Defining Content and the Content Industry's Impact on Culture

Role: Principal Investigator

Timeline: 2015-2017 and 2018-2020

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Until the early 2000s, “content” was seldom used to describe journalism or creative work in fields such as film, photography, or literature. By the mid-2010s, it had become the dominant term for nearly everything circulating online. Drawing on insights from a two-year ethnography carried out working for  content farms, historical research, and theorizing on the culture industry, this study investigated how “content” emerged as the organizing logic of the digital era. I examined how this linguistic shift—reclassifying diverse genres and forms as “content”—reshaped communication practices, creative labor, journalism, and political life. Rather than treating content as a neutral descriptor, the study revealed how this reclassification promoted the rapid rise of a vast industry built on circulation, optimization, and visibility.

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Key Questions

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• What do we actually mean when we call something “content,” and how did this category expand so dramatically?

• Why did circulation—rather than meaning, craft, or quality—become the defining metric for value online?

• How have search engines, social media platforms, and optimization strategies reorganized cultural and political life?

• What happens to journalism, truth, and public knowledge when content farms, branded “thought leadership,” and troll factories operate alongside traditional news?

• How do creators, artists, and writers navigate a world where “content capital” increasingly outweighs cultural capital?

• How will the arrival of AI change the content industry?

 

Methodology and Rationale

 

The study started as an extended ethnographer of content farms (also known as content mills) carried out between 2015 and 2017. Over two years, I embedded myself in several different content farms, turning out content for as little as few cents per word on topics ranging from subliminal messaging to the benefits of dating farmers. The resulting study, "Ghostwriting for Algorithms," which I have not yet published, was eventually re-conceptualized as a book focused on the concept of content, which was solicited by The MIT Press for their Essential Knowledge series.  As a result, while this study was informed by my original ethnographic work, it would ultimately also draw on media history, political economy, platform and algorithm studies​, and cultural and information theory to expose the mechanisms—technological, economic, and linguistic—that transformed “content” from a minor descriptor into a powerful organizing principle in the early twenty-first century. 

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Study Design

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• Embedded industry experience: The study started with a sustained participant observation carried out producing content for content farms.  

• Etymological research: A etymology of "content" was carried out, with a focus on the term's circulation since the 1990s.

• Case study research: Case studies on content farms, SEO operations, influencer markets, pay-to-play journalism, and disinformation infrastructures are referenced throughout the study.

• Theoretical framing: The framework for the book relies on and updates key theoretical frameworks on the culture industry and knowledge economy and leverages structural analysis of algorithmic incentives and platform economics.

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Sampling

 

My empirical research drew directly from my ability to secure and complete work for multiple content farms, giving me firsthand access to the day-to-day mechanics of content production. To contextualize this embedded research, I examined a wide range of complementary sources, including case studies and platform infrastructures (YouTube, Instagram, Facebook, LiveJournal), content-creation pipelines and editorial protocols, disinformation and state-sponsored content operations, and public-facing industry materials alongside scholarship in media studies, STS, and political economy. Together, these sources provided a robust and diverse evidence base, allowing me to analyze the content industry simultaneously as a labor system and as a cultural and technological formation.​

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Screener Logic

 

This study sought to define the concept of "content." Ultimately, content was defined as any digital material whose primary value lies in its capacity to circulate. In practice, this means content is produced or shaped according to optimization demands; evaluated by engagement metrics rather than meaning; embedded in monetization systems; and defined more by its platform context than its intrinsic qualities. 

 

Bias and Validity

 

In phase 1 of the study (embedded research), ​I remained attentive to both the strengths and limits of participant observation. While performing low-paid content labor provided direct access to industry practices, it also risked over-representing one perspective on the content industry. To mitigate this, I triangulated ethnographic insights with secondary studies on digital platforms and industry reports. Validity was further strengthened through a multi-method design that combined participant observation, etymological and historical research, case studies of platform infrastructures and SEO operations, and analyses of influencer markets and disinformation systems. Sampling centered on digital environments shaped by circulation and optimization, with legacy media used as a comparative counterpoint. Another limitation of the study was its focus on the U.S. and English-language platforms from the mid-2010s to early 2020s.

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Key Insights

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• Content’s rise signals a structural shift: In today’s media economy, circulation increasingly outweighs meaning as the primary cultural and economic driver.

• A new form of capital is emerging: Social and cultural capital are giving way to content capital—a logic that reshapes what writers, artists, and media workers must do to stay visible.

• Content industries accelerate journalism’s decline: As local news infrastructures erode, content farms and automated production systems are reshaping the public information landscape.

• Media literacy alone is no longer enough: Even high levels of literacy cannot fully equip audiences to navigate an environment where the boundaries between news, advertising, and manipulation are increasingly blurred.

• AI amplifies these dynamics: Generative systems dramatically increase the volume, speed, and opacity of content production, accelerating the shift toward circulation-driven media and further destabilizing traditional informational gatekeepers.

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Impact

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This study, which culminated in the publication of Content, a book published in MIT's Essential Knowledge Series, and a series of related articles, provided scholars, technologists, policymakers, and the public with a framework for understanding how content is produced and when, why, and how the concept was adopted. The study has influenced academic research, policy debates, and public discourses on content. 

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