Predicting user engagement with textual, visual, and social media features for online travel agencies' Instagram post
Evidence from machine learning
Bibliographic Data
| ID | 21699985 |
|---|---|
| Authors | Hyunsang Son (0000-0003-2292-1209, University of New Mexico), Young Eun Park (0009-0008-5655-6370, Sookmyung Women's University, corresponding author) |
| Year | 2024 |
| Volume | 27 |
| Issue | 22 |
| Pages | 3608-3622 |
| Publication date | 2024-11-16 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Current Issues in Tourism (JOURNAL) |
| Journal identifiers | ISSN: 1368-3500 • E-ISSN: 1747-7603 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13683500.2023.2278087 |
| OpenAlex | W4388460802 |
| Language | EN |
| Citations received | 3 |
| References cited | 56 |
By utilizing supervised, unsupervised, and transfer learning techniques, the present article analyzes the entire three major online travel agencies’ Instagram posts (n = 6,083) to investigate which features contribute more to predicting the user engagement. Among 109 textual, visual, and social media post specific features that we initially extracted, we find the important features using the XGBoost algorithm and estimate the effects of each feature on user engagement (i.e. number of likes) using Negative Binomial regression. The results indicate that OTAs should emphasize the travel related emotion, luxurious, outdoorsy, and celebration in the post wordings in captions but should avoid the big words (words with more than six letters). In terms of images, it is recommended to use the image with fewer lines, fewer parallel lines, but more corners. For an Instagram message-delivering strategy, uploading a post during the evening is recommended
Advertising · Business · Linguistics · Machine learning · Natural language processing · Social media · Upload · User engagement · World Wide Web · Computer Science · Digital Marketing and Social Media · Diverse Aspects of Tourism Research · Sentiment Analysis and Opinion Mining · Artificial Intelligence
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| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |