How to Set Up a Research Assignment About New Social Platforms: From Bluesky to Digg

Hook: Turn student frustration into rigorous, real‑world research

Instructors: if your students struggle to find clear, reliable ways to compare new social networks—because community posts are noisy, vendor claims are biased, and platform behavior shifts weekly—you need a repeatable assignment format that teaches critical evaluation and produces publishable insights. This step‑by‑step guide shows how to build a comparative research project that asks students to assess usability, moderation, and business models of emerging networks like Bluesky and the revived Digg (public beta in Jan 2026). You'll get templates, a ready‑to‑use rubric, ethical checkpoints, and strategies for 2026's fast‑moving social media landscape.

Why this assignment matters in 2026

Late 2025 and early 2026 taught instructors a hard lesson: platform features, moderation failures, and AI risks can reshape user flows overnight. For example, Bluesky introduced cashtags and live‑stream badges while seeing a near‑50% jump in U.S. installs after high‑profile content moderation controversies on competing networks. At the same time, Digg reopened to the public, removed paywalls, and positioned itself as a friendlier news aggregator. These shifts mean students can study real, consequential change—but only if their projects use consistent methods and ethical safeguards.

"Bluesky's downloads jumped nearly 50% after early January 2026 events, and Digg relaunched public beta with a paywall‑free model." — reporting from TechCrunch and ZDNet, Jan 2026

Learning outcomes (what students should demonstrate)

• Design a comparative research question focused on usability, moderation, or business strategy.
• Collect and triangulate qualitative and quantitative evidence from emerging platforms.
• Apply ethical research practices when observing, interviewing, or scraping public platform data.
• Produce a structured report, visualizations, and a presentation that supports clear recommendations.

Step 1 — Define the comparative scope and research questions

Start narrow. Comparative projects succeed when students compare specific features or outcomes across platforms, not the entire social web.

1. Pick two or three platforms: example pairings for 2026 — Bluesky vs Digg, Bluesky vs an X/X‑alternative, or Digg vs Reddit‑style aggregator.
2. Choose focus areas (pick up to three):
   • Usability — onboarding, discovery, content creation, accessibility.
   • Moderation — policy clarity, enforcement speed, appeals, transparency reports.
   • Business model — revenue sources, paywalls, subscriptions, ad models, and community funding.
3. Formulate precise questions. Good examples:
   • How do Bluesky and Digg guide new users through content discovery within the first 10 minutes?
   • What transparency mechanisms do each platform use to report moderation outcomes?
   • How likely is the platform’s current business model to generate sustainable revenue within three years?

Step 2 — Choose methods and metrics

Combine qualitative and quantitative methods so students learn triangulation. Here's a practical toolkit.

Usability methods

• Heuristic evaluation using Nielsen’s usability heuristics (adapted to social networks).
• Moderated usability tests: 5–8 participants, 3 core tasks (sign up, post, discover topic), timed sessions.
• SUS (System Usability Scale) survey for quantitative comparison.

Moderation analysis

• Policy mapping: collect written policy pages and produce a comparative table.
• Enforcement case study: track 5 recent moderation actions (publicly documented) and code outcomes.
• Transparency scoring: presence of reports, appeal paths, and public data exports.

Business model evaluation

• Revenue mapping: ads, subscriptions, donations, data licensing, partnerships.
• Competitive positioning: user acquisition tactics, growth numbers (e.g., Appfigures installs for Bluesky in early 2026), and risks.
• SWOT analysis and 3‑year revenue projection scenarios.

Step 3 — Build the assignment scaffold

Use a clear timeline and milestones. Example for an 8‑week module:

1. Week 1: Topic selection and research questions (group formation).
2. Week 2: Methods workshop—usability testing, ethics, data collection tools.
3. Week 3–4: Data collection (surveys, tests, policy collection); interim check‑in.
4. Week 5: Data cleaning, coding sessions, and preliminary analysis.
5. Week 6: Draft report due; peer review exchange.
6. Week 7: Finalize visuals, prepare presentation.
7. Week 8: Presentations and instructor grading.

Step 4 — Ethical and legal checklist (required)

Emerging platforms and live communities create real safety and legal concerns. Make ethics non‑negotiable.

• Human subjects: Submit assignment outline to your IRB or follow your institution’s low‑risk protocol. Require informed consent for all user tests and interviews.
• Platform terms: Check Terms of Service and API rules before scraping. Prefer official APIs and public posts only.
• Privacy: De‑identify data; avoid collecting sensitive content. If students encounter illegal material, instruct immediate reporting to platform safety teams rather than including it in assignments.
• Age restrictions: Do not recruit minors without guardian consent; treat accounts of minors as protected data.

Step 5 — Tools, templates, and data sources

Provide a toolbox so students spend time on analysis, not setup.

• Survey tools: Google Forms, Qualtrics, or Typeform with embedded consent statement.
• Usability testing: Lookback, Zoom screen recording, or open tools like OBS; use a shared task script.
• Data collection: Platform APIs where available; for Apps data, cite market trackers (Appfigures) for install trend context.
• Text analysis: Python (pandas + nltk), R (tidytext), or no-code tools for sentiment and topic clustering.
• Visualization: Tableau, Data Studio, or Python/Altair for reproducible charts.

Step 6 — A ready‑to‑use grading rubric (instructor copy)

Use this weighted rubric to ensure consistency across sections. Adjust percentages to match your course priorities.

• Research design and clarity (10%): Clear question, appropriate scope.
• Methodology (25%): Sound methods, justified sample, documented protocol.
• Data quality and analysis (30%): Evidence triangulation, reproducible steps, valid coding.
• Ethical conduct (10%): Consent forms, data protection, platform TOS compliance.
• Presentation and communication (15%): Clear visuals, concise report, actionable recommendations.
• Reflection and future directions (10%): Limitations acknowledged, predictions for platform trajectory.

Descriptor examples (scoring bands)

• 90–100 (Excellent): Methods fully reproducible; multiple data sources; strong links between evidence and claims.
• 75–89 (Good): Reasonable design; some gaps in triangulation or minor ethical clarifications needed.
• 60–74 (Satisfactory): Basic comparisons made; missing rigor in coding or weak justification of methods.
• <60 (Unsatisfactory): Poorly scoped; ethical issues or unreliable data handling.

Step 7 — Example assignment brief (copyable for syllabus)

Below is a compact brief you can paste into your LMS.

Assignment: Comparative platform study (group)

Deliverables: 12–15 page report, dataset/codebook, 10‑minute presentation, 2‑page policy appendix.

Focus: Compare Bluesky and Digg on usability, moderation transparency, and business model viability.

Requirements: 5 moderated usability tests per platform; collect and code 10 public moderation cases; map revenue streams with sources and citations.

Step 8 — Grading workflow for large classes

Scaling this assignment requires clear rubrics, peer review, and TA calibration.

• Use a single shared rubric in your LMS and have TAs grade identical sections of projects to build reliability.
• Schedule a peer review week where each group assesses two other reports using a simplified checklist (counts for 10% of grade).
• Provide a reproducibility badge for submissions that include code and raw datasets (encourage open science practices while respecting privacy).

Step 9 — Teaching notes: classroom activities and mini‑lectures

Embed short, focused lectures during the module:

• Week 2: How to run a 30‑minute moderated usability test (live demo).
• Week 3: Reading session on contemporary moderation incidents (use the Jan 2026 X/Grok case to discuss nonconsensual AI imagery and regulatory responses).
• Week 5: Data ethics clinic—how to anonymize screenshots and transcripts.

Step 10 — Example findings and discussion prompts (for class critique)

Give students a model of what strong insights look like. Example conclusions based on 2026 patterns:

• Usability: Bluesky’s live badges and cashtags improved niche discovery but increased onboarding complexity for new users; average SUS difference of ~8 points versus Digg in pilot tests.
• Moderation: Bluesky published clearer moderation documentation but struggled with scale after a surge in installs; Digg prioritized community moderation tools and transparency, reducing formal takedowns but raising concerns about bias in volunteer moderation.
• Business model: Digg’s paywall removal aims to grow active users quickly; revenue projections suggest a 2‑tier ad/subscription hybrid will be necessary within 18 months if user growth plateaus.

Advanced strategies for 2026 and beyond

Encourage advanced students to:

• Build automated monitoring dashboards that track policy changes and feature flags using public API endpoints.
• Use causal inference techniques to estimate the impact of feature launches (e.g., Bluesky’s cashtags) on engagement metrics, when data permits.
• Model moderation outcomes under different governance futures (centralized moderation, decentralized moderation, AI‑assisted moderation).

Pitfalls to avoid

• Comparing platforms on raw user counts without normalizing for active users or context (e.g., spikes from external news coverage).
• Relying solely on anecdotal evidence from public posts—always triangulate with structured tests or policy documents.
• Ignoring platform rules about automation and scraping; noncompliance can lead to account suspension or legal issues.

Actionable takeaways (give students confidence to publish findings)

• Start with a tight research question—less is more when platforms are changing fast.
• Mix methods: usability tasks, policy mapping, and simple quantitative signals (SUS, engagement rates) provide robust comparisons.
• Make ethics visible: require consent, anonymize, and document TOS checks in every submission.
• Use a clear rubric and incremental milestones to keep projects on track and reproducible; consider a stepwise grading plan similar to an editorial 30‑day blueprint to keep momentum.

Closing — Next steps for instructors

In 2026, platform churn and AI‑driven features will keep the field dynamic. This assignment template lets students do meaningful, publishable work while learning research rigor and digital ethics. You can copy the brief, reuse the rubric, and adapt the timeline for a shorter module. If you want, start next term by assigning students to track a feature rollout (like Bluesky’s cashtags or Digg’s paywall removal) and publish a one‑page policy brief for campus IT or student affairs.

Call to action

Ready to implement this in your course? Download the editable rubric, user‑test script, and assignment brief from our instructor toolkit (or email us to request templates). Try the assignment once, iterate based on student feedback, and share standout student reports with our learning community so other instructors can build on your experience.

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