April 22-24, 2021
Promoting Equity in Health Communication Research
Promoting Equity in Health Communication Research
Abstract: Emotions and Perceived Relative Risk Mediate the Effects of E-cigarette Misinformation on Intentions to Purchase E-cigarettes
Introduction: There is a gap in knowledge on the affective and cognitive mechanisms underlying effects of exposure to health (mis)information on social media. Informed by Lerner and Keltner’s Appraisal Tendency Framework, this study aimed to understand how discrete emotional responses and perceived relative harm of e-cigarette use compared with smoking regular cigarettes mediate the effect of (mis)information tweets about the harms of e-cigarettes on Twitter on the intention to purchase e-cigarettes. The Appraisal Tendency Framework provides a nuanced explanation of discrete emotions in shaping perceptions and behavioral outcomes, with each emotional distinctively leading to differential risk perceptions and behavioral consequences. We hypothesized that exposure to (mis)information about e-cigarettes will have an indirect effect on intention to purchase e-cigarettes through perceived relative harm of e-cigarettes (H1), discrete emotions (H2), and through emotions and perceived relative harm in serial (H3).
Methods: We conducted a web-based experiment among 2400 adult smokers in the US and UK aged 18 and older who reported current cigarette smoking but not current vaping in the past 30 days enrolled from an online survey panel. Participants were randomly assigned to view four tweets within each of the conditions: 1) E-cigarettes are just as or more harmful than smoking, 2) E-cigarettes are completely harmless, 3) Evidence of e-cigarette harms are uncertain, or 4) Control (physical activity). They completed baseline and post-test measures of intention to purchase e-cigarettes and perceived relative harm of e-cigarettes versus smoking. After viewing all four tweets, they reported whether the messages elicited specific emotions (Scared/Hopeful/Worried/Happy/Angry/Relieved). We fitted mediation models using structural equation modeling and bootstrap procedures to assess the indirect effects of exposure to tweets through perceived relative harm of e-cigarettes, six discrete emotions (scared, worried, angry, hopeful, happy, and relieved), and through each emotion and perceived relative harm in serial. We obtained the indirect effect coefficients and bias-corrected confidence intervals to assessed whether each indirect effect was statistically significant (i.e., the confidence interval did not include zero).
Results: The results partially support hypotheses that exposure to (mis)information tweets about harms of e-cigarettes influence intention to purchase e-cigarettes compared with control tweets through perceived relative harm (2 of 3 indirect effects were significant) (H1), discrete emotional responses (8 of 18 indirect effects were significant) (H2), and serially through emotional responses and perceived relative harm (9 of 18 indirect effects were significant) (H3). Specifically, participants who viewed tweets that e-cigarettes were just as or more harmful than smoking had lower intention to purchase e-cigarettes versus the control condition through perceived relative harm; being worried, hopeful, and happy; and serially through being sad, angry, and hopeful and perceived relative harm.
Conclusions: Discrete emotional responses and perceived relative harm mediate the effects of brief exposure to health (mis)information of e-cigarette harms on Twitter on adult smokers’ intention to purchase e-cigarettes in multiple and complex ways. These findings contribute to theorizing the mechanisms of how health (mis)information influence behavioral intention and future interventions to address the effects of exposure to health (mis)information on social media.
Methods: We conducted a web-based experiment among 2400 adult smokers in the US and UK aged 18 and older who reported current cigarette smoking but not current vaping in the past 30 days enrolled from an online survey panel. Participants were randomly assigned to view four tweets within each of the conditions: 1) E-cigarettes are just as or more harmful than smoking, 2) E-cigarettes are completely harmless, 3) Evidence of e-cigarette harms are uncertain, or 4) Control (physical activity). They completed baseline and post-test measures of intention to purchase e-cigarettes and perceived relative harm of e-cigarettes versus smoking. After viewing all four tweets, they reported whether the messages elicited specific emotions (Scared/Hopeful/Worried/Happy/Angry/Relieved). We fitted mediation models using structural equation modeling and bootstrap procedures to assess the indirect effects of exposure to tweets through perceived relative harm of e-cigarettes, six discrete emotions (scared, worried, angry, hopeful, happy, and relieved), and through each emotion and perceived relative harm in serial. We obtained the indirect effect coefficients and bias-corrected confidence intervals to assessed whether each indirect effect was statistically significant (i.e., the confidence interval did not include zero).
Results: The results partially support hypotheses that exposure to (mis)information tweets about harms of e-cigarettes influence intention to purchase e-cigarettes compared with control tweets through perceived relative harm (2 of 3 indirect effects were significant) (H1), discrete emotional responses (8 of 18 indirect effects were significant) (H2), and serially through emotional responses and perceived relative harm (9 of 18 indirect effects were significant) (H3). Specifically, participants who viewed tweets that e-cigarettes were just as or more harmful than smoking had lower intention to purchase e-cigarettes versus the control condition through perceived relative harm; being worried, hopeful, and happy; and serially through being sad, angry, and hopeful and perceived relative harm.
Conclusions: Discrete emotional responses and perceived relative harm mediate the effects of brief exposure to health (mis)information of e-cigarette harms on Twitter on adult smokers’ intention to purchase e-cigarettes in multiple and complex ways. These findings contribute to theorizing the mechanisms of how health (mis)information influence behavioral intention and future interventions to address the effects of exposure to health (mis)information on social media.