This study provides a cross-platform, longitudinal investigation of pictures depicting political candidates posted to Facebook and Instagram over a 15-month period during the 2020 US election (n = 4,977). After motivating an exploratory research design, we set out to expound: the extent of cross-platform image posting across Facebook and Instagram; the emotion expression of politicians across the ... [Show full abstract] two platforms; and the relationship between these emotions and post performance. Our analysis of eight political campaigns (seven Democratic challengers and the Republican incumbent) finds relatively high and stable levels of cross-posting candidate pictures across the two platforms. The exception is the incumbent campaign, where cross-posting activity rose in proximity to the primary elections. Regarding emotions, we utilize both computer vision and crowd coding to identify happiness as the dominant emotion on Facebook and Instagram. Overall, we detect little variation in candidate emotion expressions – across campaigns and across platforms. However, we do find differences in how platform audiences respond to emotions, proxied here through post performance. Results from binomial logistic regressions show that in comparison with Calm, posts exhibiting Anger are less likely to overperform on both Facebook and Instagram. Most interestingly, we find diverging patterns for Happiness, which performs better than Calm on Instagram but not on Facebook. We interpret these findings to suggest first, that Instagram users reward emotionality from politicians. Second and more importantly, we argue that differing audience responses to emotions – captured through social media metrics – may reveal a generation polarization in what different segments of the electorate prefer their political leaders to be.