raised about the power of political microtargeting to sway voters' opinions, influence elections, and undermine democracy. Yet little research has directly estimated the persuasive advantage of microtargeting over alternative campaign strategies. Here, we do so using two studies focused on U.S. policy issue advertising. To implement a microtargeting strategy, we combined machine learning with message pretesting to determine which advertisements to show to which individuals to maximize persuasive impact. Using survey experiments, we then compared the performance of this microtargeting strategy against two other messaging strategies. Overall, we estimate that our microtargeting strategy outperformed these strategies by an average of 70% or more in a context where all of the messages aimed to influence the same policy attitude (Study 1). Notably, however, we found no evidence that targeting messages by more than one covariate yielded additional persuasive gains, and the performance advantage of microtargeting was primarily visible for one of the two policy issues under study. Moreover, when microtargeting was used instead to identify which policy attitudes to target with messaging (Study 2), its advantage was more limited. Taken together, these results suggest that the use of microtargeting-combining message pretesting with machine learning-can potentially increase campaigns' persuasive influence and may not require the collection of vast amounts of personal data to uncover complex interactions between audience characteristics and political messaging. However, the extent to which this approach confers a persuasive advantage over alternative strategies likely depends heavily on context.