MECO6936 Social Media Communication
Name: Yuan Liu
Tutor: Jennifer Hagedorn Tuesday 8AM – 11AM
In the advent of Web 2.0, online social media has burgeoned and spread at a rapid speed. The need for cultural intermediaries also grew along with the demand for translation between cultural capital to economic capital by the audience and the companies.
As more and more people gradually have access to the internet, the data they created also increased at an astonishing speed. In order to adapt to this fast-paced development on the internet and social networks, the workload faced by companies and cultural intermediaries is increased too. It became hard for Traditional cultural intermediaries to handle the work because they can no longer handle these works by themselves manually 24 hours a day. People need a new way to process the huge amount of data creating by the users. Therefore, the algorithms, which can help people to categorize and statistic data, became to play an increasingly important role in social media and on the internet as cultural intermediaries.
What is cultural intermediary?
Lots of experts gave their definition to Cultural
intermediaries. In class, the video from Jonathon Hutchinson mentioned that “Cultural
intermediaries are the agents who are located in the middle of cultural
production. They are the agents who facilitate the process of co-creative media
production.” Adkins (2011) explain the concept of cultural intermediary in her article Cultural intermediaries she
explained that cultural intermediary refers to ‘those sets of occupations and
workers involved in the production and circulation of symbolic goods and
services in the context of an expanding cultural economy in postwar Western
societies.’ As Bourdieu (1986) mentions in his
book, cultural capital could be translated into other forms of capital. From
this perspective, Hutchinson (2017) gave the definition of cultural intermediaries’
that cultural intermediary is the capital translators who have the abilities to
understand messages from producers and deliver them to its audience effectively.
In all, cultural intermediaries are the agents between institutions,
organizations and the
Figure1 examples of cultural intermediary. (Hutchinson, 2019)
What is algorithm?
The notion of algorithm in mathematics can date back to ancient Greek (Cooke, Roger L. 2005). For algorithm in computer science, it can date back to decades ago. Rogers (1987) once explain it that “an algorithm is a procedure for computing a function (with respect to some chosen notation for integers) “. While Striphas explains it as “the sorting, classifying and hierarchizing of people, places, objects and ideas” in the computational process.” (Striphas, 2015). In all, the algorithm is a series of actions, such as identifying and categorizing the content, taken by computer based on the data provided by users in order to achieve a certain purpose.
Figure 2 example of recommended algorithm. (Nykjaer, 2019)
How it works in social media?
In our daily use of internet or social media platform, the most common use of the algorithm is the content recommendation. The algorithm helps companies and institution to process the huge amount of data they collected from the users. In the perspective of the algorithm. It creates algorithmic identities for all users. As Cheney-Lippold (2018) put it “In other words, online you are not who you think you are.” Then, based on these algorithmic features, it calculates and analyzes the pattern of the users’ viewing history and then send relevant content to each user. In this way, there is a great chance that their user would prefer and accept the recommended contents to them. By applying the algorism in the social media platform, “intermediaries can have a new and nuanced view of cultural habits occurring across communication environments. in front of the algorithm.” (Cheney-Lippold, 2018). The way the algorithm works as a cultural intermediary is similar to human cultural intermediaries. As Hutchinson (2017) mentioned, the algorithm first helps quantitative social network analysis (SNA) by capturing the data from the internet. Then it helps to clean and visualize the data. Based on these data, the algorithm can calculate the final result according to different purposes and different targets.
Case study about how it works.
Nowadays, nearly every social media platform recommend content based on their algorithm. The platform will recommend relevant content according to your viewing history and those content you spent more time on them. Sometimes, they can even guess the potential content which you might prefer based on their algorithm. Take Instagram, the primary focus in our welcome week campaign, as an example. In its searching page, Instagram will show all the pictures and short videos you might like based on user’ viewing and searching history. Here, the algorithm works as the agent. It finds the cultural productions which it considered would be an appeal to you and translate it into economic capital. In another way of speaking, the content that the algorithm provides to you is the advertisements.
Figure3,4 examples of the different recommended content on Instagram
How the algorithm as cultural intermediary applies in our campaign?
So, follow this idea, in our welcome week campaign, #youtoohavethepower, we use the recommended algorithm as one of our ways to reach more people. In the campaign, “the welcome week of the USyd” is one keyword for the social media platform so that everyone, who search for this topic, can reach to the content. Also, we add popular superhero element to our campaign as our main theme, according to the big heat of Marvel and DC movies. The algorithm here on the social media platform will play the role of our agent who advertises our cultural content to the audience. Once the algorithm spots someone who is interested in superhero content or our keyword, it will automatically recommend our content to the audience’s list of recommendations. In this way, the algorithm plays the role of cultural intermediary as it works as the agent who helps us find our main audience and find out what content our audience preferred.
The algorithm as cultural intermediaries is necessary and crucial in today’s society because the human cannot deal with those huge amounts of data in such short and limited time. The efficiency algorithm provides is irreplaceable, and as the development of algorithm and internet, the algorithm as cultural intermediaries has deeply rooted in our daily life.
However, even using algorithm is now one of the most effective ways to disseminate the cultural content and to translate cultural capital into other forms of capital, it still has potential risk for the society and for the audience. As Striphas (2015) points out, “culture and the media systems that create, distribute and perpetuate an algorithmic culture are at risk of limiting public culture in favor of an elitist cultural re-appropriation.” Furthermore, due to the imperfectness and incompleteness of the algorithm, it may cause a recession of cultural multiformity. For example, the famous app Tiktok has faced a similar problem. Once their user viewed a certain type of video, the algorithm will recommend a similar type of content to him or her endlessly. In the end, it will develop like a snowball that user can only see one type of content.
In the future, I believe the algorithm will continue to be the major cultural intermediaries in our social media platform and on the internet. However, what difference is that it will become more intelligent as the development of big data science and other High technologies such as artificial intelligence and quantum computer. And the most important thing is that it will serve the users well in a more mature and humanizing way.
Adkins, L. (2011). Cultural intermediaries. In D. Southerton (Ed.), Encyclopedia of consumer culture(Vol. 1, pp. 389-391). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781412994248.n147
Bourdieu, P. (1986). The Forms of Capital. In R. J. G., Handbook of Theory and Research for the Sociology of Education (pp. 241–258). New York: Greenwood Press.
Cheney-Lippold, J. (2018). We Are Data: Algorithms and the Making of Our Digital Selves. European Journal Of Communication, 33(1), 110-110. doi: 10.1177/0267323117753751c
Hallinan, B., & Striphas, T. (2014). Recommended for you: The Netflix Prize and the production of algorithmic culture. New Media & Society, 18(1), 117–137. doi:10.1177/1461444814538646
Hutchinson, J. (2017). Cultural Intermediaries. [S.l.]: Palgrave Macmillan.
Hutchinson, J. (2019). The role of the Cultural Intermediary within professional participato…. Retrieved from https://www.slideshare.net/JonathonHutchinson/iamcr2013-hutchinson
Maguire, J., & Matthews, J. (2012). Are we all cultural intermediaries now? An introduction to cultural intermediaries in context. European Journal Of Cultural Studies, 15(5), 551-562. doi: 10.1177/1367549412445762Nykjaer, K. (2019). How does the Amazon recommendation system work? – Analyze the algorithm and make a prototype that visualizes the algorithm. Retrieved from https://kunuk.wordpress.com/2012/03/04/how-does-the-amazon-recommendation-system-work-analyze-the-algorithm-and-make-a-prototype-that-visualizes-the-algorithm/
Striphas, T. (2015). Algorithmic culture. European Journal Of Cultural Studies, 18(4-5), 395-412. doi: 10.1177/1367549415577392