A deep understanding of influencer marketing in the tourism industry: a structural analysis of unstructured text

ABSTRACT

Using both a word frequency approach and a cutting-edge transfer learning technique for natural language processing with BERTopic, the present study analysed the entire texts from the top 40 travel influencers’ Instagram posts (n = 23,223). Among the 256 features that we initially extracted, we ranked the top 19 features using the machine learning algorithm XGBoost and estimated the effects of these features on consumer engagement using Negative Binomial regression. The results show that seasonal trips, travel destination recommendations, recommendations for fashion during the trip, and emphasising travel-related emotion generate a higher level of engagement. For message strategy, specifically focusing on linguistic features, it is recommended that influencers use analytic, authentic, want-related, and space-related words in the caption but should avoid using too many hashtags. Also, overall, influencers should avoid sending messages during the night, with messages that are too long or with too many emojis.

KEYWORDS:

• Influencer
• Machine learning
• Transfer learning
• Social media
• BERTopic
• Topic modeling
• Destination marketing

Disclosure statement

No potential conflict of interest was reported by the author(s).