1. Introduction
Architectural and historical assets portray cultural heritage [1]. Architecture can serve as a medium for country promotion and a symbol of territorial identity [2]. Among the various aspects of cultural heritage studied in several countries, historical architecture, castles, and museums have been identified as fascinating places that attract tourists worldwide. Scholars have found that architectural heritage appeals to tourists and improves perceptions of place image [3,4]. The essence of understanding, defining, and interpreting living heritage for future generations is thus associated with illustrating the relationship between the architectural heritage and the tourists’ perception [3,5].
Destination image is essential in destination choice decision making, brand differentiation, and marketing [6]. Being competitive and differentiating themselves in the market is necessary for destinations [6], given that tourists desire more genuine and unique experiences [7]. Previous research showed that the destination image perceived by people is constantly evolving. At the same time, a nation’s history and culture have become a significant part of reality that subsequent generations cannot change [8]. Marketing materials are used as representative symbols in tourism discourse to shape tourists’ interpretation of a destination image [9]. The role of information in portraying the destination image is widely acknowledged in academia [10]. In the social media era, user interaction has far more subtle effects on destination image representation than what has been theorised and empirically investigated [11]. In the tourism industry, social media significantly impacts how people seek information and share it, their perception of destination image [12], and travelling choices [13].
Keeping a presence on social media is inescapable for destinations worldwide [14], and recent research on media’s effect on forming destination image focused on diversified social media platforms [15]. Cultivation theory is widely used to analyze how social media cultivates a destination image. Therefore, the controversy lies in selecting the online platforms and the content to promote the destination. It has been demonstrated that videos are more engaging because they can influence the potential demand from tourists [14]. YouTube is one of the largest video platforms worldwide [16] and the most popular media sharing website globally [17]. Therefore, the impacts of YouTube on destination image have attracted scholars’ attention.
Beijing, the capital of China and a historical city with more than 3000 years of history, possesses plenty of historical relics and cultural architectures. Its ancient charm of history is the main factor that attracts tourists [18]. Figure 1 presents a glimpse of the Forbidden City, and Figure 2 presents the location of eight architectural heritage sites investigated in this study.
According to the Academy of Contemporary China and World Studies [19], Beijing has been the most popular Chinese city among worldwide visitors. It is also the most crucial destination in China and a starting point for travelling to China [20]. French is one of the most widely used languages on the Internet and, at the same time, the only language with English on five continents among the top five widely spoken languages in the world [21]. The French content on YouTube is a considerable data source. The research rarely studies the formation of Beijing’s destination image via architectural heritage on YouTube. Therefore, fulfilling this research gap in understanding French-speaking users’ perception of Beijing’s architectural heritage will provide insight into strategies to enhance Beijing’s destination image in francophone territory. Thus, this study proposed the following three research questions:
(1). How does Beijing’s architectural heritage in French YouTube videos cultivate Beijing’s destination image?
(2). How do YouTube viewers perceive Beijing’s architectural heritage tourism destination image cultivated by these videos?
(3). What are the characteristics of YouTube video networks?
Based on this context and the three questions, this paper reviewed the literature on architectural heritage in tourism and online destination image cultivation via YouTube. Then, using big data mining, social network analysis, and natural language processing, this paper analysed Beijing’s architectural heritage from the projection and perception perspectives. Finally, this study discusses the data analysis results and provides implications and future research orientations.
2. Literature Review
2.1. Architectural Heritage in Tourism
Architectural heritage is a kind of immovable cultural heritage, including historical buildings, monuments, and archaeological sites [22]. According to Taher Tolou Del et al. [23], it is comprised of three components: monuments refer to all structures and buildings, including their fixtures and fittings, that have a prominent archaeological, aesthetic, historical, social, scientific, or technological interest; groups of buildings with historical, social, archaeological, aesthetic, scientific, or technical significance and which are sufficiently coherent to define topographic units; and sites that are partially built upon, distinctive enough to be topographically defined, and homogenous enough to be of significant archaeological, social, historical, artistic, scientific, or technical interest.
Architectural heritage motivates visitors to visit specific destinations [24]. It shapes people’s, especially tourists’, perception of a goal [3]. The creativity and aesthetics of various ethnicities in various places can be witnessed in historic architecture, which encompass many different types of monuments/sites and old buildings [25]. As a remarkable component of cultural heritage and tourism resources [26], architectural heritage includes the most significant commemorative and secondary buildings and their natural and artificial environments in historic towns and distinctive villages [27]. Therefore, numerous destinations have supported and subsidised their repurposing [3].
The research on architectural heritage and tourism has thus been developed from different perspectives. The majority of recent studies focused on its relationship with sustainable tourism, such as Wang et al. [28], Hmood et al. [29], Esmail [30], and Bogan [31]; protection and conservation of architectural heritage in tourism, such as Giannakopoulou and Kaliampakos [32], Zhang et al. [33], and Zhang et al. [34]; and territorial tourism development, such as Kostopoulou [35], Buchrieser [36], and Cocola-Gant [37].
However, research rarely explores its value in forming destination images, despite the critical role of architecture in the destination image. Previous studies usually considered cultural heritage as a measurement attribute of the destination image [38,39,40,41,42]. Others confirmed that the architectural heritages affected the representation or perception of the online destination image. For example, Kaur and Kaur [43] stated that architectural heritage is one of the image dimensions affecting tourists’ intention to recommend a heritage destination. Lojo et al. [44] found that Chinese travel blogs of Barcelona highly resembled the traditional city representations. Su et al. [45] evaluated Nanluoguxiang heritage street. They stated that multistakeholders shared the cognitive image as a classical, traditional street with Beijing style. Yang et al. [46] investigated the Grand Canal’s destination image in the WeChat official account. They summarised the tourist attraction’s features and offered suggestions for constructing destination brand image from the supply side.
Generally speaking, the existing literature considered architectural heritage only as a cognitive image attribute instead of investigating how it shapes and interprets the destination image on social media in depth.
2.2. YouTube and Online Destination Image Cultivation
The destination image is commonly defined as the beliefs, ideas, and impressions people have of a destination of place [47], in other words, the destination representation of an individual [48]. It has been considered the soul of the tourism industry’s development, which is crucial to destination competition and significantly impacts tourists’ purchase decisions [49].
Current studies confirmed that media could impact people’s perception of destination image [50,51,52], mainly social media in our era [53], such as Twitter [54], Sina Weibo [13], TripAdvisor [55], and YouTube [56]. Therefore, an increasing number of studies are currently introducing the idea of an “online destination image” due to the Internet’s recent emergence as the primary medium for communication and information sharing [46]. The online destination image refers to an online portrayal of knowledge, collective beliefs, ideas, feelings, and overall impression people hold about a destination [57]. There are three basic dimensions of a destination image: cognitive, emotional (named also affective image), and the overall images composed of cognitive and emotional images [46,47]. The emotional part of online content can be expressed through adjectives or information concerning subjective feelings, while the cognitive part can be expressed through nouns or information concerning the object [58]. The online destination image can be explained by the perceived image of the tourists and the projected image of the destination [8,59]. For a long time, the projected image could be studied by reviewing how destination marketing organisations, marketers, and tourism-relevant websites benefit from the Internet for promoting a destination, which can be seen as a brand image or reputation [46]. While most research throws light on the perceived destination image and is centred around the perception of visitors or potential visitors [45], there is a lack of study on both the projected and perceived destination images of the general public.
Moreover, there is controversy that the perceived destination image is continuously changing. It needs the accumulation of collected information and time [60], and the destination image often relies on content and information generated by visitors, residents, and suppliers [61]. However, the history and culture of the country are objective and cannot be changed by later generations [8], especially the architectural heritage. Therefore, the importance of media information in constructing and communicating the destination image has become more critical.
Currently, information sources and communication methods have radically evolved. Social media offers multifunctional online platforms which meet people’s need for image cultivation, real-time communication, and creative self-realisation [62]. Tourists increasingly rely on social-media-generated online destination images [63]. They also construct the timely online destination image [46]. Therefore, selecting the appropriate platform and content to promote the destination is vital. Among all types of content, such as text, audio, and figure, videos are more engaging, and they might have a stronger impact on the potential demand of tourists [14].
YouTube is one of the largest video platforms and most popular media-sharing social networks worldwide [16,17]. YouTube allows destination management organisations, tourists, tourism companies, and other businesses to showcase their brands and identities [14]. Its impact on a place image and how it impacts the place image have attracted scholar’s attention, such as Tiago, Moreira, and Borges-Tiago [14], Huertas et al. [64], and Chang [56]. Therefore, it would be meaningful to study YouTube content.
Most scholars believe that various types of social media remarkably impact destination image perception [65,66,67]. They attempt to explain the impact of media on destination image. Introduced by Gerbner, cultivation theory focuses on how comprehensive messages gradually influence the public as they expose to media messages daily. This theory emphasises how the institutional practices of the media can shape meanings in the process of mass dissemination of information, thereby influencing public knowledge and beliefs over time [68]. This system approach divides the interaction effect into three components: firstly, media institutions refer to new symbolic environments being created due to the mass production and rapid dissemination of messages. Secondly, mass-produced messages mean that specific meanings are produced and shared throughout the media environment. Thirdly, the cultivation effect refers to widespread messages that cultivate public beliefs in media [68].
Accordingly, three analysis types are outlined. First, institutional analysis investigates how the mass media interact with other institutions, make decisions, create message systems, and carry out their societal roles. Second, message system analysis explores how collections of messages can be viewed as dynamic systems with symbolic functions having social consequences. Finally, cultivation analysis investigates what common ideas, images, thoughts, and associations these messages try to cultivate in large and heterogeneous communities and the implications for public policy [68,69].
The empirical evidence on the formation of Beijing’s destination image via architectural heritage on YouTube needs to be more presented. This article aimed to fill these gaps. First, it needed a deeper understanding of the impact of architectural heritage on destination images via big data mining, social network analysis, and natural language processing methods. Second, a comparison between projected and perceived images was needed. Third, French was selected, as it is one of the most widely used languages on the Internet and the second most used diplomacy language [21]. It is also the only language spoken on five continents alongside English [70].
3. Data Collection and Analysis
Following the system approach of cultivation theory [69] and the online destination types [46], this section analyses Beijing’s online architectural heritage destination image from both the projection and perception sides.
First, data on Beijing’s architectural heritage tourism relevant French videos on YouTube were collected via YouTube API v3. Open application programming interfaces (APIs) promote interoperability via the provision of data-sharing tools to create widely used web apps, enable seamless social media service integration, give rise to developer ecosystems that are mutually beneficial, and build on top of social media platforms [71].
Then, the comments of these videos were retrieved via NodeXL version 1.0.1.510, an open-source graph visualisation tool from Microsoft. It was utilised for a social network analysis of the YouTube data. It is installed as an add-in to Microsoft Excel. NodeXL supports social network structure analysis. It can directly import social network data from Twitter, YouTube, Flickr, and email or use a multifunctional plug-in to capture real-time social network data from social networks. It can perform centrality analysis by multiple clustering methods. NodeXL also supports manual and various automatic layouts in real-time and filters and displays data according to specified conditions [72]. Through the official YouTube API (v3), NodeXL allows researchers to collect videos and comments with titles, keywords, descriptions, or users’ IDs. The collected metadata can then be visualised through several algorithms and methods, such as Clauset–Newman–Moore algorithm, Harel–Koren fast multiscale algorithm, force-directed, and Treemap [72]. NodeXL has been proven as a useful tool for collecting and analysing YouTube data [73,74].
Next, as per Deng and Li [58], to understand the online cognitive and emotional images, sentiment and KWIC analyses were applied. The sentiment analysis was conducted via MeaningCloud version 1.0.0 to understand the emotion of the architectural heritage in the YouTube videos and their comments. The accuracy of MeaningCloud has been ensured, since it uses hybrid machine interpretation, which combines lexicon with a rule-based machine translation (RBMT) approach and corpus-based interpretation and machine learning. Various studies have accepted and proved its accuracy for sentiment analysis [75]. KWIC analysis was applied for understanding the cognitive–emotional image. KWIC refers to extracting cooccurrence words from the text [76]. Given a specific keyword, this technique extracted words’ strings and presented the keyword’s dominant meaning and contexts [77]. To be specific, the projected emotional images were investigated through sentiment analyses of YouTube video descriptions. The perceived emotional images were investigated through sentiment analyses of videos’ comments. A KWIC analysis was applied for investigating the perceived cognitive–emotional image.
In addition, to understand the video viewers’ and commentors’ behaviour on sharing information concerning Beijing’s destination image regarding architectural heritage, video viewers’ preference of video type was investigated via MANOVA by SPSS 19. Video commentors’ interactive behaviour was investigated through social networks analysis.
3.1. Data Collection
To determine the keywords for mining data on YouTube, which is the most appreciated tourist architectural heritage in Beijing in the eyes of French speakers, this research referenced Beijing’s tourism site rank on TripAdvisor. TripAdvisor is now the largest travel platform [78], where the reviews are deemed to be reliable [79,80]. Referencing the rank of the most appreciated monuments in Beijing on TripAdvisor [81], we eliminated 2 modern architectures and merged all 8 sections of the Great Wall as one keyword, i.e., the Great Wall. Finally, 8 French keywords of the traditional architectural heritage of Beijing for mining data on YouTube are shown in Table 1.
YouTube open application programming interfaces (open APIs) were utilised for gathering the relevant data on YouTube. As per Yao, Li, and Song [73], this study gathered 2646 YouTube videos in French. After eliminating the videos without descriptions, finally, 2237 videos were reserved, and their 25,234 comments were collected. The data collected via open APIs included channel ID, channel title, video ID, publication date, video title, video description, tags, video category, duration, dimension, definition quality, view count, like count, dislike count, favourite count, comments of videos, and some technical parameters.
3.2. Data Analysis
3.2.1. Video Type Preference
The first analysis step involved an investigation of YouTube users’ preference for video type. When uploading videos, YouTube users can assign standard categories (label tags) for them [73]. This study found that there were 7 video types. MANOVA was applied to understand the type of video concerning previous architectural heritage in Beijing that fascinated the most French-speaking YouTube users. A multivariate analysis of variance was used when there were two or more dependent variables. It is helpful for inferring interaction effects in metric multivariate multifactor data [82].
The result was presented in Table 2 and Table 3. The data are usually analysed based on Wilk’s lambda [82]. Wilks’ lambda was used to test the dissimilarity between the means of identified groups [83]. The multivariate tests showed that the p-value of Wilk’s lambda was 0.000, which means p < 0.0005. There was a statistically significant difference in YouTube users’ behaviour (number of views, likes, and comments) between video types, F (18, 7456) = 3.59, p < 0.0005; Wilk’s λ = 0.976, and partial η2 = 0.008. Tukey, a pairwise comparison technique, was applied as a post hoc test in this study. Its function is to use the predetermined statistical distribution to calculate the honest significant difference (i.e., the HSD) between two means [84]. According to the Tukey post hoc comparisons, YouTube users tended to view, like, and comment on entertainment videos.
3.2.2. Sentiment Analysis of Beijing’s Architectural Heritage Relevant Videos
To understand the attitudes that the YouTube videos portrayed towards Beijing’s destination image and issues they were concerned about, this study investigated the video descriptions via sentiment analysis using MeaningCloud. The sentiment of 2237 video descriptions were divided into strong negative, negative, neutral, positive, and strongly positive. The sentiment analysis result is illustrated in Figure 3.
The result in Figure 3 reflects that 94.46% of the video descriptions were neutral, positive, and strongly positive. This illustrates that people’s perceptions of videos related to Beijing’s architectural heritage as a tourist spot were neutral (1177), positive (788), and strongly positive (11). This implies that video viewers might obtain positive/neutral messages regarding Beijing’s architectural heritage.
3.2.3. Viewer’s Social Network Analysis
To understand how YouTube video viewers perceive Beijing’s destination image and their social interactions when watching videos, this study conducted a social network analysis.
Social network analysis measures and visualises informal and formal relationships to discover what promotes or hinders knowledge flows binding interacting units [85]. The rise of social media has offered a chance to build large social networks essential for communicating information, ideas, and influence in which typic phenomena in real-life are word-of-mouth effects [86].
Step one was an investigation of the relationships among YouTube viewers. In the network, the cluster called community, refers to a group of nodes connecting closely. This study used NodeXL to mine YouTube users’ comments and analyse the social networks of these comments and then conducted a sentimental analysis of the video comments via MeaningCloud, as per Yao, Li, and Song [73]. Nodes (i.e., “vertices” or “entities”) refer to a social structure or content, virtual physical location, event, or individual, for instance, an institution, organisation, or country [87]. The Clauset–Newman–Moore algorithm, a widely used algorithm for analysing large networks and community classification [88], was used to characterise the network. This greedy heuristic method is ideal for quickly discovering communities [89], which seek to examine all nodes pair by pair to identify which two nodes can be merged as a cluster after first assuming that all nodes are independent clusters [90].
Clauset–Newman–Moore algorithm
Algorithm 1 applied after the data visualisation was the Harel–Koren fast multiscale layout algorithm, which aims to generate a graph [91]. The algorithm is as follows.
| Algorithm 1: Harel–Koren fast multiscale layout algorithm |
| Layout (G (V, E)) |
| Goal: Find L, a nice layout of G |
| 1. Compute the all-pairs shortest distance |
| 2. Set up a random layout L |
| 3. |
| 4. while do |
| 4.1 |
| 4.2 |
| 4.3 |
| 4.3.1 |
| 4.4 |
| 5. end |
Calculating via these two algorithms, the multiple clusters of video viewers was organised as shown in Figure 4, and the data results are in Table 4. Figure 4 presents the inactive network clusters of video commenters visualised using the Harel–Koren fast multiscale layout algorithm. Table 4 details a graphic metric of video commentors on YouTube and interprets Figure 4 via the index and value. Figure 4 and Table 4 reflect video commenters’ social networks, in other words, their interactive behaviour, when watching these videos.
According to the above data results, there were 22,677 vertices in the networks composed of Beijing’s architectural heritage tourism video commenters. Among them, 20,867 were unique edges, and 4367 were edges with duplicates. The average geodesic distance signifies the average number of paths that one node uses to reach the others [92]. In this study, the average geodesic distance was 10.042648, meaning that 10 people were needed to reach others via 10 people. This exceeded the value defined by Guare [93] in the six degrees of separation, where everyone is linked to everyone else by a chain of six or fewer people [94]. Compared with this value in other knowledge sharing cases in social media, for example, the value 3.6 for francophone users and 3.2 for English speaking users in Twitter when sharing occupational safety knowledge [95], such a result coincides with Yao, Li, and Song’s [73] finding that YouTube users’ viewing behaviour showed less crossviews and comments.
This revealed a discursive connection among the video commenters on YouTube. Moreover, the modularity determines the fitness of the groups in a network. It counts the number of edges that split from one group to join another [92]. The number in this study was 0.875765. The higher the degree of modularity, the lower the group’s quality [96]. Therefore, this social network’s average geodesic distance and modularity value proved that the interaction among these YouTube commenters was loose. Referencing research by Yao et al. [97], this result could be interpreted that the commenters of these videos were unwilling to visit different clusters to discuss Beijing’s tourist architectural heritage.
3.2.4. Semantic Analysis of YouTube Users’ Comments
To understand the perceptions and attitudes of YouTube commenters towards Beijing’s destination image, the first step involved a sentiment analysis with MeaningCloud. The distribution of 25,234 comments’ sentiment is presented in Figure 5. The video descriptions and comments’ sentiments were divided into 5 categories: N+ (strongly negative), N (negative), Neu (neutral), P (positive), and P+ (strongly positive). Figure 5 reflects the commentors’ perceived emotional (affective) image. It illustrates that most were neutral comments, and 53.85%, and 36.79% were a positive or strongly positive sentiment. These statistics reflects that when watching videos, most YouTube viewers held a positive or neutral attitude towards Beijing’s architectural heritage. In line with the video contents, negative comments were substantially fewer, meaning that YouTube viewers do not receive much negative messages by reading the comments.
Meanwhile, further comment content analysis via keyword in context (KWIC) was conducted to understand viewers’ views. Nowadays, multilingual scenarios in social media are expected. In this study, the comments contain many different languages. To analyse French speakers’ attitudes more accurately towards Beijing’s architectural heritages, a Markov chain-based method was used to identify the languages in the comments. Markov chains are widely applied in the financial industry and data science, such as for handwriting recognition, spam filtering, and text generation. A Markov chain is a stochastic process with a set of states shifting from one state to another. These transitions are decided by probabilities, which depend on the current state of the Markov chain [98,99]. A Markov chain-based method can identify the language through the maximum likelihood decision rule. Empirical studies proved that compared with the N-gram method, this method could recognise languages with a fast speed and lower error rate [100].
After the analysis, 134 languages were found in the comments, and 13,518 (approximately 55%, ranked no. 1) were French. This result shows that Beijing’s classic architecture has aroused broad interest on YouTube. Based on the previous sentiment analysis, we believed that the classic architecture in Beijing was neutrally or positively perceived worldwide, and it was concluded that a video of classic architecture is an effective tool to promote the image of Beijing as a destination.
Next, simple word frequency was analysed, and the 10 most frequent and meaningful French keywords in the video comments were found. Then, this study investigated the keyword in context (KWIC) to better understand these French comments. This study extracted five contextual words to the specific keyword’s left and right. For each frequent keyword in the video comments, this study picked the top 10 meaningful contextual words according to their scores. The formula used to calculate the score is shown below. “” and “” refer to the frequency with which the contextual word appears before or after the keyword, and the formula is shown below.
KWIC calculated the number of contextual words to the left and right of the keywords and the total count. The higher the score, the higher the correlation between the contextual word and the keyword. The KWIC analysis result reflects the cognitive–emotional image perceived by the commentors. Table 5 lists the context of these keywords, and Appendix A (Table A1) provides detailed information. Table 5 lists the most frequent and meaningful French keywords mentioned in the video comments, contextual words, number of times that the contextual words appeared on the right- and left-hand sides of the French keywords, and the score, which indicates the correlation between the French keywords and contextual words.
Through KWIC, this study found that the most frequent word was “merci (thanks)”, which shows that audiences were grateful when they watched these videos to acquire Beijing’s information. Many positive adjectives, such as “grand (great)”, “bonne (good)”, “beau (beautiful)”, and “aime (love)”, were found, which reflects the audiences’ good impression of Beijing’s architectural heritage and the city where it was situated. Meanwhile, words such as “magnifique (magnificent)” and adorer (adore) indicate that Beijing’s classic architecture clips were also popular with audiences. In addition, “the Great Wall” was frequently mentioned, and it can be inferred that the Great Wall is a symbol widely recognised by the French with a positive attitude. However, we also found that “Chine (China)” was most frequently mentioned while “Beijing” was absent, which shows that when people watched these videos, they associated these architectural heritages to China rather than Beijing itself, another frequent word “pays (country)” also corroborated this finding.
4. Discussion
4.1. General Discussion
Destination image (Table A2 in Appendix B) has been crucial in destination choice, brand differentiation, and marketing [6]. Architectural heritage shapes a destination’s image. It significantly motivates visits to specific destinations [24]. The destination image in people’s eyes has constantly been changing [8], while the architectural heritage, culture, and history cannot be rewritten, as these resource endowments have historically been relatively stable. Social media provides a convenient platform for the international communication of destination images. Applying Gerbner’s cultivation theory, this paper analysed both the projected destination image and perceived destination image of Beijing on YouTube. The result found that architectural heritage can shape Beijing’s destination image via social media, which has far-reaching significance, contributes to the communication of the architectural heritage of Beijing, and proposes helpful suggestions for shaping Beijing’s destination image in the digital era.
To answer the first research question, by MANOVA analysis, this paper found that among Beijing architectural heritage-related tourism videos, the entertainment-type videos fascinated the most viewers. YouTube users considered Beijing architectural heritage-related tourism clips as entertainment videos. At the same time, the sentiment analysis of the video descriptions showed that YouTube video uploaders generally held a positive or neutral attitude towards Beijing’s architectural heritage-related tourism videos. This result shows that these videos exhibited a neutral or complimentary projected online destination image of Beijing.
To answer the second question, the sentiment analysis found that 94.46% of video descriptions held a neutral or positive sentiment, the comment results corroborated that 90.64% of viewers held a neutral or positive sentiment. Approximately 53.84% of viewers held a neutral attitude towards Beijing’s architectural heritage-related tourism videos, and approximately 36.79% held a positive or strongly positive sentiment attitude towards Beijing’s architectural heritage-related tourism videos. This result reflects that architectural heritage-related tourism videos can indeed cultivate people’s perceived destination images. YouTube viewers hardly received negative information from architectural heritage clips.
In addition, the KWIC analysis found that although this research focused on Beijing and its architectural heritages when watching these videos, francophone people tended to connect architecture with the country rather than the city. Among many architectural heritages, the Great Wall was not the first but the most widely recognised by francophones. This result presents that the architectural heritage did not only portray the destination image but impacted the destination country’s image to be stronger. Moreover, the KWIC analysis showed that the videos (or TV series) with architectural heritage elements fascinated them and shaped a positive image, which further connects the destination image to the destination country image.
The answers to the first and second questions illustrate the consistency of the projected and perceived destination image, therefore indicating the cultivation effect of these YouTube videos. This finding suggests the necessity of enhancing the role of architectural heritage to shape a better destination image, since it effectively impacts the destination image [46]. As Costa and Carneiro [24] stated, in addition to investigating the general image of the destination, it is essential to examine the image of specific elements of the tourism destination, such as its architectural heritage, and to investigate how visitors learn about this heritage, as it is one of the primary attractions of many destinations.
To answer the final question regarding the clusters that developed in the YouTube videos networks, the social network analysis showed that although users crosswatched some videos on Beijing’s architecture and left comments, the mediating distance between users was long, and no positive interactions were found. In addition, users were less willing to leave their clusters to discuss with other clusters. This showed that Beijing’s architecture videos lacked the means to stimulate users’ interactions. This finding suggests the needs to improve users’ interaction to better communicate and cultivate Beijing’s destination image, since information and knowledge sharing often requires users’ extensive interaction on social media [101].
4.2. Contributions and Implication
This study expanded the research on Beijing’s architectural heritage via online social media analysis, and it is significant to online destination image studies and development, offering practical and academic insights.
Firstly, this study provides a comprehensive understanding of Beijing’s architectural heritage online destination image based on French YouTube videos’ content and comments. It overcame the sample size problems of surveys and qualitative interviews. At the same time, this study used big data mining and analysis, which enhanced the validity and reliability of the research results [46]. This study contributes to local governments regarding how social media projects the destination image and how users perceive the destination information via YouTube videos.
Secondly, this study applied a mixed approach that combined big data mining, social networks analysis by algorithm, KWIC analysis, and semantic analysis by natural language processing, including word cloud and sentiment analyses, which innovated the statical research method in the architectural heritage study and saving costs. In contrast, it is easier to gather and process unstructured text [102], generating a complete and pellucid result. Therefore, this study enriched the research method in online architectural heritage relevant destination image study.
Thirdly, as for policy and managemental enlightenment, this study proposes that the tourism agencies of Beijing cooperate with opinion leaders for destination country image building. In addition, online celebrities should be invited to close the distance with users by sharing their own stories. Sharing experiences, tourism-related suggestions, and expressing emotions can effectively motivate users to comment, like, and forward [103]. On the other hand, the research results also show that when watching architectural heritage tourism in Beijing, viewers tended to associate it with the country with such architectural heritage. Therefore, organisations or agencies that wish to improve the destination country image may choose an appropriate city or cities as representation. Another finding of this study was that videos containing architectural heritage should be actively used for international communication, because they can create a positive attitude among audiences and indirectly enhance the image of a city or even a country.
4.3. Limitation and Future Works
This study also has some limitations. Firstly, this study focused only on YouTube, and there are many social media platforms worth investigating. Thus, it is recommended to include more social media platforms in the future.
Secondly, this study only investigated Beijing, and many other heritage sites in other cities such as Rome were not included. Thus, in the future, it is suggested to extend the target area to other territories or compare several cities in the same country.
Thirdly, this study investigated only Beijing’s architectural heritage tourism relevant videos in French as a pilot study. Videos in other languages, such as English, Chinese, German, Korean, Japanese, and Spanish, can be studied in the future. In addition, this study conducted only KWIC analyses for comments in French, while 134 languages were found in the comments. Thus, it is recommended that future research could investigate videos in other languages or compare videos and comments of different languages to understand better the cultivation effect of the projected and perceived architectural heritage relevant to the online destination image in different languages.
Conceptualisation and writing—original draft preparation, methodology, visualisation, and writing—review, L.S.; writing—original draft, review and editing, R.Y.M.L.; writing—review and editing and supervision, T.W. All authors have read and agreed to the published version of the manuscript.
Not applicable.
The data presented in this study are available in article.
The authors are grateful to the reviewers, whose insightful comments and helpful suggestions significantly contributed to improving this paper.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 2. Map of the 8 architectural heritage locations in this study in Beijing (source: generated by authors).
Enlarge this image.Figure 4. Inactive network clusters of the video commenters visualised using the Harel–Koren fast multiscale layout algorithm.
Top architectural heritages of Beijing.
| Rank | Name (in French) | Name (in English) |
|---|---|---|
| 1 | La Grande Muraille | The Great Wall |
| 2 | Cité Interdite | The Forbidden City |
| 3 | Palais d’Eté | Summer Palace |
| 4 | Temple du Ciel | Temple of Heaven |
| 5 | Parc Jingshan | Jingshan Gongyuan |
| 6 | Parc Beihai | Beihai Park |
| 7 | La Ville d’Eau de Gubei | Gubei Water Town |
| 8 | Place de Tiananmen | Tiananmen |
Source: adapted from TripAdvisor [
MANOVA results.
| Multivariate Tests c | |||||||
|---|---|---|---|---|---|---|---|
| Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta Squared | |
| Intercept | Pillai’s Trace | 0.002 | 1.708 a | 3.000 | 2636.000 | 0.163 | 0.002 |
| Wilks’ Lambda | 0.998 | 1.708 a | 3.000 | 2636.000 | 0.163 | 0.002 | |
| Hotelling’s Trace | 0.002 | 1.708 a | 3.000 | 2636.000 | 0.163 | 0.002 | |
| Roy’s Largest Root | 0.002 | 1.708 a | 3.000 | 2636.000 | 0.163 | 0.002 | |
| VideoType | Pillai’s Trace | 0.024 | 3.580 | 18.000 | 7914.000 | 0.000 | 0.008 |
| Wilks’ Lambda | 0.976 | 3.590 | 18.000 | 7456.219 | 0.000 | 0.008 | |
| Hotelling’s Trace | 0.025 | 3.598 | 18.000 | 7904.000 | 0.000 | 0.008 | |
| Roy’s Largest Root | 0.018 | 7.717 b | 6.000 | 2638.000 | 0.000 | 0.017 | |
a Exact statistic. b Statistic is an upper bound on F that yields a lower bound on the significance level. c Design: Intercept + VideoType.
Tukey post hoc comparisons for the video types.
| Tukey HSD | |||||||
|---|---|---|---|---|---|---|---|
| Dependent Variable | (I) Video Type | (J) Video Type | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
| Lower Bound | Upper Bound | ||||||
| View Count | Entertainment | Sport | 1,133,731.0802 | 777,467.78398 | 0.770 | −1,160,268.3037 | 3,427,730.4641 |
| Travel and Event | 1,119,576.2946 * | 262,878.64937 | 0.000 | 343,925.5575 | 1,895,227.0317 | ||
| People and Blog | 1,125,327.8333 * | 284,779.24448 | 0.002 | 285,057.1175 | 1,965,598.5491 | ||
| News and Politics | 975,491.9188 | 347,105.55338 | 0.074 | −48,679.0398 | 1,999,662.8774 | ||
| Education and Science | 1,100,783.0888 | 391,002.55643 | 0.073 | −52,910.5330 | 2,254,476.7106 | ||
| Others | 1,134,624.4533 | 761,717.05111 | 0.751 | −1,112,900.7586 | 3,382,149.6652 | ||
| Sport | Entertainment | −1,133,731.0802 | 777,467.78398 | 0.770 | −3,427,730.4641 | 1,160,268.3037 | |
| Travel and Event | −14,154.7856 | 777,228.16498 | 1.000 | −2,307,447.1488 | 2,279,137.5775 | ||
| People and Blog | −8403.2469 | 784,906.14358 | 1.000 | −2,324,350.2827 | 2,307,543.7889 | ||
| News and Politics | −158,239.1614 | 809,605.27502 | 1.000 | −2,547,063.5479 | 2,230,585.2250 | ||
| Education and Science | −32,947.9914 | 829,374.12263 | 1.000 | −2,480,102.4144 | 2,414,206.4316 | ||
| Others | 893.3730 | 1,056,026.18438 | 1.000 | −3,115,021.4806 | 3,116,808.2267 | ||
| Travel and Event | Entertainment | −1,119,576.2946 * | 262,878.64937 | 0.000 | −1,895,227.0317 | −343,925.5575 | |
| Sport | 14,154.7856 | 777,228.16498 | 1.000 | −2,279,137.5775 | 2,307,447.1488 | ||
| People and Blog | 5751.5387 | 284,124.41535 | 1.000 | −832,587.0358 | 844,090.1132 | ||
| News and Politics | −144,084.3758 | 346,568.50766 | 1.000 | −1,166,670.7253 | 878,501.9736 | ||
| Education and Science | −18,793.2058 | 390,525.88192 | 1.000 | −1,171,080.3500 | 1,133,493.9384 | ||
| Others | 15,048.1587 | 761,472.47571 | 1.000 | −2,231,755.4081 | 2,261,851.7255 | ||
| People and Blog | Entertainment | −1,125,327.8333 * | 284,779.24448 | 0.002 | −1,965,598.5491 | −285,057.1175 | |
| Sport | 8403.2469 | 784,906.14358 | 1.000 | −2,307,543.7889 | 2,324,350.2827 | ||
| Travel and Event | −5751.5387 | 284,124.41535 | 1.000 | −844,090.1132 | 832,587.0358 | ||
| News and Politics | −149,835.9145 | 363,460.81535 | 1.000 | −1,222,264.7709 | 922,592.9419 | ||
| Education and Science | −24,544.7445 | 405,591.54114 | 1.000 | −1,221,284.6798 | 1,172,195.1908 | ||
| Others | 9296.6199 | 769,307.71805 | 1.000 | −2,260,625.6425 | 2,279,218.8824 | ||
| News and Politics | Entertainment | −975,491.9188 | 347,105.55338 | 0.074 | −1,999,662.8774 | 48,679.0398 | |
| Sport | 158,239.1614 | 809,605.27502 | 1.000 | −2,230,585.2250 | 2,547,063.5479 | ||
| Travel and Event | 144,084.3758 | 346,568.50766 | 1.000 | −878,501.9736 | 1,166,670.7253 | ||
| People and Blog | 149,835.9145 | 363,460.81535 | 1.000 | −922,592.9419 | 1,222,264.7709 | ||
| Education and Science | 125,291.1700 | 451,539.08507 | 1.000 | −1,207,021.7621 | 1,457,604.1022 | ||
| Others | 159,132.5345 | 794,491.92076 | 1.000 | −2,185,098.3318 | 2,503,363.4007 | ||
| Education and Science | Entertainment | −1,100,783.0888 | 391,002.55643 | 0.073 | −2,254,476.7106 | 52,910.5330 | |
| Sport | 32,947.9914 | 829,374.12263 | 1.000 | −2,414,206.4316 | 2,480,102.4144 | ||
| Travel and Event | 18,793.2058 | 390,525.88192 | 1.000 | −1,133,493.9384 | 1,171,080.3500 | ||
| People and Blog | 24,544.7445 | 405,591.54114 | 1.000 | −1,172,195.1908 | 1,221,284.6798 | ||
| News and Politics | −125,291.1700 | 451,539.08507 | 1.000 | −1,457,604.1022 | 1,207,021.7621 | ||
| Others | 33,841.3644 | 814,627.61193 | 1.000 | −2,369,801.9483 | 2,437,484.6772 | ||
| Others | Entertainment | −1,134,624.4533 | 761,717.05111 | 0.751 | −3,382,149.6652 | 1,112,900.7586 | |
| Sport | −893.3730 | 1,056,026.18438 | 1.000 | −3,116,808.2267 | 3,115,021.4806 | ||
| Travel and Event | −15,048.1587 | 761,472.47571 | 1.000 | −2,261,851.7255 | 2,231,755.4081 | ||
| People and Blog | −9296.6199 | 769,307.71805 | 1.000 | −2,279,218.8824 | 2,260,625.6425 | ||
| News and Politics | −159,132.5345 | 794,491.92076 | 1.000 | −2,503,363.4007 | 2,185,098.3318 | ||
| Education and Science | −33,841.3644 | 814,627.61193 | 1.000 | −2,437,484.6772 | 2,369,801.9483 | ||
| Like Count | Entertainment | Sport | 7398.2209 | 4,289.68349 | 0.599 | −5258.9355 | 20,055.3772 |
| Travel and Event | 7533.5302 * | 1450.43464 | 0.000 | 3253.8723 | 11,813.1881 | ||
| People and Blog | 7349.8283 * | 1571.27131 | 0.000 | 2713.6292 | 11,986.0273 | ||
| News and Politics | 5294.6672 | 1915.15712 | 0.083 | −356.2027 | 10,945.5370 | ||
| Education and Science | 6652.8486 * | 2157.35912 | 0.034 | 287.3367 | 13,018.3606 | ||
| Others | 7297.0814 | 4202.77872 | 0.592 | −5103.6534 | 19,697.8162 | ||
| Sport | Entertainment | −7398.2209 | 4289.68349 | 0.599 | −20,055.3772 | 5258.9355 | |
| Travel and Event | 135.3094 | 4288.36139 | 1.000 | −12,517.9460 | 12,788.5647 | ||
| People and Blog | −48.3926 | 4330.72469 | 1.000 | −12,826.6453 | 12,729.8600 | ||
| News and Politics | −2103.5537 | 4467.00230 | 0.999 | −15,283.9076 | 11,076.8002 | ||
| Education and Science | −745.3722 | 4576.07705 | 1.000 | −14,247.5625 | 12,756.8180 | ||
| Others | −101.1395 | 5826.63125 | 1.000 | −17,293.2197 | 17,090.9407 | ||
| Travel & Event | Entertainment | −7533.5302 * | 1450.43464 | 0.000 | −11,813.1881 | −3253.8723 | |
| Sport | −135.3094 | 4288.36139 | 1.000 | −12,788.5647 | 12,517.9460 | ||
| People and Blog | −183.7020 | 1567.65829 | 1.000 | −4809.2404 | 4441.8365 | ||
| News and Politics | −2238.8631 | 1912.19397 | 0.905 | −7880.9898 | 3403.2637 | ||
| Education and Science | −880.6816 | 2154.72906 | 1.000 | −7238.4333 | 5477.0701 | ||
| Others | −236.4488 | 4201.42928 | 1.000 | −12,633.2019 | 12,160.3043 | ||
| People and Blog | Entertainment | −7349.8283 * | 1571.27131 | 0.000 | −11,986.0273 | −2713.6292 | |
| Sport | 48.3926 | 4330.72469 | 1.000 | −12,729.8600 | 12,826.6453 | ||
| Travel and Event | 183.7020 | 1567.65829 | 1.000 | −4441.8365 | 4809.2404 | ||
| News and Politics | −2055.1611 | 2005.39738 | 0.948 | −7972.2942 | 3861.9720 | ||
| Education & Science | −696.9796 | 2237.85393 | 1.000 | −7299.9999 | 5906.0406 | ||
| Others | −52.7469 | 4244.66028 | 1.000 | −12,577.0575 | 12,471.5638 | ||
| News and Politics | Entertainment | −5294.6672 | 1915.15712 | 0.083 | −10,945.5370 | 356.2027 | |
| Sport | 2103.5537 | 4467.00230 | 0.999 | −11,076.8002 | 15,283.9076 | ||
| Travel and Event | 2238.8631 | 1912.19397 | 0.905 | −3403.2637 | 7880.9898 | ||
| People and Blog | 2055.1611 | 2005.39738 | 0.948 | −3861.9720 | 7972.2942 | ||
| Education and Science | 1358.1814 | 2491.36980 | 0.998 | −5992.8637 | 8709.2266 | ||
| Others | 2002.4142 | 4383.61428 | 0.999 | −10,931.8946 | 14,936.7231 | ||
| Education and Science | Entertainment | −6652.8486 * | 2157.35912 | 0.034 | −13,018.3606 | −287.3367 | |
| Sport | 745.3722 | 4576.07705 | 1.000 | −12,756.8180 | 14,247.5625 | ||
| Travel and Event | 880.6816 | 2154.72906 | 1.000 | −5477.0701 | 7238.4333 | ||
| People and Blog | 696.9796 | 2237.85393 | 1.000 | −5906.0406 | 7299.9999 | ||
| News and Politics | −1358.1814 | 2491.36980 | 0.998 | −8709.2266 | 5992.8637 | ||
| Others | 644.2328 | 4494.71308 | 1.000 | −12,617.8846 | 13,906.3502 | ||
| Others | Entertainment | −7297.0814 | 4202.77872 | 0.592 | −19697.8162 | 5103.6534 | |
| Sport | 101.1395 | 5826.63125 | 1.000 | −17,090.9407 | 17,293.2197 | ||
| Travel and Event | 236.4488 | 4201.42928 | 1.000 | −12,160.3043 | 12,633.2019 | ||
| People and Blog | 52.7469 | 4244.66028 | 1.000 | −12,471.5638 | 12,577.0575 | ||
| News and Politics | −2002.4142 | 4383.61428 | 0.999 | −14,936.7231 | 10,931.8946 | ||
| Education and Science | −644.2328 | 4494.71308 | 1.000 | −13,906.3502 | 12,617.8846 | ||
| Comment Count | Entertainment | Sport | 399.7862 | 354.55697 | 0.920 | −646.3710 | 1445.9433 |
| Travel and Event | 406.6881 * | 119.88337 | 0.012 | 52.9598 | 760.4164 | ||
| People and Blog | 365.1875 | 129.87093 | 0.074 | −18.0102 | 748.3851 | ||
| News and Politics | −106.2006 | 158.29427 | 0.994 | −573.2643 | 360.8631 | ||
| Education and Science | 321.3022 | 178.31309 | 0.547 | −204.8291 | 847.4335 | ||
| Others | 312.9086 | 347.37400 | 0.973 | −712.0544 | 1337.8717 | ||
| Sport | Entertainment | −399.7862 | 354.55697 | 0.920 | −1445.9433 | 646.3710 | |
| Travel and Event | 6.9019 | 354.44770 | 1.000 | −1038.9328 | 1052.7366 | ||
| People and Blog | −34.5987 | 357.94917 | 1.000 | −1090.7649 | 1021.5675 | ||
| News and Politics | −505.9868 | 369.21298 | 0.818 | −1595.3880 | 583.4144 | ||
| Education and Science | −78.4840 | 378.22838 | 1.000 | −1194.4861 | 1037.5181 | ||
| Others | −86.8775 | 481.59095 | 1.000 | −1507.8616 | 1334.1065 | ||
| Travel and Event | Entertainment | −406.6881 * | 119.88337 | 0.012 | −760.4164 | −52.9598 | |
| Sport | −6.9019 | 354.44770 | 1.000 | −1052.7366 | 1038.9328 | ||
| People and Blog | −41.5006 | 129.57231 | 1.000 | −423.8172 | 340.8159 | ||
| News and Politics | −512.8887 * | 158.04935 | 0.020 | −979.2297 | −46.5477 | ||
| Education and Science | −85.3859 | 178.09571 | 0.999 | −610.8757 | 440.1040 | ||
| Others | −93.7794 | 347.26246 | 1.000 | −1118.4134 | 930.8545 | ||
| People and Blog | Entertainment | −365.1875 | 129.87093 | 0.074 | −748.3851 | 18.0102 | |
| Sport | 34.5987 | 357.94917 | 1.000 | −1021.5675 | 1090.7649 | ||
| Travel and Event | 41.5006 | 129.57231 | 1.000 | −340.8159 | 423.8172 | ||
| News and Politics | −471.3881 | 165.75293 | 0.067 | −960.4593 | 17.6832 | ||
| Education and Science | −43.8853 | 184.96626 | 1.000 | −589.6474 | 501.8769 | ||
| Others | −52.2788 | 350.83565 | 1.000 | −1087.4558 | 982.8982 | ||
| News and Politics | Entertainment | 106.2006 | 158.29427 | 0.994 | −360.8631 | 573.2643 | |
| Sport | 505.9868 | 369.21298 | 0.818 | −583.4144 | 1595.3880 | ||
| Travel and Event | 512.8887 * | 158.04935 | 0.020 | 46.5477 | 979.2297 | ||
| People and Blog | 471.3881 | 165.75293 | 0.067 | −17.6832 | 960.4593 | ||
| Education and Science | 427.5028 | 205.92021 | 0.367 | −180.0862 | 1035.0918 | ||
| Others | 419.1092 | 362.32067 | 0.910 | −649.9555 | 1488.1740 | ||
| Education and Science | Entertainment | −321.3022 | 178.31309 | 0.547 | −847.4335 | 204.8291 | |
| Sport | 78.4840 | 378.22838 | 1.000 | −1037.5181 | 1194.4861 | ||
| Travel and Event | 85.3859 | 178.09571 | 0.999 | −440.1040 | 610.8757 | ||
| People and Blog | 43.8853 | 184.96626 | 1.000 | −501.8769 | 589.6474 | ||
| News and Politics | −427.5028 | 205.92021 | 0.367 | −1035.0918 | 180.0862 | ||
| Others | −8.3936 | 371.50337 | 1.000 | −1104.5528 | 1087.7657 | ||
| Others | Entertainment | −312.9086 | 347.37400 | 0.973 | −1337.8717 | 712.0544 | |
| Sport | 86.8775 | 481.59095 | 1.000 | −1334.1065 | 1507.8616 | ||
| Travel and Event | 93.7794 | 347.26246 | 1.000 | −930.8545 | 1118.4134 | ||
| People and Blog | 52.2788 | 350.83565 | 1.000 | −982.8982 | 1,087.4558 | ||
| News and Politics | −419.1092 | 362.32067 | 0.910 | −1488.1740 | 649.9555 | ||
| Education and Science | 8.3936 | 371.50337 | 1.000 | −1,087.7657 | 1,104.5528 | ||
Based on the observed means. The error term is mean square (error) = 5331865.410. * The mean difference was significant at the 0.05 level.
Graph metrics of the video commentors on YouTube.
| Graph Type | Directed |
|---|---|
| Vertices | 22,677 |
| Unique Edges | 20,867 |
| Edges With Duplicates | 4367 |
| Total Edges | 25,234 |
| Self-Loops | 108 |
| Reciprocated Vertex Pair Ratio | 4.45474 × 10−5 |
| Reciprocated Edge Ratio | 8.90908 × 10−5 |
| Connected Components | 361 |
| Single-Vertex Connected Components | 12 |
| Maximum Vertices in a Connected Component | 15,492 |
| Maximum Edges in a Connected Component | 17,381 |
| Maximum Geodesic Distance (Diameter) | 30 |
| Average Geodesic Distance | 10.042648 |
| Graph Density | 4.36561 × 10−5 |
| Modularity | 0.875765 |
The 10 meaningful contextual words.
| Keywords in |
Contextual Words | Number of Times That the Contextual Words Appear on the Left-Hand Side of the French Keywords | Number of Times That the Contextual Words Appear on the Right-Hand Side of the French Keywords | Total Frequency | Correlation Score between French Keywords |
|---|---|---|---|---|---|
| Merci (Thanks) | Beaucoup (a lot) | 19 | 166 | 185 | 163.550 |
| Vidéo (video) | 104 | 186 | 290 | 105.467 | |
| Avoir (have) | 79 | 131 | 210 | 80.983 | |
| Seigneur (lord) | 22 | 76 | 98 | 73.9983 | |
| Amen (amen) | 63 | 20 | 83 | 42.550 | |
| Infiniment (infinitively) | 3 | 43 | 46 | 42.167 | |
| Grand (great or big) | 45 | 5 | 50 | 41.867 | |
| Bon (good) | 60 | 53 | 113 | 41.067 | |
| Beau (beautiful) | 39 | 76 | 115 | 39/050 | |
| Chef (Chief) | 11 | 39 | 50 | 37.893 | |
| Vidéo (video) | Super (super) | 120 | 21 | 151 | 121.833 |
| Merci (thanks) | 187 | 104 | 291 | 105.667 | |
| Avoir (have) | 118 | 143 | 261 | 81.383 | |
| Bon (good) | 79 | 27 | 106 | 74.283 | |
| Beau (beautiful) | 75 | 15 | 90 | 65.583 | |
| Faire (make or do) | 86 | 47 | 133 | 49.367 | |
| Superbe (superb) | 45 | 11 | 56 | 47.183 | |
| Excellent (excellent) | 35 | 10 | 45 | 35.187 | |
| Voir (watch or see) | 47 | 17 | 64 | 23.300 | |
| Adorer (adore) | 42 | 16 | 58 | 23.050 | |
| Pouvoir (can) | Avoir (have) | 136 | 65 | 201 | 131.117 |
| Faire (make or do) | 18 | 133 | 151 | 102.200 | |
| Dire (say or tell) | 12 | 59 | 71 | 41.633 | |
| Voir (watch or see) | 13 | 44 | 57 | 33.517 | |
| Aller (go) | 13 | 23 | 36 | 20.267 | |
| Mettre (put) | 4 | 22 | 25 | 17.983 | |
| Vidéo (video) | 16 | 29 | 45 | 13.983 | |
| Regarder (watch or look) | 6 | 14 | 20 | 12.900 | |
| Trouver (find) | 1 | 18 | 19 | 12.417 | |
| Parler (talk or speak) | 5 | 13 | 18 | 10.350 | |
| Bon (good) | Très (very) | 200 | 26 | 226 | 184.333 |
| Vidéo (video) | 30 | 78 | 108 | 75.483 | |
| Film (film) | 18 | 64 | 82 | 63.050 | |
| Continuation (continuation) | 3 | 48 | 51 | 48.522 | |
| Merci (thanks) | 54 | 62 | 116 | 41.933 | |
| Humeur (humour) | 0 | 41 | 41 | 41.000 | |
| Journée (day or daytime) | 2 | 39 | 41 | 39.083 | |
| Courage (courage) | 5 | 38 | 43 | 37.317 | |
| Moment (moment) | 2 | 26 | 29 | 25.483 | |
| Travail (job or work) | 3 | 27 | 30 | 25.017 | |
| Beau (beautiful) | Très (very) | 149 | 22 | 171 | 143.533 |
| Vidéo (video) | 14 | 76 | 90 | 65.533 | |
| Trop (too) | 45 | 4 | 49 | 40.550 | |
| Merci (thanks) | 76 | 41 | 117 | 39.450 | |
| Film (film) | 10 | 17 | 27 | 18.600 | |
| Image (image or photo or picture) | 2 | 19 | 21 | 18.167 | |
| Français (French) | 3 | 17 | 20 | 17.283 | |
| Pays (country) | 2 | 16 | 18 | 15.783 | |
| Histoire (history) | 9 | 15 | 24 | 14.917 | |
| Super (super) | 16 | 7 | 23 | 14.876 | |
| Aimer (love) | Bien (well) | 23 | 64 | 87 | 81.050 |
| Beaucoup (much) | 15 | 53 | 68 | 63.400 | |
| Vidéo (video) | 15 | 28 | 43 | 13.633 | |
| Film (film) | 7 | 25 | 32 | 11.950 | |
| Savoir (know) | 3 | 13 | 16 | 10.983 | |
| Voir (watch or see) | 3 | 13 | 16 | 7.783 | |
| Merci (thanks) | 8 | 20 | 28 | 9.317 | |
| Aller (go) | 5 | 9 | 14 | 6.733 | |
| Histoire (history) | 4 | 15 | 19 | 6.540 | |
| Chine (China) | 3 | 10 | 13 | 4.533 | |
| Film (film) | Bon (good) | 64 | 17 | 81 | 62.800 |
| Voir (watch) | 16 | 46 | 62 | 25.167 | |
| Beau (beautiful) | 17 | 10 | 27 | 18.600 | |
| Super (super) | 18 | 6 | 24 | 16.183 | |
| Merci (thanks) | 11 | 19 | 30 | 14.883 | |
| Aimer (love) | 25 | 7 | 32 | 11.950 | |
| Regarder (watch) | 21 | 5 | 26 | 11.333 | |
| Adorer (adore) | 18 | 9 | 27 | 10.250 | |
| Meilleur (better) | 13 | 10 | 23 | 9.433 | |
| Magnifique (magnificent) | 15 | 5 | 20 | 9.350 | |
| Chine (China) | Muraille (wall) | 64 | 1 | 65 | 32.083 |
| Aller (go) | 21 | 7 | 28 | 11.850 | |
| Voir | 11 | 10 | 21 | 8.533 | |
| Grand (great or big) | 16 | 5 | 21 | 7.017 | |
| Vif (live or vivid or bright) | 8 | 3 | 11 | 5.700 | |
| Histoire (history) | 15 | 2 | 17 | 5.633 | |
| Bon (good) | 2 | 11 | 13 | 5.317 | |
| Merci (thanks) | 1 | 12 | 13 | 4.767 | |
| Pays (country) | 6 | 10 | 16 | 4.717 | |
| Aimer (louve) | 10 | 3 | 13 | 4.533 | |
| Super (super) | Vidéo (video) | 30 | 116 | 146 | 116.833 |
| Intéressant (interesting) | 3 | 45 | 48 | 42.000 | |
| Merci (thanks) | 30 | 48 | 78 | 32.050 | |
| Bien (well) | 16 | 19 | 35 | 18.933 | |
| Cool (cool) | 3 | 17 | 20 | 18.400 | |
| Film (film) | 6 | 18 | 24 | 16.183 | |
| Beau (beautiful) | 7 | 16 | 23 | 14.867 | |
| Bon (good) | 14 | 14 | 28 | 14.217 | |
| Episode (episode) | 2 | 12 | 14 | 12.583 | |
| Format (format) | 5 | 13 | 18 | 11.750 | |
| Grand (great or big) | Muraille (wall) | 0 | 59 | 59 | 56.200 |
| Merci (thanks) | 6 | 49 | 55 | 46.867 | |
| Chose (thing) | 1 | 13 | 14 | 13.200 | |
| Bravo (bravo) | 3 | 14 | 17 | 12.900 | |
| Pays (country) | 3 | 15 | 18 | 11.783 | |
| Chine (China) | 5 | 16 | 21 | 7.017 | |
| Ville (city) | 3 | 7 | 10 | 6.783 | |
| Homme (people or man) | 1 | 6 | 7 | 6.250 | |
| Maître (master) | 1 | 6 | 7 | 6.200 | |
| Beau (beautiful) | 5 | 8 | 13 | 5.183 |
Appendix A. Detail of KWIC
Detail of KWIC.
| Keyword in |
Contextual Words (L = Count on the Left of Keyword, R = Count on the Right of Keyword) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Contextual Words | L5 | L4 | L3 | L2 | L1 | R1 | R2 | R3 | R4 | R5 | Score | Count | |
| Merci (Thanks) | Beaucoup (a lot) | 3 | 8 | 3 | 4 | 1 | 154 | 1 | 0 | 5 | 6 | 163.550 | 185 |
| Vidéo (video) | 18 | 15 | 16 | 38 | 17 | 2 | 2 | 112 | 49 | 21 | 105.467 | 290 | |
| Avoir (have) | 30 | 19 | 20 | 10 | 0 | 30 | 9 | 32 | 28 | 32 | 80.983 | 210 | |
| Seigneur (lord) | 1 | 3 | 7 | 6 | 5 | 56 | 4 | 9 | 6 | 1 | 73.983 | 98 | |
| Amen (amen) | 3 | 9 | 11 | 24 | 16 | 2 | 7 | 1 | 4 | 6 | 42.550 | 83 | |
| Infiniment(infinitively) | 0 | 1 | 0 | 0 | 2 | 38 | 2 | 2 | 1 | 0 | 42.176 | 46 | |
| Grand(great or big) | 4 | 0 | 3 | 0 | 38 | 1 | 0 | 2 | 0 | 2 | 41.867 | 50 | |
| Bon (good) | 17 | 12 | 18 | 10 | 3 | 7 | 7 | 14 | 10 | 15 | 41.074 | 113 | |
| Beau (beautiful) | 7 | 6 | 12 | 11 | 3 | 2 | 4 | 33 | 25 | 12 | 39.050 | 115 | |
| Chef (Chief) | 1 | 1 | 1 | 6 | 2 | 29 | 2 | 3 | 4 | 1 | 37.983 | 50 | |
| Vidéo (video) | Super (super) | 5 | 3 | 5 | 8 | 99 | 5 | 15 | 5 | 1 | 5 | 121.833 | 151 |
| Merci (thanks) | 22 | 49 | 112 | 2 | 2 | 17 | 38 | 16 | 15 | 18 | 105.667 | 291 | |
| Avoir (have) | 41 | 27 | 43 | 4 | 3 | 10 | 11 | 36 | 48 | 38 | 81.383 | 261 | |
| Bon (good) | 4 | 10 | 3 | 2 | 60 | 0 | 8 | 7 | 5 | 7 | 74.283 | 106 | |
| Beau (beautiful) | 8 | 5 | 4 | 3 | 55 | 0 | 3 | 6 | 4 | 2 | 65.583 | 90 | |
| Faire (make or do) | 12 | 11 | 24 | 39 | 0 | 2 | 11 | 14 | 11 | 9 | 49.367 | 133 | |
| Superbe (superb) | 2 | 1 | 0 | 2 | 40 | 2 | 5 | 1 | 2 | 1 | 47.183 | 56 | |
| Excellent (excellent) | 0 | 1 | 4 | 1 | 29 | 1 | 6 | 1 | 0 | 2 | 35.187 | 45 | |
| Voir (watch) | 4 | 6 | 14 | 23 | 0 | 0 | 4 | 1 | 2 | 10 | 23.300 | 64 | |
| Adorer (adore) | 2 | 4 | 3 | 33 | 0 | 0 | 0 | 6 | 3 | 7 | 23.050 | 58 | |
| Pouvoir (can) | Avoir (have) | 23 | 10 | 7 | 26 | 70 | 26 | 12 | 7 | 7 | 13 | 131.117 | 201 |
| Faire (make or do) | 9 | 2 | 3 | 3 | 1 | 68 | 44 | 15 | 4 | 2 | 102.200 | 151 | |
| Dire (say or tell) | 4 | 6 | 2 | 0 | 0 | 24 | 19 | 14 | 2 | 0 | 41.633 | 71 | |
| Voir (watch or see) | 7 | 3 | 2 | 1 | 0 | 20 | 14 | 9 | 0 | 1 | 33.517 | 57 | |
| Aller (go) | 3 | 2 | 1 | 2 | 5 | 9 | 1 | 4 | 4 | 5 | 20.267 | 36 | |
| Mettre (put) | 2 | 1 | 0 | 1 | 0 | 14 | 3 | 4 | 0 | 0 | 17.983 | 25 | |
| Vidéo (video) | 4 | 1 | 4 | 7 | 0 | 0 | 1 | 12 | 8 | 8 | 13.983 | 45 | |
| Regarder (watch or look) | 2 | 0 | 4 | 0 | 0 | 9 | 3 | 2 | 0 | 0 | 12.900 | 20 | |
| Trouver (find) | 0 | 0 | 1 | 0 | 0 | 7 | 9 | 1 | 1 | 0 | 12.417 | 19 | |
| Parler (talk or speak) | 2 | 1 | 1 | 1 | 0 | 6 | 4 | 2 | 0 | 1 | 10.350 | 18 | |
| Bon (good) | Très (very) | 12 | 8 | 7 | 7 | 166 | 1 | 2 | 9 | 6 | 8 | 184.333 | 226 |
| Vidéo (video) | 8 | 5 | 8 | 8 | 1 | 60 | 2 | 2 | 10 | 4 | 75.483 | 108 | |
| Film (film) | 5 | 6 | 4 | 2 | 1 | 54 | 3 | 2 | 1 | 4 | 63.050 | 82 | |
| Continuation (continuation) | 0 | 0 | 1 | 2 | 0 | 47 | 0 | 0 | 0 | 1 | 48.522 | 51 | |
| Merci (thanks) | 15 | 10 | 15 | 7 | 7 | 3 | 10 | 19 | 12 | 18 | 41.933 | 116 | |
| Humeur (humour) | 0 | 0 | 0 | 0 | 0 | 41 | 0 | 0 | 0 | 0 | 41.000 | 41 | |
| Journée (day or daytime) | 0 | 1 | 0 | 1 | 0 | 38 | 0 | 1 | 0 | 0 | 39.083 | 41 | |
| Courage (courage) | 1 | 0 | 1 | 2 | 1 | 33 | 2 | 1 | 1 | 1 | 37.317 | 43 | |
| Moment (moment) | 1 | 1 | 0 | 0 | 1 | 23 | 1 | 1 | 0 | 1 | 25.483 | 29 | |
| Travail (job or work) | 2 | 0 | 1 | 0 | 0 | 23 | 1 | 1 | 1 | 1 | 25.017 | 30 | |
| Beau (beautiful) | Très (very) | 6 | 5 | 6 | 2 | 130 | 1 | 6 | 7 | 3 | 5 | 143.533 | 171 |
| Vidéo (video) | 2 | 3 | 6 | 3 | 0 | 55 | 3 | 4 | 5 | 9 | 65.533 | 90 | |
| Trop (too) | 2 | 3 | 2 | 2 | 36 | 1 | 0 | 1 | 0 | 2 | 40.550 | 49 | |
| Merci (thanks) | 12 | 25 | 33 | 4 | 2 | 3 | 11 | 12 | 6 | 9 | 39.450 | 117 | |
| Film (film) | 3 | 1 | 5 | 1 | 0 | 15 | 0 | 1 | 1 | 0 | 18.600 | 27 | |
| Image (image or photo or picture) | 0 | 0 | 1 | 0 | 1 | 16 | 0 | 1 | 2 | 0 | 18.167 | 21 | |
| Français (French) | 1 | 1 | 1 | 0 | 0 | 16 | 1 | 0 | 0 | 0 | 17.283 | 20 | |
| Pays (country) | 0 | 1 | 0 | 0 | 1 | 14 | 0 | 1 | 0 | 1 | 15.783 | 18 | |
| Histoire (history) | 4 | 1 | 1 | 3 | 0 | 11 | 0 | 1 | 2 | 1 | 14.917 | 24 | |
| Super (super) | 2 | 1 | 1 | 1 | 11 | 1 | 0 | 1 | 1 | 4 | 14.876 | 23 | |
| Aimer (love) | Bien (well) | 4 | 1 | 3 | 0 | 15 | 64 | 0 | 0 | 0 | 0 | 81.050 | 87 |
| Beaucoup (much) | 0 | 0 | 0 | 0 | 15 | 45 | 6 | 0 | 0 | 2 | 63.400 | 68 | |
| Vidéo (video) | 5 | 6 | 4 | 0 | 0 | 0 | 9 | 9 | 6 | 4 | 13.633 | 43 | |
| Film (film) | 4 | 1 | 2 | 0 | 0 | 1 | 10 | 10 | 2 | 2 | 11.950 | 32 | |
| Savoir (know) | 2 | 0 | 1 | 0 | 0 | 8 | 4 | 0 | 1 | 0 | 10.983 | 16 | |
| Voir (watch or see) | 1 | 2 | 0 | 0 | 0 | 6 | 5 | 1 | 1 | 0 | 7.783 | 16 | |
| Merci (thanks) | 2 | 3 | 3 | 0 | 0 | 2 | 2 | 5 | 6 | 5 | 9.317 | 28 | |
| Aller (go) | 1 | 0 | 0 | 2 | 2 | 1 | 1 | 4 | 2 | 1 | 6.733 | 14 | |
| Histoire (history) | 1 | 1 | 2 | 0 | 0 | 0 | 3 | 10 | 2 | 0 | 6.540 | 19 | |
| Chine (China) | 0 | 1 | 2 | 0 | 0 | 0 | 4 | 2 | 3 | 1 | 4.533 | 13 | |
| Film (film) | Bon (good) | 4 | 1 | 2 | 3 | 54 | 1 | 2 | 4 | 5 | 5 | 62.800 | 81 |
| Voir (watch) | 2 | 3 | 7 | 34 | 0 | 0 | 4 | 1 | 3 | 8 | 25.167 | 62 | |
| Beau (beautiful) | 0 | 1 | 1 | 0 | 15 | 0 | 1 | 5 | 1 | 3 | 18.600 | 27 | |
| Super (super) | 2 | 3 | 0 | 3 | 10 | 2 | 2 | 1 | 0 | 1 | 16.183 | 24 | |
| Merci (thanks) | 2 | 1 | 8 | 0 | 0 | 8 | 3 | 2 | 4 | 2 | 14.883 | 30 | |
| Aimer (love) | 2 | 2 | 10 | 10 | 1 | 0 | 0 | 2 | 1 | 4 | 11.950 | 32 | |
| Regarder (watch) | 1 | 0 | 0 | 20 | 0 | 0 | 0 | 1 | 0 | 4 | 11.333 | 26 | |
| Adorer (adore) | 2 | 2 | 1 | 13 | 0 | 0 | 1 | 2 | 3 | 3 | 10.250 | 27 | |
| Meilleur (better) | 2 | 0 | 1 | 0 | 7 | 1 | 1 | 0 | 0 | 1 | 9.433 | 13 | |
| Magnifique (magnificent) | 1 | 1 | 0 | 0 | 3 | 4 | 2 | 0 | 2 | 2 | 9.350 | 15 | |
| Chine (China) | Muraille (wall) | 0 | 0 | 1 | 63 | 0 | 0 | 0 | 0 | 1 | 0 | 32.083 | 65 |
| Aller (go) | 2 | 3 | 3 | 13 | 0 | 2 | 0 | 0 | 4 | 1 | 11.850 | 28 | |
| Voir (watch or see) | 0 | 4 | 4 | 3 | 0 | 2 | 2 | 3 | 2 | 1 | 8.533 | 21 | |
| Grand (great or big) | 1 | 3 | 11 | 0 | 1 | 0 | 0 | 3 | 0 | 2 | 7.017 | 21 | |
| Vif (live or vivid or bright) | 1 | 0 | 0 | 7 | 0 | 1 | 2 | 0 | 0 | 0 | 5.700 | 11 | |
| Histoire (history) | 3 | 0 | 9 | 3 | 0 | 0 | 0 | 1 | 0 | 1 | 5.633 | 17 | |
| Bon (good) | 0 | 2 | 0 | 0 | 0 | 2 | 2 | 2 | 3 | 2 | 5.317 | 13 | |
| Merci (thanks) | 0 | 1 | 0 | 0 | 0 | 0 | 6 | 2 | 1 | 3 | 4.767 | 13 | |
| Pays (country) | 1 | 1 | 4 | 0 | 0 | 0 | 1 | 4 | 2 | 3 | 4.717 | 16 | |
| Aimer (love) | 1 | 3 | 2 | 4 | 0 | 0 | 0 | 2 | 1 | 0 | 4.533 | 13 | |
| Super (super) | Vidéo (video) | 5 | 1 | 5 | 15 | 4 | 95 | 8 | 5 | 3 | 5 | 116.833 | 146 |
| Intéressant (interesting) | 1 | 0 | 0 | 1 | 1 | 39 | 0 | 0 | 2 | 4 | 42.000 | 48 | |
| Merci (thanks) | 2 | 3 | 15 | 8 | 2 | 5 | 13 | 15 | 8 | 7 | 32.050 | 78 | |
| Bien (well) | 6 | 4 | 5 | 1 | 0 | 13 | 0 | 2 | 2 | 2 | 18.933 | 35 | |
| Cool (cool) | 1 | 0 | 0 | 0 | 2 | 16 | 0 | 0 | 0 | 1 | 18.400 | 20 | |
| Film (film) | 1 | 0 | 1 | 2 | 2 | 10 | 3 | 0 | 3 | 2 | 16.183 | 24 | |
| Beau (beautiful) | 4 | 1 | 1 | 0 | 1 | 11 | 1 | 1 | 1 | 2 | 14.867 | 23 | |
| Bon (good) | 2 | 3 | 4 | 4 | 1 | 6 | 3 | 1 | 2 | 2 | 14.217 | 28 | |
| Episode (episode) | 0 | 1 | 0 | 0 | 1 | 11 | 0 | 1 | 0 | 0 | 12.583 | 14 | |
| Format (format) | 0 | 1 | 1 | 2 | 1 | 7 | 2 | 2 | 2 | 0 | 11.750 | 18 | |
| Grand (great or big) | Muraille (wall) | 0 | 0 | 0 | 0 | 0 | 55 | 1 | 0 | 2 | 1 | 56.200 | 59 |
| Merci (thanks) | 2 | 0 | 2 | 0 | 2 | 42 | 0 | 3 | 0 | 4 | 46.867 | 55 | |
| Chose (thing) | 1 | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 13.200 | 14 | |
| Bravo (bravo) | 1 | 0 | 2 | 0 | 0 | 11 | 1 | 1 | 0 | 1 | 12.900 | 17 | |
| Pays (country) | 0 | 0 | 3 | 0 | 0 | 9 | 1 | 1 | 3 | 1 | 11.783 | 18 | |
| Chine (China) | 2 | 0 | 3 | 0 | 0 | 1 | 0 | 11 | 3 | 1 | 7.017 | 21 | |
| Ville (city) | 1 | 1 | 0 | 1 | 0 | 5 | 1 | 1 | 0 | 0 | 6.783 | 10 | |
| Homme (people or man) | 0 | 1 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 6.250 | 7 | |
| Maître (master) | 1 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 6.200 | 7 | |
| Beau (beautiful) | 2 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 4 | 1 | 5.183 | 13 | |
Appendix B. Glossary of Terms
Glossary of Terms.
| Term | Definition | Reference |
|---|---|---|
| Architectural heritage | Immovable cultural heritage, including historical buildings, monuments, and archaeological sites | UNESCO [ |
| Destination image | The set of beliefs, ideas, and impressions that people have of a destination of place | Baloglu and McCleary [ |
| Cultivation theory | How a much more comprehensive range of messages gradually influence the public, as they are exposed to media messages daily | Potter [ |
| Open APIs (open application programming interfaces) | Open APIs promote interoperability via the provision of the data-sharing tools required to create widely used web apps, enable seamless social media service integration, and give rise to developer ecosystems that are mutually beneficial and build on top of social media platforms | Bodle [ |
| NodeXL | An open-source graph visualisation tool that supports social network structure analysis from Microsoft | NodeXL [ |
| Clauset–Newman–Moore algorithm | A widely used algorithm for analysing large networks and community classification | Vieira, Xavier, Ebecken, and Evsukoff [ |
| Harel–Koren fast multiscale layout algorithm | Algorithm enables both multiscale graph representation and locally good layout | Yao, Li, Song, and Crabbe [ |
| Nodes (i.e., vertices) | A social structure or content, virtual physical location, event, or individual, for instance, an institution, organisation, or country | Hansen, Shneiderman, and Smith [ |
| Edge(s) | Link or connection that occurs when two vertices collaborate or exchange information | NodeXL [ |
| Average geodesic distance | Average number of paths that one node reaches the others | NodeXL [ |
| Modularity | Index that determines the fitness of the groups in a network | NodeXL [ |
| KWIC (keyword in context) | Extracting co-occurrence words from the text | Ängsal, Brodén, Fridlund, Olsson, and Öhberg [ |
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; Wareewanich, Thitinant 1 1 Chakrabongse Bhuvanarth International Institute for Interdisciplinary Studies, Rajamangala University of Technology Tawan-Ok, Bangkok 10400, Thailand
2 Sustainable Real Estate Research Center, Department of Economics and Finance, Hong Kong Shue Yan University, Hong Kong 999077, China