DC Elective 1: Social Media and the Attention Economy

Digital Communication Certificate · Elective 1

Social Media and the Attention Economy in Education

This elective covers how social media platforms design for engagement, what their AI recommendation systems reproduce, and how you teach students to analyze the platforms they use every day and to use them with critical awareness. Plan on 3 to 4 hours: about 2 on the activities and the builder, the rest reading.

What this elective asks you to do

Analyze the rhetorical and algorithmic features of one piece of social media content. (Activity 1)
Analyze a platform with the three structural questions. (Activity 2)
State an operative decision about the platform, grounded in your analysis. (Activity 2, prompt 6)
Design an assignment treating a platform as an object of analysis. (Activity 2 and the assignment builder)

Section 1

The Attention Economy

When a student's attention drifts from a reading to a phone, the student is responding to a system built to pull it there. Platform companies design their products, often with teams of behavioral scientists and machine learning researchers, to capture and hold attention. Notification timing, variable rewards, infinite scroll, and personalized feeds are deliberate design choices. Each is tuned to raise engagement metrics, and those metrics compete by design with sustained concentration, careful thinking, and deliberate reflection.

In the attention economy, attention is the commodity. Social media companies charge users nothing because they sell user attention and behavioral data to advertisers. Their designs aim to increase time on the platform and the data collected, and those aims often conflict with users' epistemic wellbeing. Moral outrage and novelty travel far on these platforms. Brady et al. (2017) found that each moral-emotional word in a message about a polarizing issue raised its diffusion by about 20 percent. Vosoughi, Roy, and Aral (2018) found that false news on Twitter spread farther and faster than true news, and that its novelty helps explain why people shared it.

What this means for your course

When you ask students for slow, deliberate thinking, you ask for the mode of attention these platforms are built to interrupt. Knowing this is the starting point for bringing social media into a course with enough critical awareness to teach students to use it well.

Section 2

Algorithmic Recommendation Systems as AI

The recommendation systems on social media platforms are AI systems. They use machine learning to predict which content a given user is most likely to engage with. Apply the AI literacy framework (Cole, 2026) to them and their structural dimensions come into view.

At the conceptual level, a recommendation system curates according to its training. It learns from engagement data (clicks, watch time, shares, comments) and is optimized to produce more of the behavior that generates that data. Accuracy, reliable sourcing, and relevance to a student's education sit outside that objective. Its outputs reflect the objective it has: engagement.

At the structural level, these systems reproduce the hierarchies encoded in their data, the pattern Noble (2018) documents in search algorithms. A system trained to maximize engagement learns to favor the content Brady et al. and Vosoughi et al. describe, so measured, qualified voices reach fewer people than inflammatory ones.

At the operative level, bringing social media into a course calls for a decision grounded in this structural analysis. What does it mean to require students to open accounts on platforms whose recommendation systems optimize for engagement at the expense of their epistemic wellbeing? What accountability conditions would make that requirement ethically defensible?

Section 3

Platform Literacy and Platform Fluency

Platform fluency means knowing how to use a platform's features well. Platform literacy means understanding what the platform does to the communication that passes through it. A student might be highly fluent in TikTok production (transitions, audio syncing, trending sounds, hashtag strategy) and still have no analytical account of how the algorithm distributes content, what data the platform collects, or how its design sets certain communicative norms as the default.

A platform-literate student sees the platform itself as a rhetorical and structural actor. It shapes what communication is possible, what gets amplified, who gets heard, and at what cost. Teaching social media literacy means teaching students to ask 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?

A course that brings in social media without platform literacy teaches students to use a tool while leaving its structural effects unexamined. This elective gives you enough platform literacy to teach it and to design assignments that build it in students.

Section 4

Social Media in Your Discipline

Every discipline has some relationship to social media, as a site of public communication, a source of misinformation about the field's findings, a channel for researchers, or a professional expectation. Scientists use Twitter/X to share findings with public audiences. Public health researchers track misinformation on social platforms. Journalists produce content for social distribution. Educators build professional learning communities on LinkedIn or Bluesky. Start with how your discipline uses social media. Assignments built from those uses develop the specific social media literacy your field requires.

Activity 1

Rhetorical Analysis of Social Media Content

Individual analysis · 45–60 minutes

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

What a complete response does

Identifies the platform, format, and creator.
Names the claims the content makes and the choices carrying them.
Explains why the recommendation system promotes the content.
States one use of the content in your course.

Prompts marked * need at least one full sentence each to submit the elective.

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, such as evidence, emotional appeals, format, and visual elements, and what makes it persuasive or credible to its intended audience.) Algorithmic analysis: What about this content makes the algorithm likely to promote it? (Consider emotional valence, fit with the platform's format, shareability, and performance on the platform's engagement metrics. Does its shareability serve or compromise its accuracy and qualification?) 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 in it?)

Contribute to the repository

Analyses from your discipline, especially those that show how algorithmic amplification affects disciplinary content, help build a cross-disciplinary resource for teaching platform literacy.

Yes, share my analysis with the C²TC repository. CWSP reviews each item before posting it and credits you by name and department unless you ask otherwise.

Activity 2

Social Media Platform Decision

Structural analysis + operative decision · 60–75 minutes

Choose one social media platform you use or are considering for your course, whether for student communication, assignment submission, professional community, or content analysis. Apply the AI literacy framework to its recommendation system. Then make an operative decision about whether and how to use it.

Structural analysis

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

What a complete response does

Answers all three structural questions with evidence from the platform.
Names one operative decision and ties it to the analysis.
Designs one assignment applying the structural questions.

Prompts marked * need at least one full sentence each to submit the elective.

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 accountability conditions must be in place? If deferred, what evidence do you need?)

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 practiced here to a platform relevant to your discipline. Include the task prompt and the assessment criteria. (Aim the assignment at platform literacy.)

Contribute to the repository

Critical social media assignments that apply structural analysis, especially from disciplines outside communication, add to the C²TC repository's resources for teaching platform literacy.

Yes, share my platform decision and assignment design with the C²TC repository. CWSP reviews each item before posting it and credits you by name and department unless you ask otherwise.

Your Artifact

Build Your Platform Literacy Assignment

Complete the fields below to generate a formatted, print-ready assignment sheet built on your rhetorical and algorithmic analysis from Activities 1 and 2. You leave with a document ready to use in your course.

What a complete response does

Frames the platform as an object of analysis.
Sets limits on platforms and content in scope.
Gives three to five criteria, each describing strong work.
States an AI use policy with its teaching reason.

Prompts marked * need at least one full sentence each to submit the elective.

Assignment title (Specific to your discipline and audience. Name the rhetorical and analytical task in the title.) 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 itself as an object of analysis.) 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 guide 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 are permitted to use AI tools for this assignment, and give the teaching reason for that policy.)

Key readings and resources

★ Cole, K. K. (2026). AI literacy is not one thing: Conceptual, structural, and operative levels for higher education governance. Pedagogies: An International Journal, forthcoming. Framework: 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). Gamification makes writing fun. In C. E. Ball & D. M. Loewe (Eds.), Bad Ideas About Writing. WVU Libraries. A critique of gamified design, relevant to how platforms build engagement into their structure.
Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313–7318.
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.
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.
Writing Spaces, Vol. 3 (writingspaces.org). Chapters on social media, digital audiences, and algorithmic writing environments.

Where you will use this work

Name the course and term where you will use what you built in this elective, if you know them. (Optional. CWSP sends one question near the end of that term.)

Four questions about this elective

Your answers here go to CWSP without your name or email, apart from your elective responses. CWSP reads them to revise the elective. They are optional, and they send when you submit.

The readings and activities applied to a course I teach. The instructions for each activity were clear. About how many hours did the elective take you? What one change would make this elective more useful for your teaching?

Complete Elective 1

When you have finished both activities, submit your responses. Your work is auto-saved in your browser.

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