Exploring and contextualizing the associations among political identification, vaccine risk and benefit perceptions, and vaccine acceptance

There is considerable research on the influence of political partisanship on vaccine acceptance. However, the current study is one of the first to investigate how political identification is related to risk and benefit perceptions vis-à-vis vaccines. Based on survey data collected in the United States regarding four different vaccines at three points in time (n = 383 for April-June 2012; n = 364 for March 2020; n = 334 for April-May 2020), Democrats were found to have more positive attitudes toward vaccinations in general and hold more favorable risk and benefit perceptions of pandemic and routine vaccines overall compared to Republicans. However, the onset of a pandemic can serve as an equalizing force and motivate similar risk and benefit perceptions across party lines, but this force can wane quickly when the information environment is politicized. For political partisans who are ideologically oriented to be more hesitant toward vaccines, two conditions can help sustain their favorable vaccine perceptions during a pandemic: (a) when they identify with the governing political party, and (b) when there are consistent reassurances of vaccine safety and effectiveness from their partisan leadership. Given that risk and benefit perceptions were found to be consistently associated with vaccine acceptance across contexts in this study, communication aimed at encouraging vaccinations should always address the risks and benefits of a vaccine in ways that are tailored for individuals with different political affiliations.

Online Moral Disengagement: An Examination of the Relationships Between Electronic Communication, Cognitive Empathy, and Antisocial Behavior on the Internet

A consequence of the proliferation of online communication is the concerning presence of antisocial behavior observed in virtual spaces. Research suggests the cognitive component of empathy is hindered by features of electronic communication which facilitates antisocial behaviors online. Investigations into how features of online communication inhibit cognitive empathy are lacking, and findings on moral disengagement and antisocial behavior have yet to be integrated with studies on cognitive empathy and electronic communication. The current study explores these relationships. One hundred and three undergraduate students completed several measures including the Online Moral Disengagement Scale, Questionnaire of Cognitive and Affective Empathy, and Online Prosocial and Antisocial Behavior Scale. Results showed a positive correlation between compulsive internet use and online moral disengagement, as well as a negative correlation between cognitive empathy and moral disengagement online. It was hypothesized that online moral disengagement would mediate the relation between cognitive empathy and antisocial behavior online but this mediation was not supported. However, a moderated relationship was revealed between cognitive empathy and moral justification, by liberalism. This moderation can be explored further and built upon by future research to deepen our understanding of how political ideology relates to virtual behavior. Furthermore, the findings concerning components of empathy and moral disengagement, and their role within the perpetration of antisocial conduct online, can inform future research as well as interventions focused on fostering prosocial behavior online and curbing cyberaggression.

Using "Big Data" Versus Alternative Measures of Aggregate Data to Predict the U.S. 2016 Presidential Election

To what extent could "Big Data" predict the results of the 2016 U.S. presidential election better than more conventional sources of aggregate measures? To test this idea, the present research used Google search trends versus other forms of state-level data (i.e., both behavioral measures like the incidence of hate crimes, hate groups, and police brutality and implicit measures like Implicit Association Test (IAT) data) to predict each state's popular vote for the 2016 presidential election. Results demonstrate that, when taken in isolation, zero-order correlations reveal that prevalence of hate groups, prevalence of hate crimes, Google searches for racially charged terms (i.e., related to White supremacy groups, racial slurs, and the Nazi movement), and political conservatism were all significant predictors of popular support for Trump. However, subsequent hierarchical regression analyses show that when these predictors are considered simultaneously, only Google search data for historical White supremacy terms (e.g., "Adolf Hitler") uniquely predicted election outcomes earlier and beyond political conservatism. Thus, Big Data, in the form of Google search, emerged as a more potent predictor of political behavior than other aggregate measures, including implicit attitudes and behavioral measures of racial bias. Implications for the role of racial bias in the 2016 presidential election in particular and the utility of Google search data more generally are discussed.