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Partisan self-interest is an important driver for people's support for the regulation of targeted political advertising.

ABSTRACT: The rapid emergence of online targeted political advertising has raised concerns over data privacy and what the government's response should be. This paper tested and confirmed the hypothesis that public attitudes toward stricter regulation of online targeted political advertising are partially motivated by partisan self-interest. We conducted an experiment using an online survey of 1549 Americans who identify as either Democrats or Republicans. Our findings show that Democrats and Republicans believe that online targeted political advertising benefits the opposing party. This belief is based on their conviction that their political opponents are more likely to be mobilized by online targeted political advertising than are supporters of their own party. We exogenously manipulated partisan self-interest considerations of a random subset of participants by truthfully informing them that, in the past, online targeted political advertising has benefited Republicans. Our findings show that Republicans informed about this had less favorable attitudes toward regulation than did their uninformed co-partisans. This suggests that Republicans' attitudes regarding stricter regulation are based not solely on concerns about privacy violations, but also, in part, are caused by beliefs about partisan advantage. The results imply that people are willing to accept violations of their privacy if their preferred party benefits from the use of online targeted political advertising.

SUBMITTER: Baum K

PROVIDER: S-EPMC8115848 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo identify and assess the preferences of people with long-term health conditions toward generalizable characteristics of self-management support interventions, with the objective to inform the design of more person-centered support services.Data sourcesPrimary qualitative and quantitative data collected on a representative sample of individuals with at least one of the fifteen most prevalent long-term conditions in the UK.Study designTargeted literature review followed by a series of one-to-one qualitative semistructured interviews and a large-scale discrete choice experiment.Data collectionDigital recording of one-to-one qualitative interviews, one-to-one cognitive interviews, and a series of online quantitative surveys, including two best-worst scaling and one discrete choice experiment, with individuals with long-term conditions.Principal findingsOn average, patients preferred a self-management support intervention that (a) discusses the options available to the patient and make her choose, (b) is individual-based, (c) face to face (d) with doctor or nurse, (e) at the GP practice, (f) sessions shorter than 1 hour, and (g) occurring annually for two-third of the sample and monthly for the rest. We found heterogeneity in preferences via three latent classes, with class sizes of 41% (C1), 30% (C2), and 29% (C3). The individuals' gender [P < 0.05(C1), P < 0.01(C3)], age [P < 0.05(C1), P < 0.05(C2)], type of long-term condition [P < 0.05(C1), P < 0.01(C3)], and presence of comorbidity [P < 0.01(C1), P < 0.01(C3), P < 0.01(C3)] were able to characterize differences between these latent classes and help understand the heterogeneity of preferences toward the above mentioned features of self-management support interventions. These findings were then used to profile individuals into different preference groups, for each of whom the most desirable form of self-management support, one that was more likely to be adopted by the recipient, could be designed.ConclusionsWe identified several factors that could be used to inform a more nuanced self-management support service design and provision that take into account the recipient's characteristics and preferences.

Project description:The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party's narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from the politicians in the U.S., including the ex-president, members of Congress, and state governors, we found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.Supplementary informationThe online version contains supplementary material available at 10.1140/epjds/s13688-021-00308-4.

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Partisan self-interest is an important driver for people's support for the regulation of targeted political advertising.

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