This study aims to examine variations in engagement with influencer posts about tourism destinations on Instagram through the lens of the strength-of-weak-ties theory. It specifically analyzes the effects of influencer type (macro, micro or nano), emotional ties to the destination (local versus travel influencers) and content sentiment (positive versus negative) on likes, comments and overall engagement rates.
Web scraping and artificial intelligence techniques were employed to collect and analyze 34,175 Instagram posts from 11,357 influencers related to a specific tourism destination. Influencers were categorized by follower count and connection to the destination, while content sentiment was assessed using machine learning and deep learning techniques. Differences in engagement, likes and comments rates were examined using a univariate generalized linear model.
The results indicate that influencers with fewer followers (nano-influencers) achieve a higher engagement rate. This effect is more pronounced when posts convey a negative sentiment, especially among travel influencers compared to local influencers. While this trend is observed for the like rate too, these interaction effects are not significant for the comments rate.
This study contributes to the literature by employing AI-based analysis and statistical modeling to examine Instagram engagement using data collected via Web scraping. The findings provide actionable insights for destination marketing organizations to enable them to refine their strategies, enhance their promotional efforts and increase destination visibility by identifying influencers with higher engagement through data-driven selection and targeted content curation.
