{"title":"通过文本、视觉和社交媒体功能预测在线旅行社Instagram帖子的用户参与度:来自机器学习的证据","authors":"Hyunsang Son, Young Eun Park","doi":"10.1080/13683500.2023.2278087","DOIUrl":null,"url":null,"abstract":"ABSTRACTBy 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.KEYWORDS: Transfer learningMachine learningInstagramOnline travel shoppingOnline travel agency (OTA) Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":51354,"journal":{"name":"Current Issues in Tourism","volume":"25 18","pages":"0"},"PeriodicalIF":5.7000,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting user engagement with textual, visual, and social media features for online travel agencies' Instagram post: evidence from machine learning\",\"authors\":\"Hyunsang Son, Young Eun Park\",\"doi\":\"10.1080/13683500.2023.2278087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTBy 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.KEYWORDS: Transfer learningMachine learningInstagramOnline travel shoppingOnline travel agency (OTA) Disclosure statementNo potential conflict of interest was reported by the author(s).\",\"PeriodicalId\":51354,\"journal\":{\"name\":\"Current Issues in Tourism\",\"volume\":\"25 18\",\"pages\":\"0\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2023-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Issues in Tourism\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/13683500.2023.2278087\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HOSPITALITY, LEISURE, SPORT & TOURISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Issues in Tourism","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/13683500.2023.2278087","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
Predicting user engagement with textual, visual, and social media features for online travel agencies' Instagram post: evidence from machine learning
ABSTRACTBy 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.KEYWORDS: Transfer learningMachine learningInstagramOnline travel shoppingOnline travel agency (OTA) Disclosure statementNo potential conflict of interest was reported by the author(s).
期刊介绍:
Journal metrics are valuable for readers and authors in selecting a publication venue. However, it's crucial to understand that relying on any single metric provides only a partial perspective on a journal's quality and impact. Recognizing the limitations of each metric is essential, and they should never be considered in isolation. Instead, metrics should complement qualitative reviews, serving as a supportive tool rather than a replacement. This approach ensures a more comprehensive evaluation of a journal's overall quality and significance, as exemplified in Current Issues in Tourism.