The shapes of exploitation in the digital industry: the new protagonism of the user exploitation

Web 2.0

The internet appeared in the 60s as military communication technology, therefore, not accessible by civilians. Its opening began after the fall of the Berlin wall, starting to massify in the 90s thanks to the emergence of intent browser’s and free email services. This massification led to the insertion of the big brands on the internet, giving birth to the digital industry, the so called Web 1.0.

The Web 1.0’s mainly attribute was the “static” content generation, in other words, the content of a website was produced and published by the same company / organization / person that owned it. The users were limited to only navigate. Because of this format, there was a direct relation between the amount of content a website could offer and the amount of workers dedicated to that function in the company.

Despite numerous attempts to vary the business model, the revenue of the digital industry have always been mainly by the exposure of advertising (banners, pop ups, paid articles, etc). This means that, the longer the users stay on a given website, the bigger is their exposition to advertisement and, therefore, the more profitable is the business. In order to increase the time spent by the users, the only possible solution was to increase the amount of content offered by the website. In the Web 1.0’s model, the production rate just wasn’t enough to generate the speculators’ expected profit.

This was the context that gave birth to the Web 2.0, characterized by the accessibility of the content production and publishing that made possible the emergence of user-generated content. The biggest examples of the Web 2.0 are the social networks (Facebook, Instagram, etc), in which the companies are dedicated exclusively to the maintenance of the structure where users work to generate content to other users [1]. In this way, it is possible to have a massive, unpaid and not even recognized workforce that enables the startups’ “miracle”: companies with an extremely lean staff that generate huge profits.

YouTube is a good example of this dynamic because it’s a website for sharing videos, a media whose production wasn’t much accessible to the majority of the population until recently. Because of this, not all users are content producers. There are two main profiles among YouTube users: those who only watch videos and those who watch and produce videos. While the first type of users have a relation with the website that is close to the Web 1.0, users that produce content are inserted in the Web 2.0 dynamic: they make the product that the website offers. Without them, the users have no reason to access it. This labor relationship in the case of YouTube is so evident that some content producers are paid for the work, even though it is so small that it can not be a major source of reliable income.

The social networks (for example, Facebook) have a slightly different dynamic as it’s so much easier to make new content for them. There, all users produce content without the need of financial incentive and, therefore, the remuneration of the site is even more scarce, restricted only to the most accessed producers. There are content creators that make a living out of it (the so called “influencers”), however, they are not paid by the company but by external advertisers.

Despite numerous attempts to vary the business model, the revenue of the digital industry have always been mainly by the exposure of advertising (banners, pop ups, paid articles, etc). This means that, the longer the users stay on a given website, the bigger is their exposition to advertisement and, therefore, the more profitable is the business. In order to increase the time spent by the users, the only possible solution was to increase the amount of content offered by the website. In the Web 1.0’s model, the production rate just wasn’t enough to generate the speculators’ expected profit.

This was the context that gave birth to the Web 2.0, characterized by the accessibility of the content production and publishing that made possible the emergence of user-generated content. The biggest examples of the Web 2.0 are the social networks (Facebook, Instagram, etc), in which the companies are dedicated exclusively to the maintenance of the structure where users work to generate content to other users. In this way, it is possible to have a massive, unpaid and not even recognized workforce that enables the startups’ “miracle”: companies with an extremely lean staff that generate huge profits.

YouTube is a website for sharing videos, a media whose production, until recently, was inaccessible to the majority of the population. That characteristic makes it easier to observe the mentioned dynamic. There are two main user profiles on  YouTube: the ones who only watch videos and the ones who watch and make videos. While the first type of users have a relation with the website that is close to the Web 1.0, users that produce content are inserted in the Web 2.0 dynamic: they make the product that the website offers. Without them, the users have no reason to access it. This labor relation, in the case of YouTube, is so clear that there is a group of content creators that are really paid and can make a living out of it (referências gringas).

The social networks (for example, Facebook) have a slightly different dynamic as it’s so much easier to make new content for them. There, all users produce content without the need of financial incentive. Therefore, even the more accessed ones don’t receive any wage. There are content creators that make a living out of it (the so called “influencers”), however, they are not paid by the company but by external advertisers.

User management

In that structure, the company workers are responsible for maintaining the technology that make the website or app possible, selling the advertising spaces, acquire new users and manage them.

The user management is made by a multidisciplinary team of designers, programmers and quality, business and product analysts. The starting point, in the same way as in a factory, is the definition of productivity metrics and goals. In the case of a social network, the final goal is to make sure that players access it the maximum possible number of times for a significant period of time, so that they are exposed to a certain amount of advertisements. In order to accomplish that, it’s necessary that the users produce content and interact with the content made by other users. All accesses, interactions and posts are counted to establish and track these metrics, so it’s very easy to make modifications and test which ones are more efficient in promoting the desired user.

Even the minimal decisions are made based on these numbers. Using Facebook as an example, from the buttons on the top of the website, made to prioritize users actions, the step-by-step of creating a new account, made to make sure that users finish the process with a reason to come back again [2] (called “hook” [3]), to the definition of new features, like the different varieties of “likes” made to encourage a quick interaction even when the user doesn’t have time to create content and the colorful boxes for short texts that make it easier to create content with better visual appeal.

Besides that, in the specific case of mobile apps, other resources can be used to help with this management like, for example, GPS location, shortcuts for sharing images and vídeos and the notifications, which are the most powerful way to bring users back to the app many times a day.

The massive and extremely diverse quality of the users in these websites and apps makes it very hard to predict the impact of changes made by the developers on the one hand. However, on the other hand, this enables countless tests, validations, analysis and segmentations, increasing the control and predictability of the changes. Two very powerful tools in that matter are A/B tests and personalization algorithms.

A/B tests are literally experiments made in the website or app [4]. They work as follows: let’s suppose that, hypothetically, the Facebook team wants to increase the number of  posts made by the users in a day. For that, they believe that changing the message in the box where users write can bring positive impacts. Many different messages are created apart from the traditional “Post a status update” like, for example, “What are you thinking about? “ or “Tell your friends something”. After coming up with possible messages, part of the user population is divided in groups that will see different versions: group A will see one, group B, another, group C, another, and so on. After a while, an analysis is made to check which group had the greatest growth in the number of posts and the winner version becomes the official until another test is made.

Personalization algorithms, on the other hand, are automatic ways of adapting the app or website to a specific user’s behaviors and interests [5]. One example that’s very discussed is the one that measures their interaction with posts and priorities posts from pages and people they interact more. That way, after a while, posts from people or pages that the user does not comment or enjoy often just stop being exposed to them.

That are countless examples of how these practices are being developed to increase the productivity of our free time, I’ve just showed some to i expose that, from the perspective of the digital giants, this labor relation of users is already clear. Due to the extremely high financial success of these practices, they tend to spread to all other areas of work and life. The “revolution” brought by the startup mentality consists of questioning how is it possible to transfer work to outside of the company using digital technologies, thus ensuring a leaner company staff and a productivity as never seen before.

References:

[1] Web 2.0 – https://web.archive.org/web/20121025113431/http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2125/1972

[2] Facebook’s Aha Moment Is Simpler Than You Think – https://mode.com/blog/facebook-aha-moment-simpler-than-you-think

[3] The Psychology Behind Why We Can’t Stop Messaging – https://www.nirandfar.com/the-psychology-behind-why-we-cant-stop-messaging/?utm_source=Chameleon&utm_medium=blog

[4] Why You Should Follow Facebook And A/B Test Everything – https://auth0.com/blog/why-you-should-ab-test-everything/

[5] Your Feed is All You: The Nuanced Art of Personalization at Facebook – https://www.vice.com/en_us/article/d7ywxa/facebook-newsfeed-personalization-hussein-mehanna

By Heloisa Yoshioka – Women, Brazilian migrant in Berlin, revolutionary militant, feminist, anti-racist, crybaby, game developer and anguished worker of the digital industry. Part of Revista Amazonas and Quilombo Invisível.

Leave a Reply

Your email address will not be published. Required fields are marked *