Misinformation featured in the media often involves a story format. While research has examined how the presence of emotions is an indicator used to distinguish between real and fake news, there has been less attention to emotional dynamics (i.e., period-to-period changes). Further, stories contain an emotional arc, or the rise and fall in sentiment throughout a story which occurs based on the plot. The current work answers the following questions: Do real and fake news differ in terms of their average sentiment volatility and/or their story shape? Further, do differences in sentiment volatility and story shape predict an article’s likelihood of being shared? We address these questions by analyzing health-related and political datasets of posts and shares of news of Twitter.