How Sentiment Divergence in Influencers’ Multimodal Social Media Posts Shapes Follower Engagement?

The growing prominence of social media (SM) influencers as key content creators on SM platforms highlights the need for a nuanced understanding of factors that drive follower engagement. Taking a sentiment-theoretic perspective, we examine the interplay between image and text sentiment in multimodal SM posts to show that alignment with followers’ sentiment consistency expectations is crucial for enhanced follower engagement with influencers’ SM posts. Drawing on expectation disconfirmation and negativity bias theories, we explore the impact of sentiment divergence in multimodal SM posts and the moderating role of the follower community’s duality tolerance on follower engagement. Analyzing a dataset of 24,000 Instagram posts, our findings suggest that sentiment divergence between the image and text within the same SM post can reduce follower engagement. Specifically, when the text sentiment is less positive than the image sentiment (negative divergence), the detrimental impact on engagement is notably more pronounced, while posts where the text sentiment is more positive than the image sentiment (positive divergence) appear to be less affected. Additionally, we find that higher duality tolerance within an influencer’s community mitigates the negative effects of sentiment divergence on follower engagement. Our research contributes to the SM literature by highlighting the importance of sentiment divergence for SM influencers and their SM community’s duality tolerance orientation when they are designing posts. We further provide practical insights on how to craft multimodal SM posts that are effective in enhancing follower engagement.