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The personalization of culture: the relationship between platforms and audiences
One of the most significant aspects in tandem with users in the age of Al and digital platforms is the personalization of popular culture and news. Regardless of different platforms, whether social media platforms or OTT service platforms, personalization has been significant, as individuals are considered a valuable entity for digital platforms and cultural industries corporations. While several different interpretations can be possible, I mainly divide the personalization of popular culture into two different perspectives: one emphasizing the role of major platforms and the other focusing on the customers, both of which are closely connected.
Most of all, the personalization of culture and information can be driven by digital platforms to maximize their profits. For the Facebook model, users are categorized into several segments in order to be sold to advertisers. Personalization helps Facebook organize users according to their specific features, such as age, gender, ethnicity, and preferences. Facebook’s “like” especially plays a role as a personal recommendation. Whenever people click “someone’s post” or “post” their individual stories and comments, their Facebook friends are able to see them. Since October 2016, Facebook has also launched new features it believed would make the social network more useful in people’s everyday lives. Facebook added a recommendation tool that helps people discover new places, events, things to do, and services around people by drawing on their friends. If they find an event to attend, Facebook has made the ticket-buying process more seamless. And when it’s time to interact with a business, there are new call-to-action buttons you can use. For Facebook, everything is “one virtuous circle of family, friends, business, and ad dollars” (Wired, 2016). In a nutshell, personalization is the reason why so many people are attracted to Al-equipped digital platforms. As van Dijck et al. (2018,42) point out, “Customization and personalization also empower users as consumers and citizens, enabling them to quickly find the most attractive offer and the information they are interested in.” On the flip side of the same token, personalization is a major business strategy for digital platforms as one of the primary business models to garner profits.
In Korea, similar to the Facebook model, Naver—the country’s largest internet portal—has started to use its own Al-based personalization services
Personalization of culture in the Al era 107 for news and search since early 2018. Content consumption in Na ver has increased sharply because of applying its Al content recommendation algorithm AiRS (Al Recommender System) to Naver and line news services. According to Naver, the daily average page view of Al content exposed to Naver’s news edition increased by 69% in a year. The number of daily users in a handful of foreign countries increased by 176%. The same company plans to expand its personalization search to some users (Korea Tech Today, 2019).
More importantly, Naver’s news section has been organized by Al software since April 2018. The company decided which news appeared in what order in the news section. The news section appeared as part of the portal site’s home page but is set to be moved deeper into the site as it starts to shift to a simpler landing page. The news section displays trending news articles recommended by the company’s artificial intelligence software AiRS. Users can see different articles based on an algorithm that tracks the user’s news consumption pattern. Which specific article among many dealing with the same topic is also decided by AiRS. Naver’s main news service is composed of two sections: one where the user can view articles selected by the news outlets they follow and the other where AiRS shows personalized articles (Song, K.S.,2018). What Facebook and Naver drive is the personalization of digital content so that users are able to consume essential information and news according to their own preferences. As Humphreys (2018, 29) points out, “One of the primary critiques about social media use is that people are sharing mundane and meaningless information.” Therefore, the personalization of culture and information implies that Al provides necessary information and news that individual users want to see and read. Al-based personalization is to dramatically shift people’s use of digital platforms.
Meanwhile, for the Netflix model, users are able to be categorized by their tastes so that Netflix recommends similar cultural content to individual users. As Kopenen (2018) argues,
An intelligent notification system sending personalized notifications could be used to optimize content and content distribution on the fly by understanding the impact of cultural content in real time on the lock screens of people’s mobile devices. The system could personalize the way the content is presented, whether serving voice, video, photos, augmented reality material or visualizations, based on users’ preferences and context.
Al can offer manifold advantages to the way audiences as users find information and cultural content. Al-driven recommendation systems can identify the most relevant cultural content for a user, taking into account the context of cultural consumption. For example, a user may want to watch a different television drama while commuting to work than on a Sunday morning.
Personalized recommendations and programs “can also cater to different consumption habits related to our different social roles, as employees, family members and citizens” (Helberger et al., 2019, 11). New Al-driven tools can even adapt cultural content “to users and the context of use, and thereby increase the chance that the news users come across can be processed and is useful” (Helberger et al., 2019, 11). This means that new digital technologies, including machine-learning algorithms, have transformed power relations. Machine-learning algorithms backed by big data “are not merely used to normalize individual behavior but rather to predict patterns of a given group or population” (Bueno, 2020, 80). In other words, “Machine-learning algorithms do not operate based on a pre-given template that links a facial image to a concrete identity. Instead, machine-learning algorithms use statistical calculation in order to extract patterns from the training datasets” (Bueno, 2020, 80). Al-equipped digital platforms have controlled the vicious circle of cultural production, from production to consumption, to empower themselves more than individual users, and therefore, it is certainly a new type of contemporary digital capitalism, which utilizes not only digital technologies but also digital platform users as major sources for their revenues.
As discussed above, Al in tandem with digital platforms evidently develops the personalization of culture. What is interesting is that these Al-equipped digital platforms have advanced “personal culture,” compared to mass culture. From the consumers’ perspectives, this means that people consume media and popular culture personally instead of enjoying cultural content together at theaters and living rooms. Since digital platforms like Facebook and Netflix supported by Al and algorithms have recommended particular cultural content to individual users, these audiences consume popular culture personally and selectively, which characterizes contemporary cultural preferences. In the age of Al, again, many digital platforms, from social media platforms to OTT platforms, “aim for increased levels of personalization” (Pangrazio, 2018, 12). For example, “Netflix uses data analysis to predict audience behavior rather than to estimate the performance of a particular program. In this sense, Netflix is in the content personalization business” (Tercek, 2019). Other platforms have also advanced the personalization of culture. For example, Google serves up different search data to individuals by learning not only their most influential links, but the ones people, personally, might be looking for, based on what they have clicked on before. Of course, the dominant digital platform is the smartphone, “through which people create a personalized interface with the world, with their own personally curated set of apps.” People are constantly “having individual experiences when together, with life plus screen creating a personalized experience of everything” (Mawdsely, 2016). People’s cultural consumption in the Al-equipped digital platform era increasingly witnesses personalized experience.
Personal culture as one of the most distinctive characteristics of the contemporary cultural sphere refers to not only popular culture and media
Personalization of culture in the Al era 109 produced and recommended by Al-equipped cultural producers and digital platforms but also cultural consumption conducted individually on and through digital platforms, including social media platforms, such as YouTube and Facebook, and OTT service platforms like Netflix. Since Al and algorithms in the realms of popular culture and media have developed personalized recommendation systems as can be seen in Netflix and Facebook, personal culture implies the entire process of cultural production, distribution, and consumption in the age of digital platforms. As people prefer Netflix to home theaters and like smartphones over home telephones, people in the early 21st century have deeply advanced the personalization of culture in their consumption.
Of course, audiences’ practices are mostly driven by Al-equipped digital platforms. The personalization of culture is very important in enabling digital platforms to reach out to the local market and audiences, as they create relevant cultural content to keep their audiences satisfied through the personalization of culture. Although Al empowers audiences, content personalization technically empowers digital platforms with massive control. As Arnold (2018, 49) aptly puts it, “This represents a shift in audience measurement and interpretation from the notion of the personalized mass to the personalized, the individuated, and the autonomous.” In this regard, Ang (1991, cited in Arnold, 2018) claimed that the audience was consequently figured as “depersonalized” and “part of a whole,” but, paradoxically, a powerful mass that exercised relatively free choices (of limited content options) that were subject to later sampling and analysis. However, unlike this kind of interpretation, in the era of convergence of Al and digital platforms, audiences have experienced disempowering empowerment, and therefore, their participation as individual users is certainly exploited by mega digital platforms.