Key characteristics of the reviewed studies
| Authors | Online publication year | Study objective | Video content type(s) examined | Sample size and source | Research approach | Analysis method(s) | Tools/Software used |
|---|---|---|---|---|---|---|---|
| Antivo et al. | 2024 | Examining online viewers’ non-social emotions in comments on sex tourism vlogs in Southeast Asia | Travel vlogs (metadata) and associated comments (textual) | 27 YouTube videos and 10,864 comments | Qualitative | Phronetic iterative qualitative data analysis | Data Miner |
| Avraham | 2018 | Examining nation branding and marketing strategies used by destinations to combat tourism crises and negative stereotypes | Tourism promotional videos (metadata, visual, textual) | 63 YouTube videos | Mixed methods | Quantitative and qualitative content analysis | Not specified |
| Barnes | 2023 | Investigating the impact of voice and music characteristics in travel and tourism video advertisements on consumer responses | Tourism ads (metadata, auditory) | 291 YouTube videos | Quantitative | Acoustic analytics, regression analysis | Python, R, Google Colab Notebook, Soundgen, Tunebat, Ultimate Vocal Remover |
| Barrett and Feng | 2020 | Analyzing online recipe videos for food safety implications related to flour handling, with a focus on identifying potential risks and safety practices | Recipe videos featuring flour (visual, textual) | 85 blog recipes and 146 YouTube videos | Mixed methods | Qualitative content analysis, descriptive statistics | Microsoft Excel, SPSS |
| Bernal et al. | 2024 | Analyzing tourism resiliency approaches of Philippine local government units during COVID-19 | Tourism promotional videos (visual, textual) | 29 videos | Qualitative | Manifest content analysis | Not specified |
| Chen et al. | 2024 | Investigating how multimodal stimuli in tourism crowdfunding projects predict project success | Videos from travel crowdfunding projects (metadata, visual, textual) | 3,659 videos | Quantitative | Textual analysis, deep learning, predictive modeling | Python (FFmpeg, OpenCV, NLTK, Python Imaging), Google Cloud Video Intelligence API |
| Deng et al. | 2022 | Exploring the content of influencer-endorsed short videos about wine on TikTok (Douyin), with particular attention to gender and generational differences in perceptions and preferences | Comments associated with wine-related short videos (textual) | 10,042 comments on Douyin | Mixed methods | LDA topic modeling, qualitative content analysis, Mann–Whitney U tests | Python (Jieba for LDA topic modeling), SPSS |
| Dewantara et al. | 2023 | Exploring Parasocial Interaction (PSI) attributes in travel vlogs and their influence on viewers’ travel intentions | Travel vlogs (metadata, visual, textual) and associated comments (textual) | 10 YouTube videos and 9,086 comments | Qualitative | Qualitative content analysis, nominal group technique, thematic analysis | NVivo, Microsoft Teams (for transcription) |
| Georgescu Paquin and Cerdan Schwitzguébel | 2021 | Analyzing the tourist landscape representation in Barcelona’s promotional videos in an overtourism context | Tourism promotional videos (visual) | 24 YouTube videos | Mixed methods | Quantitative visual content analysis, qualitative semiotic analysis | Not specified |
| Hoebanx and French | 2023 | Examining how slot machine videos on YouTube portray gambling and align with the norms of YouTube’s platform economy | Slot machine videos (visual, textual) and associated comments (textual) | 21 YouTube videos and 186 comments | Qualitative | Thematic analysis | Not specified |
| Huertas et al. | 2017 | Analyzing how Spanish DMOs use YouTube to communicate their promotional videos and to study whether these videos communicate brands through attraction factors and emotional values | Tourism promotional videos (metadata, visual, textual) | 542 YouTube videos | Mixed methods | Quantitative content analysis, qualitative content analysis, statistical correlation | FanpageKarma |
| Ketter and Avraham | 2021 | Examining digital marketing strategies used by NTBs during the COVID-19 pandemic | Tourism promotional videos (visual, textual) | 29 YouTube videos | Qualitative | Qualitative content analysis | Not specified |
| Lakmali et al. | 2024 | Analyzing how tourists portray crisis-affected destinations through YouTube vlogs and their content creation motivations | Travel vlogs (metadata, textual) | 10 YouTube videos | Qualitative | Thematic analysis | QDA Miner Lite, Excel |
| Lang | 2024 | Analyzing how Hangzhou’s cultural and tourism bureau constructs its international image on YouTube | Tourism promotional videos (visual, textual, auditory) | 83 YouTube videos | Qualitative | Multimodal critical discourse analysis | Not specified |
| Lau et al. | 2024 | Examining how museums reach Generation Z virtual tourists using TikTok videos and the relationship between video elements and types of engagement | Museum promotional videos (metadata, textual, auditory) | 313 videos on TikTok | Mixed methods | Thematic analysis, descriptive analysis, regression analysis | Excel, SPSS |
| Li et al. | 2023 | Exploring cultural meaning construction in social media through analysis of Liziqi’s YouTube channel | Lifestyle vlogs (visual) and associated comments (textual) | 5 YouTube videos and 500 comments | Qualitative | Content analysis (coding), textual analysis (decoding) | Python, NVivo |
| Li et al. | 2024 | Comparing spatial behavior of Chinese and foreign tourists based on landmark recognition in travel vlogs | Travel vlogs (visual, metadata) | 439 YouTube videos, 1,059 videos on Bilibili | Quantitative | Spatial analysis, landmark recognition | Baidu AI APIs, Baidu Maps Coordinate Picking System, Python |
| Ma et al. | 2023 | Examining the effectiveness of bullet comments associated with food vlogs on the tourists’ travel intentions | Food vlogs (metadata, visual, textual, auditory) and associated bullet comments (textual) | 20 videos on Bilibili and 133,680 bullet comments | Mixed methods | Qualitative content analysis, thematic analysis, computational sentiment analysis | Python |
| Ma et al. | 2023 | Exploring how food travel vlogs awaken travel intentions through viewers’ social and non-social emotions | Food vlogs (metadata) and associated comments (textual) | 32 videos on Bilibili and 91,437 comments | Mixed methods | Qualitative content analysis, LDA topic modeling, computational sentiment analysis | Python, (SnowNLP for sentiment analysis) |
| Mesana et al. | 2024 | Mapping online viewers’ social and non-social emotions when watching UNESCO cultural heritage sites’ travel vlogs | Travel vlogs (metadata) and associated comments (textual) | 64 YouTube videos and 3,089 comments | Qualitative | Qualitative sentiment analysis, phronetic iterative data analysis | Data Miner |
| Motahar et al. | 2021 | Exploring how Iran is framed as a travel destination by Social Media Influencers (SMIs) on YouTube | Travel videos (metadata, visual, textual) | 10 YouTube videos | Qualitative | Netnography and narrative analysis | Not specified |
| Nazir | 2023 | Examining destination branding through social media, comparing foreign influencers’ narratives with official presentations | Travel vlogs (visual, textual) | 8 YouTube videos | Qualitative | Thematic analysis | NVivo |
| Rauf and Pasha | 2024 | Understanding Global North–South dynamics in YouTube gastronomic tourism videos and audience feedback | Food vlogs (metadata, textual) and associated comments (textual) | 9 YouTube videos and 128,000 comments | Mixed methods | Textual analysis, qualitative content analysis, computational sentiment analysis | Python, YouTube API |
| Salangsang et al. | 2022 | Examining elements of luxury travel in tourism video advertisements from Asian countries during COVID-19 | Tourism promotional videos (metadata, visual, textual) | 122 YouTube videos | Qualitative | Qualitative content analysis | NVivo, Ncapture |
| Sharma | 2023 | Comparing message strategies adopted by celebrities vs social media influencers in brand-related YouTube content | Fashion and food industry videos (metadata, visual, textual, auditory) | 638 YouTube videos | Mixed methods | Qualitative content analysis, chi-square test, ANOVA, Mann–Whitney U test, Kruskal–Wallis H | SPSS |
| Tavakoli and Ling | 2022 | Exploring consumer perceptions of virtual food consumption and its sociological implications using online comments about a promotional video | Comments associated with a virtual food promotion video (textual) | 250 comments on Facebook | Qualitative | Thematic analysis | Not specified |
| Tham et al. | 2023 | Examining how Penang, Malaysia, is marketed on TikTok by different stakeholders and how they present the destination’s image | Tourism promotional videos (metadata, visual, textual, auditory) | 30 videos on TikTok | Qualitative | Multimodal analysis, Burke’s Pentadic analysis | Not specified |
| Vujičić et al. | 2021 | Examining the techno-social dimensions of tourist drone videography to understand production practices and creator differences | Drone vacation videos (metadata, visual) | 351 YouTube videos | Mixed methods | Qualitative content analysis, descriptive statistics, chi-square tests, regression analysis | Webometric Analyst, YouTube Statistics, SPSS |
| Warton and Brander | 2017 | Assessing the value of the TV show Bondi Rescue for improving tourist beach safety awareness | TV shows (visual, textual) | 98 episodes | Mixed methods | Qualitative content analysis, t-tests, ANOVA | R |
| Wen et al. | 2021 | Exploring travel constraints related to physician-assisted suicide tourism | Documentaries (metadata, textual) and associated comments (textual) | 25 YouTube videos and 1,231 comments | Qualitative | Thematic analysis | Nvivo |
| Yayla et al. | 2024 | Examining food preferences and gastro-tourist typologies of digital nomads | Digital nomads videos (visual, textual) and associated comments (textual) | 21 YouTube videos and 326 comments | Qualitative | Qualitative content analysis | Not specified |
| Yıldırım and Kaya | 2024 | Investigating digital nomads’ impressions and reflections toward intangible cultural heritage (ICH) during travel | Travel videos (visual, textual) and associated comments (textual) | 5 videos on social media and associated comments | Qualitative | Thematic analysis | MAXQDA |
| Yoo, Kim et al. | 2024 | Examining the relationships between discrete emotions expressed by travel influencers and viewer engagement | Travel videos (metadata, textual) | 5,008 YouTube videos | Quantitative | Computational sentiment analysis, regression analysis | Amazon Rekognition API, Receptiviti API (Syntax-Aware Lexical Emotion Engine module) |
| Yoo, Piscarac et al. | 2024 | Investigating the effectiveness of digital outdoor advertising in redefining urban tourism appeal and city branding using Seoul’s “Wave” campaign | Comments associated with YouTube videos (textual) | 956 comments on YouTube | Mixed methods | Word frequency analysis, centrality analysis, network analysis | Ucinet 6, NetDraw |
| Yu | 2019 | Investigating public perceptions of humanlike robots as employees in the hotel industry | Humanlike robot videos (metadata) and associated comments (textual) | 2 YouTube videos and 1,621 comments | Mixed methods | Cluster analysis, thematic analysis | NVivo, Data Miner |
| Zhang | 2021 | Analyzing public perceptions of service robots in hospitality and tourism amid COVID-19 | Comments associated with news report videos (textual) | 1,852 comments on YouTube | Quantitative | Explorative analysis (word cloud), computational sentiment analysis, LDA topic modeling | Python (Gensim for topic modeling), SentiStrength |
| Zhu et al. | 2024 | Examining the impacts of content features of pro-environmental tourism videos on viewers’ in-consumption engagement | Pro-environmental videos (metadata, visual, textual, auditory) | 44 videos on Bilibili | Quantitative | Regression analysis | Microsoft Azure AI Video Indexer, Python (Librosa, FFmpeg), R |
| Authors | Online publication year | Study objective | Video content type(s) examined | Sample size and source | Research approach | Analysis method(s) | Tools/Software used |
|---|---|---|---|---|---|---|---|
| Antivo | Examining online viewers’ non-social emotions in comments on sex tourism vlogs in Southeast Asia | Travel vlogs (metadata) and associated comments (textual) | 27 YouTube videos and 10,864 comments | Qualitative | Phronetic iterative qualitative data analysis | Data Miner | |
| Avraham | 2018 | Examining nation branding and marketing strategies used by destinations to combat tourism crises and negative stereotypes | Tourism promotional videos (metadata, visual, textual) | 63 YouTube videos | Mixed methods | Quantitative and qualitative content analysis | Not specified |
| Barnes | 2023 | Investigating the impact of voice and music characteristics in travel and tourism video advertisements on consumer responses | Tourism ads (metadata, auditory) | 291 YouTube videos | Quantitative | Acoustic analytics, regression analysis | Python, R, Google Colab Notebook, Soundgen, Tunebat, Ultimate Vocal Remover |
| Barrett and Feng | 2020 | Analyzing online recipe videos for food safety implications related to flour handling, with a focus on identifying potential risks and safety practices | Recipe videos featuring flour (visual, textual) | 85 blog recipes and 146 YouTube videos | Mixed methods | Qualitative content analysis, descriptive statistics | Microsoft Excel, |
| Bernal | 2024 | Analyzing tourism resiliency approaches of Philippine local government units during COVID-19 | Tourism promotional videos (visual, textual) | 29 videos | Qualitative | Manifest content analysis | Not specified |
| Chen | 2024 | Investigating how multimodal stimuli in tourism crowdfunding projects predict project success | Videos from travel crowdfunding projects (metadata, visual, textual) | 3,659 videos | Quantitative | Textual analysis, deep learning, predictive modeling | Python (FFmpeg, OpenCV, NLTK, Python Imaging), Google Cloud Video Intelligence API |
| Deng | 2022 | Exploring the content of influencer-endorsed short videos about wine on TikTok (Douyin), with particular attention to gender and generational differences in perceptions and preferences | Comments associated with wine-related short videos (textual) | 10,042 comments on Douyin | Mixed methods | Python (Jieba for | |
| Dewantara | 2023 | Exploring Parasocial Interaction (PSI) attributes in travel vlogs and their influence on viewers’ travel intentions | Travel vlogs (metadata, visual, textual) and associated comments (textual) | 10 YouTube videos and 9,086 comments | Qualitative | Qualitative content analysis, nominal group technique, thematic analysis | NVivo, |
| Georgescu Paquin and Cerdan Schwitzguébel | 2021 | Analyzing the tourist landscape representation in Barcelona’s promotional videos in an overtourism context | Tourism promotional videos (visual) | 24 YouTube videos | Mixed methods | Quantitative visual content analysis, qualitative semiotic analysis | Not specified |
| Hoebanx and French | 2023 | Examining how slot machine videos on YouTube portray gambling and align with the norms of YouTube’s platform economy | Slot machine videos (visual, textual) and associated comments (textual) | 21 YouTube videos and 186 comments | Qualitative | Thematic analysis | Not specified |
| Huertas | 2017 | Analyzing how Spanish DMOs use YouTube to communicate their promotional videos and to study whether these videos communicate brands through attraction factors and emotional values | Tourism promotional videos (metadata, visual, textual) | 542 YouTube videos | Mixed methods | Quantitative content analysis, qualitative content analysis, statistical correlation | FanpageKarma |
| Ketter and Avraham | Examining digital marketing strategies used by NTBs during the COVID-19 pandemic | Tourism promotional videos (visual, textual) | 29 YouTube videos | Qualitative | Qualitative content analysis | Not specified | |
| Lakmali | Analyzing how tourists portray crisis-affected destinations through YouTube vlogs and their content creation motivations | Travel vlogs (metadata, textual) | 10 YouTube videos | Qualitative | Thematic analysis | ||
| Lang | Analyzing how Hangzhou’s cultural and tourism bureau constructs its international image on YouTube | Tourism promotional videos (visual, textual, auditory) | 83 YouTube videos | Qualitative | Multimodal critical discourse analysis | Not specified | |
| Lau | 2024 | Examining how museums reach Generation Z virtual tourists using TikTok videos and the relationship between video elements and types of engagement | Museum promotional videos (metadata, textual, auditory) | 313 videos on TikTok | Mixed methods | Thematic analysis, descriptive analysis, regression analysis | Excel, |
| Li | 2023 | Exploring cultural meaning construction in social media through analysis of Liziqi’s YouTube channel | Lifestyle vlogs (visual) and associated comments (textual) | 5 YouTube videos and 500 comments | Qualitative | Content analysis (coding), textual analysis (decoding) | Python, |
| Li | 2024 | Comparing spatial behavior of Chinese and foreign tourists based on landmark recognition in travel vlogs | Travel vlogs (visual, metadata) | 439 YouTube videos, 1,059 videos on Bilibili | Quantitative | Spatial analysis, landmark recognition | Baidu AI APIs, Baidu Maps Coordinate Picking System, Python |
| Ma | 2023 | Examining the effectiveness of bullet comments associated with food vlogs on the tourists’ travel intentions | Food vlogs (metadata, visual, textual, auditory) and associated bullet comments (textual) | 20 videos on Bilibili and 133,680 bullet comments | Mixed methods | Qualitative content analysis, thematic analysis, computational sentiment analysis | Python |
| Ma | 2023 | Exploring how food travel vlogs awaken travel intentions through viewers’ social and non-social emotions | Food vlogs (metadata) and associated comments (textual) | 32 videos on Bilibili and 91,437 comments | Mixed methods | Qualitative content analysis, | Python, (SnowNLP for sentiment analysis) |
| Mesana | 2024 | Mapping online viewers’ social and non-social emotions when watching UNESCO cultural heritage sites’ travel vlogs | Travel vlogs (metadata) and associated comments (textual) | 64 YouTube videos and 3,089 comments | Qualitative | Qualitative sentiment analysis, phronetic iterative data analysis | Data Miner |
| Motahar | 2021 | Exploring how Iran is framed as a travel destination by Social Media Influencers ( | Travel videos (metadata, visual, textual) | 10 YouTube videos | Qualitative | Netnography and narrative analysis | Not specified |
| Nazir | 2023 | Examining destination branding through social media, comparing foreign influencers’ narratives with official presentations | Travel vlogs (visual, textual) | 8 YouTube videos | Qualitative | Thematic analysis | NVivo |
| Rauf and Pasha | 2024 | Understanding Global North–South dynamics in YouTube gastronomic tourism videos and audience feedback | Food vlogs (metadata, textual) and associated comments (textual) | 9 YouTube videos and 128,000 comments | Mixed methods | Textual analysis, qualitative content analysis, computational sentiment analysis | Python, YouTube API |
| Salangsang | 2022 | Examining elements of luxury travel in tourism video advertisements from Asian countries during COVID-19 | Tourism promotional videos (metadata, visual, textual) | 122 YouTube videos | Qualitative | Qualitative content analysis | NVivo, Ncapture |
| Sharma | 2023 | Comparing message strategies adopted by celebrities vs social media influencers in brand-related YouTube content | Fashion and food industry videos (metadata, visual, textual, auditory) | 638 YouTube videos | Mixed methods | Qualitative content analysis, chi-square test, ANOVA, Mann–Whitney | |
| Tavakoli and Ling | 2022 | Exploring consumer perceptions of virtual food consumption and its sociological implications using online comments about a promotional video | Comments associated with a virtual food promotion video (textual) | 250 comments on Facebook | Qualitative | Thematic analysis | Not specified |
| Tham | 2023 | Examining how Penang, Malaysia, is marketed on TikTok by different stakeholders and how they present the destination’s image | Tourism promotional videos (metadata, visual, textual, auditory) | 30 videos on TikTok | Qualitative | Multimodal analysis, Burke’s Pentadic analysis | Not specified |
| Vujičić | 2021 | Examining the techno-social dimensions of tourist drone videography to understand production practices and creator differences | Drone vacation videos (metadata, visual) | 351 YouTube videos | Mixed methods | Qualitative content analysis, descriptive statistics, chi-square tests, regression analysis | Webometric Analyst, YouTube Statistics, |
| Warton and Brander | 2017 | Assessing the value of the TV show Bondi Rescue for improving tourist beach safety awareness | TV shows (visual, textual) | 98 episodes | Mixed methods | Qualitative content analysis, | |
| Wen | 2021 | Exploring travel constraints related to physician-assisted suicide tourism | Documentaries (metadata, textual) and associated comments (textual) | 25 YouTube videos and 1,231 comments | Qualitative | Thematic analysis | Nvivo |
| Yayla | 2024 | Examining food preferences and gastro-tourist typologies of digital nomads | Digital nomads videos (visual, textual) and associated comments (textual) | 21 YouTube videos and 326 comments | Qualitative | Qualitative content analysis | Not specified |
| Yıldırım and Kaya | 2024 | Investigating digital nomads’ impressions and reflections toward intangible cultural heritage ( | Travel videos (visual, textual) and associated comments (textual) | 5 videos on social media and associated comments | Qualitative | Thematic analysis | MAXQDA |
| Yoo, Kim | 2024 | Examining the relationships between discrete emotions expressed by travel influencers and viewer engagement | Travel videos (metadata, textual) | 5,008 YouTube videos | Quantitative | Computational sentiment analysis, regression analysis | Amazon Rekognition API, |
| Yoo, Piscarac | 2024 | Investigating the effectiveness of digital outdoor advertising in redefining urban tourism appeal and city branding using Seoul’s “Wave” campaign | Comments associated with YouTube videos (textual) | 956 comments on YouTube | Mixed methods | Word frequency analysis, centrality analysis, network analysis | Ucinet 6, NetDraw |
| Yu | 2019 | Investigating public perceptions of humanlike robots as employees in the hotel industry | Humanlike robot videos (metadata) and associated comments (textual) | 2 YouTube videos and 1,621 comments | Mixed methods | Cluster analysis, thematic analysis | NVivo, Data Miner |
| Zhang | 2021 | Analyzing public perceptions of service robots in hospitality and tourism amid COVID-19 | Comments associated with news report videos (textual) | 1,852 comments on YouTube | Quantitative | Explorative analysis (word cloud), computational sentiment analysis, | Python (Gensim for topic modeling), SentiStrength |
| Zhu | 2024 | Examining the impacts of content features of pro-environmental tourism videos on viewers’ in-consumption engagement | Pro-environmental videos (metadata, visual, textual, auditory) | 44 videos on Bilibili | Quantitative | Regression analysis | Microsoft Azure AI Video Indexer, Python (Librosa, FFmpeg), |