DC Elective 1: Social Media and the Attention Economy

Digital Communication Certificate · Elective 1

Social Media and the Attention Economy in Education

How social media platforms engineer engagement, what their AI recommendation systems reproduce, and how faculty can teach students to analyze and use social media as a critical practice rather than an invisible infrastructure. Estimated time: 3–4 hours.

Section 1

The Attention Economy

Your students are not distracted by social media. That framing reverses the causal structure. Social media platforms are engineered — by large teams of behavioral scientists and machine learning researchers — to capture and hold human attention. Notification timing, variable reward ratios, infinite scroll, and algorithmically personalized feeds are not features that happen to be engaging. They are optimized to maximize engagement metrics that, by design, compete directly with sustained concentration, nuanced thinking, and deliberate reflection.

The attention economy is the economic system in which human attention is the commodity being bought and sold. Social media companies offer services free of monetary charge because their product — what they sell to advertisers — is user attention and behavioral data. Platform design is optimized for time-on-platform and behavioral data collection, not for users' epistemic wellbeing. These are frequently in tension: outrage, novelty, and social validation reliably outperform nuance, qualification, and careful argument in algorithmic distribution systems.

The pedagogical implication

When faculty ask students to engage in slow, deliberate, nuanced thinking, they are asking for exactly the cognitive mode that social media platforms are engineered to interrupt. Understanding this is not an argument against using social media in education. It is the prerequisite for using it with enough critical awareness to teach students to navigate it well.

Section 2

Algorithmic Recommendation Systems as AI

Social media platforms' content recommendation systems are AI systems — specifically, recommendation engines that use machine learning to predict which content a given user is most likely to engage with. Applying the AI literacy framework (Cole, 2026) to these systems reveals their structural dimensions.

At the conceptual level: recommendation systems are not neutral curators. They are trained on engagement data — clicks, watch time, shares, comments — to maximize the behaviors that generate that data. They are not trained to surface the most accurate content, the most reliable sources, or the information most relevant to users' educational needs. Their training objective is engagement maximization, and their outputs reflect that objective.

At the structural level: these systems reproduce the hierarchies encoded in their training data. Content from accounts with existing large followings gets amplified more readily than content from new accounts — which advantages established voices over emerging ones. Emotional and partisan content reliably outperforms careful, qualified communication in engagement metrics — which advantages inflammatory voices over measured ones. The students most likely to encounter misinformation are those whose platform behavior patterns and social networks have been identified by the algorithm as receptive to it.

At the operative level: deploying social media in educational contexts requires a decision grounded in this structural analysis. What does it mean to require students to create accounts on platforms whose recommendation systems are designed to optimize for engagement at the expense of their epistemic wellbeing? What accountability conditions would need to be in place for that requirement to be ethically defensible?

Section 3

Platform Literacy vs. Platform Fluency

Platform fluency — knowing how to use a platform's features effectively — is not the same as platform literacy. A student can be highly fluent in TikTok production (knowing how to use transitions, audio syncing, trending sounds, and hashtag strategy) while having no analytical understanding of how the platform's algorithm distributes content, what data it collects, or how its design encodes specific communicative norms as default.

Platform literacy is the capacity to see the platform itself as a rhetorical and structural actor — not just as a channel for communication, but as a system that shapes what communication is possible, what gets amplified, who gets heard, and at what cost. Teaching social media literacy means teaching students to ask the structural questions: What kind of AI system is this recommendation engine? What does it optimize for, and what does that optimization reproduce? Who built it and whose interests does the design serve?

Faculty who incorporate social media into their courses without teaching platform literacy are teaching tool use without teaching the tools' structural implications. The goal of this elective is to give you enough platform literacy to teach it — and to design assignments that develop it in students.

Section 4

Social Media in Your Discipline

Every discipline has a relationship to social media — as a site of public communication, a source of misinformation about disciplinary findings, a tool for researcher communication, or a professional expectation. Scientists use Twitter/X to communicate findings to public audiences; public health researchers track misinformation on social platforms; journalists produce content for social distribution; educators build professional learning communities on platforms like LinkedIn or Bluesky. Understanding how your discipline uses and navigates social media is the starting point for designing assignments that develop relevant disciplinary social media literacy — not generic media literacy, but the specific literacy your field requires.

Activity 1

Rhetorical Analysis of Social Media Content

Individual analysis · 45–60 minutes

Select one piece of social media content relevant to your discipline — a viral post about a scientific finding, a thread by a researcher, a TikTok explanation of a disciplinary concept, a professional organization's account, or a widely-shared infographic. Analyze it as both a rhetorical artifact and as a product of the platform's algorithmic system.

Describe the content you selected. (Platform, format, creator/account, approximate reach/engagement, URL if available.) Rhetorical analysis: What claims does this content make, and how? (Examine the rhetorical choices — evidence, emotional appeals, format, visual elements — and what makes it persuasive or credible to its intended audience.) Algorithmic analysis: What about this content makes it likely to be engaged with algorithmically? (Consider: emotional valence, format for the platform, shareability, how it performs against the platform's engagement metrics — and whether its shareability serves or compromises its accuracy or nuance.) Pedagogical application: How would you use this content in your course? (As a positive model, a critical case study, or a prompt for analysis? What would you want students to examine rather than simply consume?)

Contribute to the repository

Discipline-specific social media analyses — especially those identifying how algorithmic amplification interacts with disciplinary content — help build a cross-disciplinary resource for teaching platform literacy.

I'd like to share my analysis with the C²TC repository.

Activity 2

Social Media Platform Decision

Structural analysis + operative decision · 60–75 minutes

Select one social media platform you use or are considering using in your course — whether for student communication, assignment submission, professional community engagement, or content analysis. Apply the AI literacy framework to that platform's recommendation system, then produce an operative decision about whether and how to use it.

Structural analysis

Apply the three structural questions to the platform's AI system

Which platform are you analyzing, and how are you currently or potentially using it in your course? What social hierarchies does this platform's recommendation system reproduce? (Whose content gets amplified? Whose communicative norms does the algorithm treat as engagement-worthy?) Who bears the costs when this platform's recommendation system fails? (Who is exposed to misinformation, harassment, or low-quality content? Are failure costs distributed evenly across user demographics?) Whose interests does the platform's design serve? (Who owns it, what is the financial model, and where does the platform's financial interest diverge from students' epistemic interests?)

Operative decision

Your principled decision about this platform in your course

State your operative decision: principled refusal, conditional adoption, or deferred decision — and ground it in the structural analysis above. (If conditional adoption: what specific accountability conditions must be in place? If deferred: what evidence is needed?)

Assignment design

Design one critical social media assignment

Design one assignment that treats a social media platform as an object of critical analysis — asking students to apply the structural questions you've practiced here to a platform relevant to your discipline. Include the task prompt and the assessment criteria. (This assignment should develop platform literacy, not platform fluency.)

Contribute to the repository

Critical social media assignments that apply structural analysis — especially from disciplines outside communication — help build the C²TC repository's resource base for platform literacy instruction.

I'd like to share my platform decision and assignment design with the C²TC repository.

Your Artifact

Build Your Platform Literacy Assignment

Complete the fields below to generate a formatted, print-ready assignment sheet grounded in your rhetorical and algorithmic analysis from Activities 1 and 2. This is the document you will walk away with — ready to deploy in your course.

Assignment title (Specific to your discipline and audience — not "Social Media Assignment" but something that signals the rhetorical and analytical task.) Course and audience (Course number and title, and the student audience.) Assignment overview (2–4 sentences for students describing the task and its purpose. Frame the platform as the object of analysis, not just the medium.) Task description (What will students produce, for what purpose, and under what constraints? Draw on your Activity 1 analysis to specify the platform context and analytical framing.) Platform and scope constraints (Which platforms are in scope? What kind of content — disciplinary, professional, public-facing? What criteria for selection?) Assessment criteria (3–5 criteria that capture quality of rhetorical analysis, algorithmic analysis, and pedagogical application. Write each as a positive description of what excellence looks like.) AI use policy (State how students may or may not use AI tools for this assignment — and what the pedagogical reason for that policy is.)

Key readings and resources

★ Cole, K. K. (2026). AI Literacy Model. cwspwolf.com/ai_literacy_unified.html. The source framework for Activity 2's structural analysis and operative decision. Read the structural literacy and operative literacy sections before beginning Activity 2.
★ Carter, G. M., & Matzke, A. (2017). The more digital technology, the better. In Bad Ideas About Writing. WVU Libraries. Foundational critique of uncritical technology adoption — the analytical basis for evaluating social media use in education.
Daniel-Wariya, J. (2017). Play is not the opposite of work. In Bad Ideas About Writing. WVU Libraries. Procedural rhetoric and platform architecture as rhetorical systems; directly applicable to analyzing how social media platforms design for engagement.
Noble, S. U. (2018). Algorithms of oppression. NYU Press, Chapters 1–2. Structural analysis of algorithmic systems and the hierarchies they reproduce — the methodological model for Activity 2.
Writing Spaces, Vol. 3 (writingspaces.org). Chapters on social media, digital audiences, and algorithmic writing environments.

Complete Elective 1

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