This section presents a new framework of aesthetic experience and popularity and implements machine learning experiments to measure the popularity of controversial and non-controversial pieces of art.
5.1. Study 1. A New Framework of Art Experience
In museum-based art, aesthetic experience has garnered a great deal of attention. Several models portraying the viewer’s involvement with art can be found in the literature [
33], but they do not account for the use of social media prior to, during, and after a visit to an art exhibition. However, other perspectives failed to recognize the emotional connection as an intrinsic component of the art experience. Moreover, since Instagram’s release in 2010, a reasonable amount of research has been conducted on its use in general, but less in a museum or gallery setting. This has prompted authors such as Suess [
108] to give a comparable perspective through the paradigm of “Art Gallery Visitor Instagramming”. Suess divided a museum visit into three stages based on physical presence: pre-visit, during visit, and post-visit. The first stage includes triggers such as Instagram posts from other museumgoers that inspire an individual to visit a museum. They are encouraged to take pictures, provide feedback, and share them on social media during or after their visit. The third stage is a circular process that focuses on the visit’s impacts, such as uploading, commenting on, and sharing photographic content. As previously noted, psychological models do not include the concept of social media. Similarly, Suess’s approach does not include the emotional aspect in any obvious sense. To bridge this gap, we present a new framework regarding the individual’s prior exposure to art and the popularity of the art. We divided the procedure into three stages, as depicted in
Figure 2 To be completely impartial, we took terms from various models describing the aesthetic experience. Due to the dissimilarity of our scenarios, we did not study the same aspects. We explain our concept in detail below.
In summary, we clarify the inadequacies of the models at the outset of this subsection, either in terms of the cognitive dimension of aesthetic experience or the use of social media during museum visits. Suess’ paradigm inspired the stage names (pre-visit stage, during visit stage, and post-visit stage) as well as the sub-stages applicable to the viewer’s social media activity. We also borrowed aesthetic experience aspects that trigger similar emotions in the viewer, such as space, artwork qualities, and personality (these factors are discussed in depth in
Section 2.1). Regarding popularity, none of the preceding models are included. Sociometric popularity is related with emotions that individuals may share with others, according to the research. Moreno [
70] illustrated this using the term “emotional judgments.” In contrast to perceived popularity, which is based on evaluations and is hence widely shared with others, the conclusion of the model of Leder et al. [
22] of aesthetic experience is “aesthetic judgments” and “aesthetic emotions”. Although the relationship is vague, because Leder et al. [
22] employed two unique notions as an outcome of the model, we might claim that popularity is a byproduct of aesthetic experience. This is the argument that
Figure 1 intends to present.
Our goal was to illustrate the aesthetic experience of the individual and the popularity of the artworks in three stages, comprising pre-visit, during visit, and post-visit, as follows:
First, the person is exposed to external triggers before the visit. Digital artworks, artist social media posts, and museum posts are external stimuli. According to studies, smart device use has mediated visual and social experiences [
109]. Art is also affected. Instagram’s audience includes “exploration” users who search the home page and peruse algorithm-suggested content. The “associated” audience uses the app to maintain contacts. The “thematic” audience receives hashtags about certain people, items, places, emotions, or phrases. Moreover, “chatterbox” viewers may like commenting [
85]. FOMO (“fear of missing out”) is yet another kind of audience made up of users who monitor other users’ online activity [
85,
110]. It may be the first step, but it is crucial because it includes the motivations that will drive the individual to potentially visit a museum, either to seek out the artwork or the location that observed. In any case, it is the viewer’s initial encounter with the artwork that they may or may not be familiar with. In both instances, the viewer can share the art encounter on social media.
Second, the aesthetic experience is determined by the environment, the artwork’s qualities, and the individual’s personality [
33]. That is when individuals engage, appreciate, judge, and generate emotions in response to artworks and their settings. This stage is an internal process. Several factors, along with social media activity, represent the viewer’s intimate attachment. First impressions are intuitive [
111], and combine visual sense, structure, style, meaning, and emotional response [
111]. Social media apps enhance the art experience when viewing exhibits. Stylianou-Lambert [
112] argues that taking photos in a museum can preserve the art encounter. Additional incentives include emotional gratification, educational interests, and amusement. After seeing these posts, users consider visiting. Budge [
113] claimed visitors use Instagram to document their art encounters and promote exhibitions. She also stated that visitors’ use of Instagram improves social cohesion and user socialization by communicating “I’m here. You can be here too” [
114]. This stage concentrates on the most critical aspect of the aesthetic experience, which is the individual’s presence in the museum while viewing an artwork. There, the observer will engage with and emotionally experience the artwork and its surroundings.
These emotions enhance social media sharing. The posts usually include emotional content. Sharing photos generates external stimulus that people like, comment on, and distribute. We placed social media networks and popularity as a third-stage outcome. As indicated, a public art installation that elicits intense emotions may gain popularity through social media sharing. When the monument was wrapped, social media reactions on both platforms increased [
78]. In another study, we examined the nexus between museum visitor posts, emotions, and artwork and meme popularity. After detecting emotions with machine learning algorithms, ranking tasks revealed the most popular meme and museum post, which can enhance aesthetic experience and popularity [
84].
Considering how
Figure 1 could be used to reshape the concept of popularity, we argued that the aesthetic experience is an internal process in which individuals are exposed to external stimuli and are compelled to attend art places in which they engage and experience emotions, which they share online, and therefore generate new external stimuli that may gain popularity. This is also the main distinction between this study and others in the same field. After critical analysis, theoretical works neglected social networking. While other studies have focused on the role of social media during the museum visit, they did not address the viewer’s emotional state. Similar surveys in laboratories or museums have relied on questionnaires, interviews, or other technologies. Social media studies in the arts evaluated user engagement or popularity, but not emotion. Kang, Chen, and Kang [
115] explored the most liked artworks and the link between Instagram likes, comments, and the artist’s creative process. Quantitative (Instagram data) and qualitative research was carried out (online questionnaires responded to by artists). Artists denied that their most liked work is also their favorite. They asserted their most popular artworks and followers’ interaction will not affect their creativity. This study did not incorporate the emotional component and found no link with popularity.
5.3. Data Handling
The next step involves data handling. The ranking requires two datasets. The LightGBM algorithm is numeral based. The data should be pre-sorted and stored in a csv (comma separated value) file. We built two datasets, one for controversial (
N = 3662) and another for non-controversial (
N = 2462) artworks, each with nine columns, namely id, label, comments, likes, saved to collection, media type, media count, has more comments, and Facebook shares. The “label” column depicts the impact of attributes on what is popular or not. As in our prior research on memes [
84], we provide experiments that declared relevance equally. There are some previous examples. In their music popularity prediction experiments, Lee and Lee [
124] utilized multiple popularity indicators extracted from song ranks measured using rank scores, including max rank and rank (the song’s rank). They anticipated the most popular song on a 100-song chart would be 100 and the least popular would be 1. Chapelle and Chang [
125] employed labels {0, 1, 2, 3, 4} for document retrieval ranking in their model, where positive numbers denoted higher relevance and negative numbers denoted lower relevance. In our most recent analysis [
84], we assigned the values for this feature from 0 (absolute negative correlation) to 3 (absolute positive correlation). Each artwork was in a separate group. Using OpenRefine [
103], all samples were properly allocated a value between 0 and 3 based on the number of interactions per post. We gave 3 to the most interacted-with posts. We gave 0 to the posts having the least engagement. Relevantly, the remaining medium values were assigned 1 and 2.
5.5. Data Exploration
As the total number of artworks used for ranking experiments in this problem is smaller than that in previous tests, it was considered proper to investigate the data distribution in both controversial and non-controversial artworks at this time. There is a total of 28 works of art, 14 of which are controversial and 14 that are not. The following two tables illustrate the distribution of values for the likes and comments features, which have the greatest variation compared to other features and can have a significant impact on the ranking.
Table 2 depicts the distribution of data for two key features of the ranking dataset. As can be noted, it contains seven columns that define the total number of posts for each controversial work of art, the average number of likes, their maximum (max) and minimum (min) values, and their standard deviation (std). Most posts concern Andy Warhol’s Campbell’s Soup Cans. According to the distribution, the minimum amount of likes for an artwork is 0, while the maximum is 5810 for the sculpture Forever Marilyn, etc.
Table 3 depicts the comment feature’s data distribution, which includes the total number of posts, the mean value of comments overall, the maximum and minimum values for each non-controversial artwork, and the standard deviation. According to the data, the minimum number of comments is 0 and the maximum number of comments is 1, related to the controversial artwork Comedian.
According to the data, the minimum number of comments is 0 and the maximum number of comments related to the artworks is 321, for Banksy’s Love Is in the Bin and Mauricio Cattelan’s Banana. In a similar vein, data regarding the non-controversial works of art are provided. Fourteen works of art of various genres were created in response to each other’s controversy in the same year. Moreover, the two tables that follow depict the distribution of values for the most variable features, likes and comments, which may have a significant impact on the ranking tests. The two tables that follow indicate the distribution of values for the likes and comments features, which have the greatest variation among the other attributes and may have a major influence on the ranking of non-controversial artworks.
Table 4 portrays the data distribution for two key features of the ranking dataset. In addition, it contains seven columns comprising the total number of posts for each non-controversial work of art, the average number of likes, their maximum (max) and minimum (min) values, and the standard deviation (std). Most posts are about Maman by Louise Bourgeois. According to the distribution, the lowest number of likes for an artwork is 0, while the maximum is 6925 for Solid Sky by Alicja Kwade.
Furthermore,
Table 5 depicts the comment feature’s data distribution, which includes the total number of posts, the mean value of comments overall, the maximum and minimum values for each non-controversial artwork, and the standard deviation. According to the data, the minimum number of comments is 0 and the maximum number of comments related to the artwork IKB-191 is 335. As shown in the preceding table, the percentage of comments for the subsequent works of art is significantly lower.
Considering the data structure and context, an additional ranking study was conducted to determine the most popular controversial and non-controversial works of art. This research query relates to controversial works of art. Due to the size of the sample and the number of discovered artworks, multiple datasets were produced to conduct the ranking experiment. Given the general technical limitations for data searching and downloading, a standard sample size (
N = 3662) of controversial Instagram artworks was obtained. The dataset contained nine columns: ID, comments (count of comments per post), likes (count of likes per post), saved to collection (if the users saved the post to their Instagram account’s private collection), media type (if the post is a feed, the carousel may contain up to ten photos or videos, which is the maximum number), media count (the number of photos or videos if the post is a carousel), has more comments (if the specific post has the most comments). In all datasets, approximately 80% (11 groups) were designated for training, whereas the remaining 20% (3 groups) were designated for validation testing, as it was desirable to include all posts from the same group. All the selected artworks were included in a testing set to determine their rating scores, despite the fact that the number of artworks is relatively small due to the previously described constraints. Therefore, in each iteration of the algorithm, the groups in the testing set were adjusted to three to ensure that all artworks in the testing set pass, and a final score was calculated by combining their individual scores. Five distinct datasets were constructed for this research query. This method helped each artwork pass the entire test set. Consequently, separate evaluation scores were extracted for each of them. To determine which group is the most popular, it is incorrect to determine which post is the most popular because a group may not be popular but have several popular posts. The objective is to determine which group is the most popular overall. Using the weighted average of all post scores for each group may be an appropriate criterion for determining the most popular group. However, because an excessive number of posts garnered negative ratings, a different method was implemented. Therefore, it was optimal to select 10% of the most popular posts. In a group of sixty posts, for instance, the average of the best six is chosen. With each algorithm run, the most popular artworks were thus identified. The final ranking is displayed in
Table 6 below.
The scores of the most popular controversial artworks range from 6.92 (the highest-ranking value) to −3.29 (the lowest and negative ranking value) according to the ranking task’s overall measurement data. Given that both datasets contained renowned works of art, this improved both the research questions and the model’s reliability. As shown in the table, the three most popular controversial works are Comedian by Maurizio Cattelan, Fountain by Marcel Duchamp, and Girl with Balloon (Love is in the Bin) by Banksy. These works have been primarily characterized as controversial. We briefly examine the reason for this. Comedian was created by the well-known artist Maurizio Cattelan. It consists of three versions, of which two were sold at Art Basel Miami Beach for
$120,000 each and the third was donated to the Guggenheim Museum. It is extraordinary that the fruit was purchased for only 30 cents at a Miami grocery store. The artwork consists predominantly of a fresh banana displayed with sticky tape on the wall. As a piece of conceptual art, it comes with the necessary certificates of authenticity and display instructions. Many questioned it, while others described it as an expensive selfie, a humorous minimalist work of art, or cynical [
127]. Although the artwork has its own Instagram account, it is also featured on the cover of the New York Post [
128]. Fountain by Marcel Duchamp is considered the most controversial work of art in the world to this day. It is a readymade sculpture of an inverted porcelain urinal bearing the signature “R. Mutt”. It sparked a heated debate among art experts about what constitutes art and whether Fountain is art. Thus far, neither the narrative behind the sculpture nor its signature have been disclosed [
129]. Similarly, the muralist Banksy painted Girl with Balloon. It is an artwork that was initially captured as a mural. It was also his only work to be transferred to paper and the only work in history that was destroyed in front of the public during its auction at Sotheby’s using a special mechanism. It was damaged immediately after its sale for £18,582,000. The auto-destructive artwork was subsequently renamed Love is in the Bin [
130]. Even the artist is controversial due to the political, capitalist, and consumerist subject matter of his works. As he has not disclosed his identity, it is assumed that on the day of the auction he was also present in the auction hall [
131]. In contrast, the least renowned work of art is Artist’s Shit by Piero Manzoni. It is also observed that, following the three most popular works of art, the next four are somewhat less popular, and that the remaining works follow a downward trend and produce negative results. This suggests that the latest artworks are unpopular.
We used the identical methodology in the subsequent ranking investigation. The present research query relates to controversial works of art. Due to the magnitude of the sample and the number of non-controversial artworks discovered (
N = 2462), multiple datasets were also created to complete the current ranking experiment. In all datasets, approximately 80% (11 groups) were designated for training, whereas the remaining 20% (3 groups) were designated for validation testing, as it was desirable to include all posts from the same group. Moreover, to determine ranking scores for each of the selected artworks, they were all included in a testing set, despite the relatively small number of artworks due to the technical limitations of Instagram. In turn, in each iteration of the algorithm, the groups in the testing set were adjusted to three so that all artworks in the testing set succeed and a final score was calculated by combining their scores. To achieve this, five distinct datasets were constructed. It is notable that the same method of selecting the weighted average (10% of all posts) was used to determine the most popular group in this dataset. Each time the algorithm was executed, the most popular non-controversial artworks were identified. The final ranking is displayed in
Table 7 below.
The scores of the most popular controversial artworks range from 9.61 (the highest-ranking value) to −1.93 (the lowest and negative ranking value) according to the ranking task’s overall measurement data. According to the results, the first two artworks appear to be the most popular, with the third artwork following closely behind with a lower ranking score, and the remaining artworks demonstrating a particularly precipitous decline, culminating in a negative ranking. Based to the ranking, Waiting for climate change by Isaac Cordal, The Gates of Hell by Auguste Rodin, and Solid Sky by Alicja Kwade are the most popular works. As with the previous experiment’s artworks, a concise explanation of the non-controversial artworks is provided in the lines that follow. The Waiting for climate change project is an art installation by Isaac Cordal that was exhibited in the summer of 2013 in the moat of the Château des Ducs de Bretagne, in Nantes, France. The installation comprised fourteen floating sculptures that moved in line with the wind and water currents. The real-size sculptures depicted figures in business attire. The objective was to demonstrate their apathy towards climate change, and specifically the rising water level [
132]. The monumental sculpture The Gates of the Hell by Auguste Rodin depicts the first part of Dante Alighieri’s Divine Comedy through 180 figures. It is exhibited in the Musée d’Orsay in Paris. It has been described as Rodin’s most repulsive and incoherent sculpture, but it has not been deemed controversial [
133]. Solid Sky by Alicja Kwade consists of a polished quartzite sphere weighing 22,000 kg and suspended in steel chains from the ceiling of the 550 Madison building in Manhattan. It is a work with political but not provocative implications, as the artist mentioned when comparing it to the planet Earth, and still remains as a symbol of capitalist corruption and a competitive world [
134]. In these two cases, for both research queries, the models that emerged may have been unique, but the results were comparable, which is essential for the ranking’s accuracy. Given that both datasets contained popular works of art, this improved both the research questions and the model’s reliability.
Since the concept of controversial and non-controversial artworks was fundamental to this context, identifying the most popular works in each category was not the sole objective of this study. In addition, it intends to investigate the emotions elicited by these works in their viewers and analyzes these emotions through text processing of Instagram posts referencing these works. Art can have a significant impact on an individual’s emotional state, inducing either positive or negative emotions, according to a review of the relevant literature. With very few exceptions, art receives no response from viewers. In fact, both the literature and the experiments demonstrate that the context and inherent qualities of art objects can affect an individual’s aesthetic experience and engagement. Therefore, the text of the posts was kept in the same order as that of the other datasets so that it would be simpler to analyze and assign emotions using the annotated method and the SMART tool discussed in a previous section. Most studies evaluate emotions using the VADER lexicon from the NLTK tool, which categorizes emotions as positive, negative, or neutral. This procedure, however, does not meet the requirements of the research topic. For this purpose, we selected the emotions of disgust and surprise, as mentioned below. The posts that express no other emotion were marked as neutral.
5.6. Emotions Distribution of Controversial and Non-Controversial Artworks
The structure of the Instagram datasets was further investigated by analyzing their distribution. Both sets of data contained both the final ranking scores and the emotion attribute. The distribution of the three selected emotions (surprise, neutral, and disgust) across both datasets is depicted in
Figure 3. The selection of emotions was based on user posts and is limited to three distinct emotions because other lexicon-based methods, such as NLTK, classify emotions into three equally categories, such as positive, negative, and neutral. Therefore, we conformed to this practice. The selection of emotions was based on Paul Ekman’s theory of basic emotions and a broader taxonomy of eleven pairs of emotions consisting of three columns, where the kinds of emotion, for instance, are emotions related to object properties, event-related emotions, social emotions, etc. [
55]. The other two columns contain positive and negative emotions. For our task, we selected the first category of emotions related to the object properties, and among the positive and negative emotions, we selected surprise and disgust, respectively. We used the neutral emotion for works that did not express any emotion or were partially insensitive.
According to the number of samples, the datasets are small because the emotion classification only pertains to the three most popular artworks in each dataset, as determined by ranking. The controversial artworks revealed a normal distribution of emotions, as depicted by the graph, with surprise represented in 430 posts, disgust in 232 posts, and neutral or ambiguous in 232 posts.
Figure 3 reveals that in the dataset of non-controversial artworks, 75 posts contain the emotion of surprise, 24 posts contain the emotion of disgust, and 327 posts contain no emotion.
According to the data, the emotion distribution in controversial works of art is more typical. However, in the non-controversial works of art, the two most intense emotions, namely surprise and disgust, are significantly less frequent than in the first set of data, and the neutral emotion predominates. This may be due to several factors, but a review of the literature on the effect of art indicates that the inherent qualities of pieces, such as those found in controversial works of art, may be responsible for evoking strong emotions. Regarding the non-controversial works of art, which neither provoke nor possess particularities, this may also explain why attendees exhibited no emotion. In addition to the emotions, the graph below displays the score distribution for the top three works of art. The scores in the table are the result of adding all post-ratings for each piece of artwork. The total number of scores in the first dataset of the three most popular controversial artworks is greater than that in the second dataset of the three most popular non-controversial artworks, as indicated by the mean value in
Table 8. This does not occur at all values and has no effect on the result, as it is a result of and dependent on the amount of data.
As explained in the experiment, this also proves the premise that controversial artworks may elicit more intense aesthetic emotions, such as disgust or surprise, than non-controversial artworks. The potential for an artwork’s qualities and the intense emotions it evokes to increase its popularity through social media engagement is a further argument in favor of the concept. As we observed, viewers were more surprised by the first scenario than the second. Based on extant research and previous experiments, it is highly likely that this is due to the inherent qualities and meaning of the artworks. Even though the qualities of non-controversial artworks were not as distinct as those of controversial artworks, observers did not experience strong emotions when viewing them. The present thesis was tasked with identifying the relationship between aesthetic experience and the popularity of art through a series of machine learning experiments. This final experiment confirms this link.