Quantifying the potential persuasive returns to political microtargeting

Abstract

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.

The distorting effects of producer strategies: Why engagement does not reveal consumer preferences for misinformation

“… 7 and 11 , we would not expect the same pattern to emerge. Of course, our findings do not rule out the possibility that novelty may drive engagement with misinformation ( 27 ), but our work shows that high levels of misinformation engagement can arise even in the absence of intrinsic novelty appeal or motivated reasoning, and especially when transmitters behave in a highly responsive manner by microtargeting their content ( 36 ). Our results also help to explain why misinformation sites publish so much content that is actually quite plausible ( SI Appendix , Fig.…”

The potential of generative AI for personalized persuasion at scale

“…On the one hand, such personalization offers tremendous opportunities to promote desired behaviors, including a healthy lifestyle 6 – 8 , financial saving 9 , or support for environmentalism 10 . On the other hand, it can have a pernicious effect on societies 11 , for example, increasing the spread of disinformation 12 , manipulating political preferences 13 , 14 , or promoting maladaptive consumer decision-making 15 , 16 . We provide the first empirical evidence demonstrating how content generated by artificial intelligence (AI) can scale personalized persuasion by automating the creation of such messages with only limited information about the message recipient.…”

Psychological inoculation strategies to fight climate disinformation across 12 countries

“…A tendency for intuitive thinking increased the protection granted by the trust inoculation—that is, an inoculation acting on the socio-affective pathway—while a tendency for deliberative thinking increased the protection given by all three psychological inoculation strategies acting on the cognitive pathway (scientific consensus, transparent communication and accuracy inoculations). These results echo calls to systematically investigate the treatment heterogeneity of behavioural interventions across multiple moderators of interest 168 , 174 , 175 . They furthermore suggest that the theoretical framework presented here can be used not only to identify coherent sets of psychological inoculation strategies but also to identify moderators explaining treatment heterogeneity due to a tendency for specific thinking styles.…”