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Digital Citizenship 101: Digital Oligarchy, Network Effect, and the Filter Bubble
Social media have become part of our daily life, they allow us to keep in touch with friends and family, develop new interests, and hear about what’s going on in the rest of the world in no time. But are we using them consciously?
My 10-year long experience as a fellow social media addict has inspired me to think we need to educate people to have a more conscientious online presence. So, after conducting a (very extensive) research for an academic paper, I decided to summarize some concepts that I think could help you develop a more critical viewpoint on social media.
Disclaimer: this won’t make you any less of an addict.
First of all, the social media industry is an oligopoly. You think the market is super diverse, the choice is soooo wide and you feel like you can barely handle all the available channels, but that’s just the surface: most of them belong to the same companies.
Take a look at this Hootsuite report
What does it show? A bunch of social media platforms and their active audience (in millions), and besides the outstanding results achieved by the podium (Facebook, Youtube, and WhatsApp), the others seem to be quite evenly distributed. This graph apparently supports the idea of a plurality of platforms.
Now, look at this other graph from Statcounter (2020).
This graph reports data per owning company. Each line represents a company, and there are very few lines because each company holds multiple platforms, meaning it wouldn’t be inaccurate to describe the industry as an oligopoly.
Nonetheless, this information should not come as a surprise: social media platforms are also known as social networks, and as networks, they are not immune to the economic phenomenon known as Network Effect. The Network Effect theory holds that the value a certain user acquires by joining a network grows proportionally to the number of actors who also do so; the more people join a platform, the more valuable will the experience be for every single user (Uzzi, 1996). Therefore, it is in the best interest of consumers to concentrate their activities in few, densely populated, platforms rather than scattered around on different ones.
Think of it this way: would you rather go to a party where there’s literally everyone, including your friends and that crush of yours, or to a party with 5 strangers?
But here is where this gets tricky (and very controversial). On one hand, the oligopoly is the manifestation of consumers’ interests and displays their satisfaction; but on the other hand, you end up in what we call the “filter bubble”.
The filter bubble is a concept coined by activist Eli Pariser and refers to the phenomenon resulting from the exposition to personalized online content based on the user pre-existing preferences and searches (Gambetta, 2018). From the user’s perspective, the filter bubble seems like a positive feature of the experience since they will be receiving more suggestions related to their pre-established taste; however, with these advantages, a problematic aspect arises too. If the user is exclusively exposed to their existing preferences and tastes, they will hardly get in touch with users expressing different opinions, or companies and institutions showcasing contrasting beliefs and values. Eventually one’s learning experience is significantly limited, and the pre-existing beliefs are strongly reinforced to the point that the user can hardly distinguish opinions from facts, and actual facts from false information reported as fact.
Research has highlighted that false information, also known as fake news, reaches greater audiences compared to actual facts, which is kinda crazy if you think about it. It’s like when someone spreads a fake rumor about you in high school and the whole school hears about it, but when something really cool actually happens to you no one cares.
Thanks to this research the need for online content to be fact-checked has increased significantly (Thorne and Vlachos, 2018). Since March 5, 2020, Twitter Inc. has introduced a fact-checking technology that highlights tweets (including any media content attached) which has been manipulated or distorted (Porro, 2020), and Facebook adopted the same policing aided by both technological and human efforts to proof-check the information published on the platform (Facebook, 2020).
To recap:
- The phenomenon of social media constitutes an oligopoly and that this is the result of the economic phenomenon known as Network Effect.
- The Filter Bubble phenomenon enhances the aggregation of like-minded individuals that are hardly exposed to diverging ideas and beliefs.
- These considerations, along with the great extent of popularity and use of these platforms, have contributed to render this oligopoly hard to regulate for governmental institutions.
- Their online nature enables Big Tech companies to exist beyond national borders and exploit their nature of privately-held companies to further complicate the imposition of governmental regulations which are instead bound to geographical borders.
So, what can we do to be more conscientious about our online experience?
In my opinion, a great first step would be exposing ourselves to content that contrasts with our viewpoint. Understanding how the algorithms function helps us explain what is being shown to us and why, whose target are we in, and who is speaking to us the loudest.
I think filter bubbles are like a matryoshka and the work is not done by popping your bubble once. This process of acquiring conscientiousness and responsible online behavior takes curiosity, and curiosity takes humbleness. Empathize with those who feel different on certain topics and hear their voices. Remember that not all control is gone, and it is still up to us who to follow and what conversations to engage with.
And what do you think can help us become more conscientious online citizens?
-Federica Petronelli
Bibliography:
Kemp, S., 2020. More Than Half Of The People On Earth Now Use Social Media. [online] Hootsuite. Available at: <https://blog.hootsuite.com/simon-kemp-social-media/> [Accessed 23 January 2021].
StatCounter Global Stats. 2021. Social Media Stats Worldwide | Statcounter Global Stats. [online] Available at: <https://gs.statcounter.com/social-media-stats> [Accessed 23 January 2021].
Uzzi, B. (1996). The sources and consequences of embeddedness for the economic performance of organizations: The network effect. American sociological review, 674-698.
Gambetta, D. (Ed.). (2018). Datacrazia: Politica, cultura algoritmica e conflitti al tempo dei big data. D editore. 369-370
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151.
Porro, G., 2020. Twitter Mette Al Bando I Deepfake – Wired. [online] Wired. Available at: <https://www.wired.it/internet/social-network/2020/02/05/twitter-deepfake-vietati/> [Accessed 23 January 2021].
Facebook.com. 2021. How Is Facebook Addressing False Information Through Independent Fact-Checkers? | Facebook Help Centre. [online] Available at: <https://www.facebook.com/help/1952307158131536> [Accessed 23 January 2021].
Thorne, J., & Vlachos, A. (2018). Automated fact checking: Task formulations, methods and future directions. arXiv preprint arXiv:1806.07687
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