Hyunsang Son
Biographic Data
| ID | 6306402 |
|---|---|
| NAME | Hyunsang Son |
| GIVEN NAMES | Hyunsang |
| FAMILY NAME | Son |
| SIGNATURE | SON H |
| AFFILIATIONS | University of New Mexico |
| ORCID | 0000-0003-2292-1209 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 3 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
A deep understanding of influencer marketing in the tourism industry: A structural analysis of unstructured text
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 …
Understanding travel influencers’ video on instagram: A transfer learning approach
Predicting user engagement with textual, visual, and social media features for online travel agencies' Instagram post: Evidence from machine learning
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 feat…
U.S. Political Partisanship and Covid-19: Risk Information Seeking and Prevention Behaviors
The global pandemic caused by SARS-CoV-2 (COVID-19) poses serious health risks to humans; yet, despite recommendations by governments and health organizations, a significant number of Americans are not engaging in preventive behaviors. To understand and explain this phenomenon, we seek guidance from a theoretical model that merges the risk information seeking and processing model and the theory of planned behavior. Furthermore, given the politici…
U.S. Political Partisanship and Covid-19: Risk Information Seeking and Prevention Behaviors
The global pandemic caused by SARS-CoV-2 (COVID-19) poses serious health risks to humans; yet, despite recommendations by governments and health organizations, a significant number of Americans are not engaging in preventive behaviors. To understand and explain this phenomenon, we seek guidance from a theoretical model that merges the risk information seeking and processing model and the theory of planned behavior. Furthermore, given the politici…
Predicting user engagement with textual, visual, and social media features for online travel agencies' Instagram post: Evidence from machine learning
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 feat…
A deep understanding of influencer marketing in the tourism industry: A structural analysis of unstructured text
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 …
Understanding travel influencers’ video on instagram: A transfer learning approach
Advertising (3 works) · Business (3 works) · Digital Marketing and Social Media (3 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Influencer marketing (2 works) · Marketing (2 works) · Marketing management (2 works) · Political science (2 works) · Sentiment Analysis and Opinion Mining (2 works)