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Abstract

This article explores festival attendees’ experiences on social media. Data on festival attendees’ posts on the Guangzhou International Light Festival were collected from TikTok. The Jieba Chinese word segmentation tool was applied to tokenize the texts and then classify the high-frequency words and phrases into five dimensions of festival experiences. The five dimensions of festival attendees’ experiences are cognitive, novel, affective, physical participation, and social interaction. A combination of the modularity algorithm and Gephi was used to cluster and visualize the festival attendees’ overall experiences and the different experiences in terms of gender and age. The results indicate that, in festival attendees’ overall experience, cognitive experience accounted for the largest proportion, followed by physical participation, and the proportion of social interaction, emotional experience, and novel experience was less. Moreover, the demographic characteristics of gender and age differ in the festival experience.

Keywords

social media festival experience TikTok modularity algorithm

Introduction

With the development of Web 2.0 technology, the growth of social media has become a global phenomenon (Li, Teng, et al., 2020). Now, social media permeates all aspects of life (MacKay et al., 2017), and it has become a participatory platform that enables consumers to communicate directly with others and share their daily life through various forms of posting and messaging (D. Leung et al., 2013; Xiang & Gretzel, 2010). As an information-intense industry, tourism is heavily influenced by social media, which has fundamentally changed the way tourism-related information is distributed and how people plan for travel (Xiang & Gretzel, 2010). Social media plays an increasingly important role in tourism (Xiang & Gretzel, 2010). Rahman et al. (2022) argued that social media is essential not only for understanding tourism but also for the tourism industry’s success. Research on social media in tourism has become an emerging topic (Zeng & Gerritsen, 2014). A literature review on social media in tourism has been published recently (Zeng & Gerritsen, 2014). A total of 130 studies published in academic journals in 2013 were included in this review. However, most social media studies solely focus on tourism destinations and hospitality, and pay little attention to the festival (D. Leung et al., 2013; MacKay et al., 2017; Park et al., 2020; Xiang et al., 2017; Yan et al., 2020).

Getz (2008) pointed out that festival is an important segment of the tourism industry, providing a unique tourism attraction for tourists. Because of the contribution to promoting the economic, social, and cultural developments of the destination (Li, Dai, et al., 2020), festivals have grown enormously in recent years (Geus et al., 2016). Robertson et al. (2007) regard events and festivals as prime manifestations of the experience economy. Festivals are experiential products like other tourism products and services (Grunwell et al., 2007). For festival attendees, unique and memorable festival experiences are an important part of their lives (Geus et al., 2016). Results by Folgado-Fernández et al.(2021) revealed that events participants’ rational and emotional engagement depends on the type of events and that events provide an opportunity for participants to reflect on what they have learned and discuss their experiences with others after the events. For festival organizers, understanding the experience of festival attendees is imperative because their experience is a predictor of their future behavior (Manthiou et al., 2014). Therefore, insights into festival experiences can help festivals better serve their intended target audience (Robertson et al., 2007). Although festival experiences have attracted much attention from researchers (Geus et al., 2016; H. Lee et al., 2017; Manthiou et al., 2014; Morgan, 2008; Packer & Ballantyne, 2010), the literature on festival experiences in social media is scarce and fragmented. This study attempted to bridge this gap.

According to the China Internet Information Center (CNNIC, http://www.cnnic.net.cn/), the number of consumers in China has reached 1,007 million of which 944 million are short-video consumers by June 2021. Launched in September 2016, the TikTok short-video app, which has become the most popular short-video platform in China, was rapidly developed because of its rich and novel content. It was the world’s second-most downloaded app in 2019 (Medina Serrano et al., 2020). By August 2020, TikTok had over 600 million active users daily (https://www.bytedance.com). These include some influential government administration media, such as CCTV News, The People’s Daily, and the Central Committee of the Communist Youth League, and include other official travel accounts to promote tourism destinations and tourism products. The Guangzhou International Light Festival (hereinafter “GZILF”) organizations also chose it, but not Sina Weibo, which is China’s biggest Twitter-like microblogging platform, to publish festival activity information after 2016. Despite the rapid growth of TikTok and the richness and diversity of consumers’ content, research on TikTok appears to be neglected. Therefore, this study attempts to explore festival attendees’ experiences on social media using TikTok data.

Demographic variables, such as gender and age, are also emphasized in this study. Social role theory posits that gender differences in social behavior lead to different reactions in the social context (Eagly, 2013). Many studies have researched the relationship between demographic profiles and tourist experiences (Alexander, 2012; Squire, 1994; Yang et al., 2017; Yfantidou, 2008). For example, Yang et al. (2017) conducted a systematic literature review on risk and gender in tourism experiences. Alexander (2012) explored the influence of age, gender, and other factors on the volunteer tourism experience. Yfantidou (2008) examined the tourist differences between men and women of varying ages in Greece, and the comparison between gender and age revealed more similarities than differences. Similarly, some researchers have examined the relationship between individuals’ sociodemographic characteristics and festival experiences. Ballantyne et al. (2014) revealed that gender and age had a small but significant effect on the festival experience at the Woodford Folk Festival in Queensland. They found that females had a stronger, more positive experience than males, and under-30s had a stronger, more positive experience than over-30s. Similarly, Brown and Sharpley (2019) revealed that festival-goers’ age and gender had an important effect on their festival experience. Compared with males, festival experience attributes are more important to females, and for younger festival-goers, most attributes of the festival experience are important. Results by Ahn et al. (2020) also showed that male and female attendees focus on different aspects of their festival experiences; male attendees may emphasize the perceived value of the festival, while females are inclined to share positive reviews about the festival on the Internet. Therefore, researchers have noticed that individuals’ demographic profiles have different effects on festival experiences. However, it is still unclear whether or how customer demographic variables (gender and age) evaluate their experiences differently at festivals, especially on social media. Hence, the objectives of the study are as follows: (1) to identify the dimensions of festival attendees’ experiences on social media and (2) to explore the differences in attendees’ gender and age. To meet the research objective, first, we collected consumers’ posted texts about the Guangzhou International Light Festival on TikTok. By using the Jibe to tokenize the posted texts, we extract high-frequency words or phrases to divide them into dimensions of festival experiences. Second, we combined the modularity algorithm and Gephi to analyze and visualize the attendees’ overall festival experiences and compare the different festival experiences in terms of attendees’ gender and age. This can help festival organizers by indicating how to design and manage the content and format of their events to provide a memorable experience for festival attendees.

Literature Review

Festival Experiences

Pine and Gilmore (1999) argued that the central character of the present economy is experiencing and that the experience economy is the final phase of economic progression. As Richards (1999) reflected, individuals have focused on consumption away from physical goods toward services and experiences, which means that the quality of life is increasingly judged in terms of access to those experiences. Successful experiences are described as anything that consumers find unique and unforgettable (K.-Y. Lee & Lee, 2019). Larsen (2007) reviewed tourism and general psychological literature and concluded that “a tourist experience is a past personal travel-related event strong enough to have entered long-term memory.”Tinsley et al. (1993) revealed memorable and meaningful experiences that can bring enjoyment, companionship, novelty, relaxation, aesthetic appreciation, and intimacy. However, researchers often ignore feedback that can occur between memory, experience, and future behavior (Geus et al., 2016).

Festivals provide space and time away from everyday life for festival attendees, where exceptional experiences can be created and shared (Morgan, 2008). Getz and Page (2016) pointed out that the essence of the festival “is intended to create, or at least shape, the individual and collective experiences of the audience or participants.” The festival experience is the core phenomenon of festival studies (Getz, 2010). Geus et al. (2016) regarded event experiences as a process and explained that “when certain conditions are met, a (multi-phased and multi-influential) experience can occur, resulting in multiple outcomes.” This experience has cognitive, cognitive, and affective components (Geus et al., 2016). For most people, we attend the event just to “have fun” or “entertainment. This experience can occur at any event, and we call this experience “generic experiences” (Donald, 2007).

Studies on the festival experience do seem to have widespread agreement that the festival experience is multidimensional (Brown et al., 2020; H. Lee et al., 2017). Cole and Illum (2006) examined 413 visitors who attended a rural heritage festival in Missouri. The survey showed that the festival experience comprised three dimensions: history appreciation, socialization, and enjoyment. Morgan (2008) explored the nature of extraordinary experiences at the 2005 Sidmouth Folk Festival and found that festival attendees evaluate their experience based on the main external and internal elements of the festival, namely design and programming, physical organization, social interaction, personal benefits, symbolic meanings, and cultural communication. Packer and Ballantyne (2010) identified four facets of music festival attendees’ experiences: music as common ground, festival atmosphere, social experience, and separation from the everyday. These four facets have an impact on the psychological, social, and well-being of festive attendees. Manthiou et al. (2014) adopted an online survey completed by 338 attendees of the VEISHEA festival, which identified four dimensions of festival attendees’ experiences, including education, entertainment, aesthetics, and escapism, using the experience economy concept of Pine and Gilmore (1998). H. Lee et al. (2017) proposed that festival attendees’ experiences could be classified into five main attributes: escape, playfulness, togetherness, sacredness, and placeness. The results were derived from the theoretical literature and tested through a survey of 450 South Koreans who had participated in festivals. Geus et al. (2016) developed an event experience scale that generated an 18-item scale comprising four dimensions: affective engagement, cognitive engagement, physical engagement, and experiencing newness. Although some studies have been conducted on festival experiences, there is a lack of consensus. Therefore, it is worth exploring the experiences of festival attendees on social media platforms.

Social Media and Festival Experiences

Social media are “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, which allow the creation and exchange of user-generated content” (Kaplan & Haenlein, 2010). It differs from the “mainstream media” in that anyone can “post,”“tag,”“dig,” or “blog,” and so forth on the Internet (Xiang & Gretzel, 2010). The content generated by these social media platforms includes abundant information created by consumers (Blackshaw, 2004; Mangold & Faulds, 2009). The benefits of participating in social media can increase consumers’ social capital and sense of participation (Ellison et al., 2007).

Festivals bring people together to share a common experience (MacKay et al., 2017), and the use of social media allows consumers to create, comment, and add to the content. It can instantly take the form of text, audio, video, images, and communities about the festival (Gyimóthy & Larson, 2015; Pozdnoukhov & Kaiser, 2011). Social media also provides a perfect environment for customers to share festival experiences (Gyimóthy & Larson, 2015; MacKay et al., 2017) and enhance their engagement (Hudson & Hudson, 2013). And the festival is a “collective experience” where the attendees can be highly influenced by others, and social media provides a vehicle for such influence (Schivinski et al., 2019). Consumers with pleasant social media experiences are more likely to attend festivals (W. Lee et al., 2012). When there are festivals, the organizers can show the attractiveness of these events on relevant social media (X. Leung et al., 2017). To some extent, social media has changed the way festival organizers promote festivals and communicate with consumers. In the area of social media, festival organizers have weakened control of the content, and it is difficult to easily modify the image they want to show (MacKay et al., 2017). The diffusion of social media, together with the increased consumer-to-consumer interaction and easier user generation of content (Gensler et al., 2013) contribute to the use of social media to share festival attendees’ experiences. Social media is a highly effective event news medium, which is faster than traditional news channels (Tonga Uriarte et al., 2020) and has become a part of the integrated marketing communications of festival organizations (Gyimóthy & Larson, 2015). Social media also helps event organizers promote their festivals (T. T. Kim et al., 2018).

While academic literature related to social media in festivals is still in its infancy, only a few studies have researched the relationship between social media and festival experience. MacKay et al. (2017) explored the multiphasic experience of festivals to understand the nature and purpose of social media by collecting tweets and posts from four festivals’ Twitter handles and Facebook accounts. They found that the purpose and nature of the message content varied across festival time points on different social media platforms. Semrad and Rivera (2018) revealed that if festival organizers could provide a memorable music festival experience, Gen Y attendees would like to share their experiences via electronic word-of-mouth (eWOM) for festivals and destinations. Masiello et al. (2020) highlighted the key role of the consumers’ experiences in having an important impact on the festival brand personality on social media. Shih et al. (2015) reported that social media can enrich festival attendees’ experiences and encourage them to interact further with others. Hoksbergen and Insch (2016) conducted face-to-face in-depth interviews with 16 adults and revealed that social media is a platform for co-creating festival experiences. Furthermore, Llopis-Amorós et al. (2019) surveyed 622 festival attendees of the main live music festival in Spain, and the results indicated that the use of social media and the spread of festival brands were different in the generational cohort. Consequently, social media has become a tool for promoting festival tourism (Ngernyuang & Wu, 2020) and building a loyal audience (MacKay et al., 2019). Festival organizations are integrating social media into the attendees’ experiences (Van Winkle & Comer, 2011), and social media is gradually becoming the experience itself (Van Winkle et al., 2018).

Festivals have been classified as special events and manifestations of the experience economy (Hoksbergen & Insch, 2016), which attract great attention from both attendees and scholars (Getz & Page, 2016). Although social media research on festival experiences is still relatively limited, a few studies have examined mega-sporting events (Jeong & Kim, 2019; H. Kim et al., 2021; Lim et al., 2015), which are relevant to this topic. It has been suggested that social media also plays an essential role in the context of sports (Prado-Gascó et al., 2017), and studies have shown that social media can allow participants to instantly share their experiences (McGillivray, 2014), enhance the level of engagement between attendees and organizations in mega-sporting events (Filo et al., 2015), and provide a multidimensional experience for fans (Vann, 2014). It can extend attendees’ experiences of sports, culture, and social values (Luguetti et al., 2019) and spread word-of-mouth(e-WOM) related to their experiences through social media (Wakefield & Bennett, 2018). Based on the literature review, the experience of sports events can be divided into a core experience and a peripheral experience (Byon et al., 2013; Chiu et al., 2017), the core experience related to the performance of players, and the peripheral experience referring to the physical and social environment and various activities that affect spectators’ experience, and they might have different experiences on social media (Chiu et al., 2017). Social media has become an increasingly important platform for both events and destination marketers (Andersson et al., 2021). Thus, understanding festival attendees’ experiences has become a critical issue for marketers (Chiu et al., 2017) because of its importance in festival marketing and management. Social media has some advantages for researchers, such as data availability, variability, and numerousness. Given these features, this study collected and analyzed user-generated content on TikTok, the most popular short-video social media platform in China, to gain valuable insights into the festival experience.

Research Method

Research Design

To explore festival attendees’ experiences and differences in demographic characteristics among festival attendees’ experiences on social media. In this study, we choose the Guangzhou International Light Festival (hereinafter “GZILF”) as the case study. Figure 1 summarizes the processes used in this study. The data were collected using TikTok, which is the most popular short-video app in China. Then, the data are preprocessed to build a festival attendees’ experience dataset, and Jieba in the Python programming language is used to tokenize the text. Finally, statistical analysis of high-frequency words and cluster analysis using the modularity algorithm and visualization were performed on the data.

Figure 1.

Framework for data analysis based on TikTok.

Data Collection

Nowadays, GZILF, together with the Lyon Light Festival and Sydney Light Festival, is known as the “world’s three major light festivals.” Since 2011, GZILF has been held 10 consecutive times by 2020. In 2015, it was selected as a large-scale cultural activity of the UNESCO (United Nations Educational, Scientific and Cultural Organization) “International Year of Light,” which has become a new symbol representing the image of Guangzhou City. The core locations of the festival are Haixinsha and Flower City Square, Tianhe District, with the largest number of attendees. The festival has attracted around 65 million attendees over the past decade. Because of the excessive number of attendees, the organizers for the first time had to adopt the appointment system to control the numbers from 2018.

Data were collected on TikTok, using “Guangzhou Light Festival” or “Guangzhou International Light Festival” as the keywords collected from festival attendees’ post content and comments on TikTok, and we obtained 76,352 records. Figure 2 shows the consumer’s post content and demographic characteristics on TikTok as an example. Various pieces of information can be obtained, such as check-in spot, post time, post text, comments, and the consumer’s demographic characteristics, such as gender, and age.

Figure 2.

Example of consumer post on TikTok.

Data Pre-Process

All post texts collected from TikTok were preprocessed by data filtering and tokenization. Data filtering includes three operations for removing incomplete or useless records: First, based on the post time, we chose data related to the GZILF on November 15, 2021, and obtained 76,352 records. Second, to ensure that the data were originally posted by festival attendees rather than forwarding others’ content, we filtered the data according to the check-in spot and obtained 58,847 records. Third, to understand the difference in demographic characteristics in festival attendees’ experiences, we chose consumers with marked gender and age. Finally, we obtained 33,304 records for the dataset, including 3,769 post texts and 29,535 comments.

The purpose of tokenization is to divide consumers’ post text about their festival experience into keywords, phrases, or other meaningful elements (Guo et al., 2017; Hou et al., 2019), such as festival location, festival time, feelings about the festival. In this study, Jieba was used for tokenization. A Chinese word segmentation module, developed by Chinese programmers in Python, was used (Hou et al., 2019). It has advantages in dealing with Chinese word segmentation; for example, it supports three types of word segmentation modes: precise, full, and search engine. It also supports traditional Chinese word segmentation and custom dictionaries; therefore, it is widely used at present in Chinese word segmentation.

Data Analysis

High-frequency words and phrases

The collected data with consumer post text on TikTok is mainly short sentences, the longest is 58 words, and the shortest is 1 word. The total number is 345,585 words, and the average is 10.38 words. Therefore, this study applied the Jieba tool to tokenize the text and then translate these tokenized words into English words and phrases. For example, the original post text is:

广州灯光节，无人机升起的那一刻，有一种莫名的感动，现场真的好震撼人心啊！

The translation is:

At the Guangzhou lighting festival, at the moment of the unmanned aerial vehicle (UAV) rising, there is a kind of inexplicable touch. The scene is good shaking!

After preprocessing the post text, the sentence was:

广州灯光节/，无人机/升起/的/那一刻/，有/一种/莫名/的/感动/，现场/真的/好/震撼人心/啊！

Filtering out meaningless words, the sentence was:

[w] 广州灯光节 [w] 无人机 [w] 升起 [w] 那一刻 [w] 一种 [w] 莫名 [w] 感动 [w] 现场 [w] 真的 [w] 震撼人心。

Therefore, these words were translated into English: [w]Guangzhou lighting festival, [w]unmanned aerial vehicle (UAV), [w] rising, [w]the moment, [w]a kind of, [w]inexplicable, [w] touch, [w]the scene, [w]shaking.

We then accumulated the words to generate high-frequency words, with words appearing over 100 times in this study. High-frequency words were translated into English words and phrases.

According to these words’ meanings, we divided these high-frequency words into five dimensions of festival experiences, which are shown in Table 1.

Table 1.

High-Frequency Words and the Dimensions of the Festival Experience.

Festival experience dimensions	High-frequency words and frequencies
Cognitive experience	Lighting Festival (4,176), Guangzhou (3,157), reservation (1,721), Guangzhou tower (817), date (673), time (647), ticket (547), evening (511), Haixinsha (254), Flower City Square (253), opening (176), reform and opening up (161), Pearl River New Town (133)
Novel experience	Light show (533), night view (297), unmanned aerial vehicle (UAV) (176), performance (127), expectation (100)
Affective experience	Applaud (844), beautiful (444), good-looking (363), motherland (201), shocking (149), China (143), charming (108)
Physical participation	Take (videos or pictures) (2,536), line 3 (692), subway (378), walk (311), crowded (235), stand (256), a sea of people (102)
Social interaction	Have a data (794), take (me) (689), friends (552), TikTok assistant (468), together (332), meet (100)

Modularity algorithm

Modularity is a method used to measure the quality of community divisions in community detection (Blondel et al., 2008; Karypis & Kumar, 1998). Its definition is given in Equation (1).

$Q = \frac{1}{2 m} \sum_{i, j} [A_{ij} - \frac{k_{i} k_{j}}{2 m}] σ (c_{i}, c_{j})$

where $A_{ij}$ is the weight of the edge between User_i and User_j, $k_{i}$ is the sum of all the weights connected to Useri, $c_{i}$ is the class label of Useri, and $σ (c_{i}, c_{j})$ is the Kronecker delta. The independent variables of the Kronecker delta are usually two integers ( $c_{i}, c_{j}$ ). If they are equal, the output value is 1; otherwise, it is 0.

Currently, the Louvain method is the most widely used method for calculating modularity. The calculation steps are as follows.

Figure 3 shows a flow chart of the community division.

Figure 3.

Flow chart of community division.

Algorithm.

Louvain Community Detection.

Input: The relationship of each pair of nodes named User_i and User_jOutput: The label of each user

1. Continuously Iterating the user node w in the data set. Assume that each node is a community and N nodes, initialize N communities, and try to join a single node. The joining community can maximize modularity improvement until all nodes are no longer changed. The definition of modularity improvement is:

pt Δ Q = [\frac{k_{i, in}}{2 m} - \frac{\sum_{tot} k_{i}}{2 m^{2}}]

\frac{k_{i, in}}{2 m}

denotes the edges of the community after putting the node into the neighbor community.

\frac{\sum_{tot} k_{i}}{2 m^{2}}

denotes all the edges of the community (including outer and inner edges).2. Processing the result of step 1, merge the several small communities into a large node, then reconstruct the network. The process flow is shown in Figure 1.3. Iterating steps 1 and steps 2 until Q tends to be stable (the sum value of modularity of all sub-communities does not change), and the algorithm is completed. The calculation method of Q is as follows:

Q = \sum_{c} [\frac{\sum in}{2 m} - {(\frac{\sum tot}{2 m})}^{2}] = \sum_{c} [e_{c} - {a_{c}}^{2}]

\sum_{c} [\frac{\sum in}{2 m}]

denotes all inner edges in the sub-community,

\sum_{c} [{(\frac{\sum tot}{2 m})}^{2}]

denotes all the edges of the sub-community.

Analysis of the Results

Statistical Analysis of High-Frequency Words

If only simple statistics are used, high-frequency words will be meaningless in the context of the words themselves (Hou et al., 2019). This study adopted a manual content analysis method like that of Xiang et al. (2017) and Boo and Busser (2018). This method of manual content analysis is necessary and flexible to allow the authors to better identify topics (Boo & Busser, 2018; Sotiriadou et al., 2014) and has a wide range of applications in research on topic classification (Boo & Busser, 2018; Cheng et al., 2019; Hou et al., 2019; Xiang et al., 2017).

Table 1 shows the statistical results of the high-frequency words and word frequencies. According to the meaning of high-frequency words, we divide the festival attendees’ experience into five dimensions: cognitive experience, novel experience, affective experience, physical participation, and social interaction. The cognitive festival includes the time, place, theme, and management of the festival, which reflect consumers’ understanding and knowledge of the GZILF. The novel experience shows that festival attendees feel fascinated, especially those who come to the festival for the first time. Affective experience shows the rich emotions of festival attendees, and these words indicate the festival attendees’ praise for the festival and their love for the country. High-frequency words related to physical participation reflect the large number of people participating in the festival and the state of festival attendees in the activity. The last is social interaction, which reflects those consumers who seek interaction and attention on social media, indicating that social media provides an interactive platform for users to socialize.

Cluster Analysis

Although the dimensions of festival attendees’ experiences are obtained from high-frequency words, to determine whether the consumers’ different demographic characteristics have differences in the festival experience, a modularity algorithm is applied and Gephi is used to visualize the attendees’ overall and different festival experiences in terms of gender and age.

Festival attendees’ overall experiences

According to the results of clustering and visualization using Gephi software, which is an open-source free cross-platform complex network analysis software based on the Java virtual machine, it is mainly used for the interactive visualization and detection of various networks and complex systems, as well as dynamic and hierarchical graphs.

Figure 4 shows the clustering results of the festival attendees’ overall experiences. We can see that the cognitive experience is the largest proportion of the festival attendees’ overall experience, which indicates that the attendees mainly focused on the introduction of the time, place, and content of the festival. Second, physical participation is another important dimension of the festival experience. The reason is that the lighting festival attracted a large number of festival attendees, which made them feel very crowded, and some people hardly moved, only standing there for several hours. Third, the proportion of festival experiences with emotional experiences and social interaction is similar. Finally, the novel experience accounts for the smallest proportion of the festival experience because the UAV performance was only shown once and for one day during the ceremony of the festival, and many people failed to see that the proportion of novel experiences was low.

Figure 4.

Clustering results of festival attendees’ overall experiences.

The different festival experiences in terms of gender

Among the 33,304 records collected, 18,753 (56.31%) records were posted by males, and 14,551 (43.69%) records were posted by females.

Figure 5 shows the clustering results of attendees’ festival experiences by gender. We learn that the proportion of cognitive experience dimensions of the two is the largest, while males have higher cognitive experience than females, indicating that males have a higher degree of rationality. Second, the physical participation and emotional experiences of females account for a large proportion of festival experiences, which indicates that compared with males, females are easily affected by the surrounding environment and are more likely to express personal emotions. While Getz and Page (2016) point out that males and females are equally affected by high social density, males suffer more from high spatial density. Finally, for the dimensions of the festival experience of novelty experience and social interaction, males and females are similar, which indicates that whether male or female, they would like to get others’ attention on social media.

Figure 5.

Clustering results of attendees’ festival experiences in terms of gender.

The different festival experiences in terms of age

Among the 33,304 data collected records, there are 1,052 (3.16%) consumers under 20 years old, 25,684 (77.12%) consumers aged 20–29 years old, 5,875 (17.64%) consumers aged 30–39 years old, and 693 (2.08%) consumers over 40 years old. The author personally participated in five consecutive lighting festivals from 2016 to 2020; the majority of festival participants were young people. Therefore, the age structure of consumers on social media platforms is similar to that of festival participants to a certain extent.

Figure 6 shows the clustering results of attendees’ festival experiences in terms of age. We can see that the cognitive experience dimension has a high proportion in each age stage, which is like the overall experience dimension distribution of users. Through further comparison, it was found that the proportion of cognitive experience of consumers over 40 years old is the highest, followed by 30–40 years old, 20–29 years old, and finally less than 20 years old, indicating that with an increase in age, the proportion of the cognitive experience dimension is larger and the degree of rationality is higher. Second, from the perspective of physical participation, the proportion of those less than 20 years old is the largest, followed by 20–29 years old and 30–40 years old, and the proportion above 40 years old is small—indicating that the younger the age, the more physical participation. The results show that young people prefer to share their physical feelings and comments on social media. The proportions of different age stages were similar in terms of novelty experience and emotional experience. Finally, in the social interaction experience dimension, each age stage shows a state of active participation in getting other people’s attention.

Figure 6.

Clustering results of attendees’ festival experiences in terms of age.

Conclusion and Discussion

This study provides a unique research perspective on the festival experience. Social media contains abundant data. Although an increasing number of researchers have realized the importance of social media platforms in tourism research, it has not been applied well to events and festivals. Experiences are the core phenomenon of a festival and understanding festival experiences is important for festival organizers, which can help them better design and manage festival programs. Only a few studies have examined the relationship between social media and festival experiences. Furthermore, with the growth of various social media platforms, researchers have ignored the rapid development of short-video social media in recent years, for example, TikTok. Compared to big data research on the tourism and hospitality industry, there is a lack of research on festivals. Based on this, this study takes TikTok as the data source and explores festival attendees’ experiences with it. By collecting data on the Guangzhou International Light Festival in TikTok, this study analyzed festival attendees’ experiences on social media and the different festival experiences of their demographic characteristics in terms of gender and age. The results of this study are as follows.

First, by using the Jieba tool to tokenize the text, we obtained high-frequency words and phrases and then classified these high-frequency words and phrases into five dimensions of the festival experience according to the meaning of words and phrases. The five dimensions of festival attendees’ experiences are cognitive, novel, affective, physical participation, and social interaction. The first three dimensions are consistent with previous research on the psychological nature of leisure experiences (Mannell & Iso-Ahola, 1987). As Geus et al. (2016) pointed out, festival experiences have cognitive, conative, and affective components, which are mentioned as experience dimensions (Getz & Page, 2016). This novel experience underlines the unique nature of events and festivals, the purpose of which is to provide an extraordinary experience (Jago & Shaw, 1998). This classification result was combined with the findings of Morgan (2008) and Geus et al. (2016). The difference is the social interaction here, which is an online interaction mode that aims to gain more attention from other people on social media. However, previous research has revealed offline social interaction, which is communication and interaction with others during festivals. Both of them are ways of social interaction, and some studies have demonstrated that social interaction is an important motivation for attending the festival (Dodd et al., 2006).

Second, a combination of the modularity algorithm and Gephi is used to cluster and visualize the festival attendees’ overall experiences and the different experiences in terms of gender and age. The findings show that festival attendees would have different experiences when attending festivals, which supports the results of previous studies (Ballantyne et al., 2014; Chiu et al., 2017; Geus et al., 2016; Morgan, 2008; Page, 2016). It was found that, in festival attendees’ overall experience, cognitive experience accounted for the largest proportion, followed by physical participation, and the proportion of social interaction, emotional experience, and the novel experience was moderate. While previous studies mainly analyzed festival attendees’ overall experiences, they rarely discussed the differences in attendees’ characteristics in the festival experience. As the literature suggests that there are often significant differences between males and females in festival experiences (Ahn et al., 2020; Ballantyne et al., 2014; Brown & Sharpley, 2019), this study also confirmed that there is indeed a gender difference in festival experiences. From the perspective of consumers’ gender characteristics, males focus on cognitive experiences and have a higher degree of rationality. Compared with males, females’ emotional experiences and physical participation are stronger, and their emotional level is higher. Zhang et al. (2009) argued that females are inclined to care more about emotional aspects, while males are inclined to focus on instrument value. “This distinction between the sexes impacts how each gender observes the environment, processes, evaluates, and retrieves information, and makes judgments” (Karatepe et al., 2006).

Third, the current study also explored the differences in age in festival experiences on social media. From an age perspective, most festival attendees are young adults, especially those aged 20–29. This result is similar to that of Llopis-Amorós et al. (2019), who find that young consumers have a more positive impact on social media. We found that with the increase in age, the proportion of the cognitive experience dimension is larger, and the degree of rationality is higher. The younger the age, the higher the physical participation dimension; young people prefer to share their physical feelings and comments on social media. Ballantyne et al. (2014) revealed that young people (in their 30s) had a stronger and more positive festival experience than those over-30s at the Woodford Folk Festival in Queensland. Because young people grow up with these rapidly developing social media platforms compared to older people who have to learn how to use them (Prensky, 2001), different festival experiences may emerge across generations (Correa et al., 2010). In the social interaction dimension, each age stage shows positive participation in attracting more people’s attention. Social media has gradually become the primary platform for festival attendees to share their experiences with friends, family members, and others (Shih et al., 2015). The participation and interaction of festival attendees on social media enrich their festival experiences and become the festival experience itself.

Theoretical Implications

This study provides unique research methods for festival attendees’ experiences on social media. We move past the limitations of prior studies and introduce new popular social media platforms in China and research methods in this study. The results of this study contribute to the theoretical development of consumers’ content-related research using Jieba and the modularity algorithm—a type of big data technology. Compared with previous research methods of festival experience on social media, such as questionnaires (Llopis-Amorós et al., 2019; Semrad & Rivera, 2018; Shih et al., 2015), interviews (Hoksbergen & Insch, 2016), and statistical analysis (MacKay et al., 2017), the methods used in this study can promote the understanding of the hidden logical relationships behind the text and more effectively identify valuable topics (Hou et al., 2019). On the one hand, the Jieba tool is used to tokenize texts on social media, which can achieve a better effect when dealing with Chinese words. The tool was developed by Chinese programmers in Python (Hou et al., 2019), has many advantages in dealing with Chinese word segmentation, and has been widely used in China. Conversely, as a measure of the quality of community division in community detection (Blondel et al., 2008; Karypis & Kumar, 1998), the use of the modularity method can contribute to finding attendees’ different festival experiences on social media. This accurately reveals consumers’ festival experiences through visualization using the Gephi software. The graph depicts festival experiences among attendees’ demographic profiles, effectively resolving the shortcomings of previous studies.

Managerial Implications

The results of this study have important practical implications for improving festival attendees’ experiences and the strategic development of festival organizations on social media. The findings of this study indicate that festival attendees’ experiences include the cognitive, novel, and affective experiences; physical participation, and social interaction on social media. Therefore, festival organizations should not only use social media for promotional activities (MacKay et al., 2017), but should also provide festival attendees with opportunities to engage with numerous facets of the festival experience (Ballantyne et al., 2014) to facilitate the building of relationships between festival attendees. Furthermore, the findings reveal that festival attendees’ experiences are triggered differently for males and females. The males are inclined to focus on cognitive experience, which is related to instrument value, while the females are likely to share an emotional experience. Accordingly, different strategies can be implemented for males and females and could be implemented (Zhang et al., 2009). Festival organizations can provide diverse, value-for-money-type activities to attract males. Conversely, to appeal to females better, festival organizations can emphasize the fun of the festival, and it is important to pay more attention to the content of the female’s emotional experience to improve the quality of the festival. Another important practical implication is that age presents a difference in festival experiences. The use of social media is particularly popular among the younger adult cohort, who prefer to share their physical feelings and comments on social media, while older adults are likely to share their cognitive experience, which focuses on information about the festival. Judging from the results of this study, it is more important for festival organizations to listen to attendees’ feedback, pay more attention to their needs, and give greater credibility to content related to the festival on social media. Therefore, festival organizations should consider social media as a key element in their marketing to improve the quality of the festival. The findings of this study can help festival organizers identify attendees’ festival experiences quickly. Using this approach, festival organizers can better understand attendees’ experiences and improve their quality of service and products.

Limitations and Future Research

This study has some limitations and highlights future research directions. First, the results of this study were based on a social media dataset. The findings of this study may not be generalizable to other festivals because of the rapid growth of social media in China. Future research could combine two or more social media platforms to obtain more data. Second, TikTok has a diversity of consumers content; this study only selected the post text, comments, gender, and age of the consumers. Future research should consider consumers’ other content and obtain more comprehensive consumer information. Third, it is worth noting that some researchers are concerned about the credibility of social media reviews (Chiu et al., 2017). However, some studies have suggested that consumers’ reviews on social media have a higher level of trustworthiness in expressed opinions than commercial information sources (Barreda & Bilgihan, 2013; Chiu et al., 2017). Although the data in this study seem quite limited compared with tens of thousands to hundreds of thousands of social media posts on mega-events and other festivals, this study attempts to introduce the new social media, which is a popular social media platform in China and even the world. Medina Serrano et al. (2020) pointed out that TikTok was the world’s second-most downloaded app in 2019. Furthermore, we collected as much comprehensive information about the festival as possible on TikTok, including user post texts, comments, user gender, and age. Additionally, as a short-video social media, TikTok is a video-sharing social media (Medina Serrano et al., 2020), and future research should focus on customers’ videos using different methods. Fourth, Gannon et al. (2019) revealed that there are differences in the festival experiences of repeat attendees and first-time attendees. This study only analyzed differences in attendees’ gender and age. Therefore, future research could analyze the differences between group characteristics in festival experiences. Given the tendency for experiences to vary at different stages of the festival (MacKay et al., 2017), it would be interesting to research how social media plays a role before, during, and after the festival phases. Future studies should conduct longitudinal research at different stages of the festival and event processes. Finally, a modularity algorithm was used in this study to achieve a better clustering effect. Combining the technology of big data in tourism can process more abundant data and further enrich existing tourism research methods.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This study was supported by a grant from the National Natural Science Foundation of China (NSFC) (No.42171228,to Guangquan Dai).

ORCID iDs

Shuhua Yin

Guangquan Dai

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