Social Media Assignment: Track a Stock Conversation Across Platforms

Hook: Turn noisy social feeds into a rigorous data project

Students and instructors: tired of vague social-media assignments that end in screenshots and hot takes? This ready-to-use assignment template teaches you how to track a stock conversation across platforms, compare how cashtags behave, analyze sentiment, and measure information accuracy. Using real 2026 trends—like Bluesky’s new cashtags and LIVE badges and the surge in cross-platform attention after the X (formerly Twitter) deepfake controversy—this assignment trains critical thinking, practical social-listening skills, and reproducible data work.

Why this matters in 2026

Since late 2025 and into early 2026, social platforms have changed how retail stock discussions form and spread. Bluesky rolled out dedicated cashtags and LIVE indicators in early 2026 amid a growth in installs, and mainstream platforms continue to evolve in response to AI-driven content and moderation challenges. At the same time, marketing and research teams face tool sprawl and the risk of low-quality signals if they don’t design disciplined data-collection and validation workflows.

“Platform features and AI change how sentiment and misinformation spread—so students need hands-on, cross-platform analysis skills.”

Learning goals and outcomes

• Conceptual: Understand differences between platform affordances (cashtags, hashtags, replies, threads, reposts) and how they shape investor conversations.
• Technical: Collect and clean social data from Bluesky, X, and one mainstream platform (Reddit/StockTwits/Discord/Reddit), run sentiment analysis, and quantify information accuracy.
• Analytical: Compare sentiment distributions, temporal sentiment vs price, and misinformation prevalence across platforms.
• Ethical & Practical: Apply platform terms of service, minimize privacy risks, and produce reproducible notebooks and an ethics statement.

Overview of the assignment

Students will choose a publicly traded stock (ticker symbol) and track its conversation for a 2–4 week window across Bluesky, X, and one mainstream venue. Deliverables: a reproducible dataset, sentiment analysis code and results, a manual fact-check sample with accuracy metrics, visualizations, and a 1,500–2,000-word report interpreting findings.

Timeframe & group size

• Project length: 3–5 weeks
• Group size: 2–4 students (individuals acceptable for advanced courses)

Step-by-step assignment template

1) Define scope and hypothesis

Pick a stock and make explicit hypotheses. Examples:

• "Bluesky cashtag mentions will show more positive sentiment than X for tickers with retail interest."
• "Misinformation rate (false price claims, doctored charts) will be higher on X than on Reddit due to bot amplification."

2) Platforms and data sources

Collect posts containing the ticker cashtag (e.g., $TSLA) and relevant hashtags or keywords on each platform:

• Bluesky: cashtags and LIVE posts (new in 2026). Use Bluesky's official API or download via authorized tools. Note: Bluesky introduced specialized cashtags in early 2026, increasing visibility for financial conversations.
• X: cashtags and replies. Watch for AI-generated or manipulated content—X was center-stage in the 2026 Grok deepfake debate, which affected platform trust.
• Mainstream platform: choose one—Reddit (r/wallstreetbets, r/stocks), StockTwits (if available), or public Discord channels. Each has different moderation & structure.

Tip: Limit your timeframe (e.g., 14 days) and volume (max 5k posts per platform) to keep projects manageable and in compliance with API limits.

3) Ethical collection & compliance

1. Read and follow each platform’s Terms of Service and rate limits. Avoid scraping if the platform prohibits it.
2. Anonymize handles in published datasets unless accounts are public figures; remove private DMs and PII.
3. Document collection methods and timestamps in a README.
4. Include an ethics statement describing potential harms (market manipulation, doxxing) and mitigation steps.

4) Data cleaning and normalization

Normalize fields across platforms so you can compare apples to apples:

• common fields: platform, post_id, timestamp (UTC), text, user_followers (or proxy), repost_count, like_count, cashtags_found, links, media_flag
• expand shortened URLs for domain analysis
• extract entities (tickers, company names, price claims)

5) Sentiment analysis methodology

Design a two-tier sentiment approach:

1. Baseline lexicon method — fast, interpretable: Vader/FINBERT-lite for finance-relevant terms.
2. Transformer model — more accurate for context and sarcasm: fine-tune a RoBERTa or DistilBERT on a small finance-specific annotated set. Alternatively, use an API like OpenAI or Hugging Face Inference with a finance-tuned model.

Practical tips:

• Account for emojis, cashtags, and market slang (e.g., "to the moon", "bagholder"). Create a small glossary and map slang to sentiment signals.
• Implement a sarcasm detector: simple heuristics (excessive punctuation, quote marks) plus model features can help, but manual review remains crucial.
• Compute per-post sentiment, then aggregate daily averages and weighted sentiment (by engagement).

6) Measuring information accuracy

Accuracy measurement should combine automated detection and manual annotation:

1. Define falsifiable claim categories: price predictions, earnings leaks, executive rumors, doctored charts.
2. Randomly sample N posts per platform (e.g., 200 each) for manual fact-checking.
3. Annotators label each sample: True, False, Misleading, Unverifiable. Use a shared codebook and calculate inter-annotator agreement (Cohen’s kappa).
4. Automate detection of obvious false positives: check claimed prices vs historical tick data, validate screenshots via reverse-image search for doctored charts.

Report accuracy as percentages and include confusion matrices and examples of errors.

7) Bot and network analysis

Identify potential bot amplification and coordinated activity:

• features: high post frequency, low follower-to-post ratio, repeated text patterns
• network graphs: retweet/repost networks, mention graphs—compute centrality and identify super-spreaders
• compare bot prevalence across platforms; early 2026 concerns about AI manipulation make this especially important

8) Visualizations & comparative metrics

Create clear cross-platform visuals:

• time-series: mention volume vs sentiment vs stock price
• stacked bar charts: distribution of sentiment labels per platform
• heatmaps: misinformation prevalence by category and platform
• network diagrams: highlight central accounts and cross-post behavior

Practical toolstack (minimal & recommended)

Avoid tool sprawl (a 2026 trend highlighted in MarTech). Use a compact stack that covers collection, modeling, annotation, and visualization:

• Collection: platform APIs (Bluesky API, X API), PRAW for Reddit, official StockTwits API
• Annotation: Prodigy, Labelbox, or a simple Google Sheets/Docs workflow for small samples
• Modeling: Python (pandas, scikit-learn), Hugging Face transformers, or API access to OpenAI for inference
• Visualization: matplotlib/seaborn, Plotly for interactive dashboards
• Notebook & Reproducibility: Jupyter or Colab + GitHub for code/data versioning

Minimal stack suggestion: use two APIs, a notebook, and a labeling sheet. Keep it lean to avoid the cost and complexity traps of tool sprawl.

Evaluation & grading rubric

Grade projects on rigor, reproducibility, and insight:

• Data collection & ethics (20%): Clear methods, README, ethics statement, compliance with ToS.
• Sentiment methodology (20%): Appropriate models, error analysis, handling of slang/sarcasm.
• Accuracy & fact-checking (20%): Sample annotation quality, inter-annotator agreement, automated checks.
• Analysis & visualization (20%): Clear cross-platform comparisons, correct interpretation, limitations discussed.
• Reproducibility & presentation (20%): Code, dataset sample, final report and presentation quality.

Sample prompts & annotation templates

Annotation codebook excerpt

• Label 0 = Unrelated/Noise (mentions other tickers or uses cashtag in non-financial context)
• Label 1 = Positive sentiment (expresses bullish outlook or praise)
• Label 2 = Negative sentiment (expresses bearish outlook or criticism)
• Label 3 = Neutral/Informational (news links, earnings reports without opinion)

Fact-checking template

1. Claim text
2. Claim type (price, earnings leak, exec rumor, chart)
3. Evidence checked (link to official filing, historical price, reverse-image results)
4. Label: True / False / Misleading / Unverifiable
5. Annotator notes

Common pitfalls and how to avoid them

• Sampling bias: High-engagement posts skew sentiment. Mitigate with random sampling plus engagement-weighted metrics.
• Sarcasm and slang: Off-the-shelf sentiment models underperform. Fine-tune on a small labeled set and add slang lexicons.
• Cross-post duplication: Identify reposts to avoid double-counting across platforms.
• API limits and data gaps: Document missing data and time windows; don’t overgeneralize findings beyond your sample.
• Tool sprawl: Keep the stack minimal—more tools increase integration risk and cost.

Interpreting results: what to look for

When you compare platforms, ask:

• Does daily sentiment lead or lag price moves? (Careful: correlation is not causation.)
• Which platform has more unverifiable claims or doctored content?
• Are certain accounts or networks driving conversation volume?
• How do platform affordances (cashtags, LIVE badges, thread structure) change conversational style and signal reliability?

Use case studies: show 2–3 illustrative examples where a rumor appears on one platform first and then spreads, or where platform moderation removed misleading posts.

2026 trends & future predictions (short)

Based on late-2025 and early-2026 developments, expect:

• More specialized tagging (cashtags on newer networks like Bluesky) and provenance signals (LIVE badges, content labels).
• Increased scrutiny from regulators and platform oversight around AI-generated content and non-consensual imagery; this affects trust in financial conversations.
• Consolidation in social-listening toolchains—teams will prefer lean, integrated stacks to avoid unnecessary cost and complexity.
• Growing demand for fine-tuned, domain-specific sentiment models that handle finance slang and AI-era manipulation.

Example student conclusion (model)

"In our 14-day sample of $XYZ conversation, Bluesky posts were shorter and more sentimentally positive on average, but had lower engagement. X showed higher volume and a greater share of unverifiable claims, often amplified by a small cluster of high-activity accounts. Our transformer-based model achieved 82% accuracy on sentiment after fine-tuning, but sarcasm and memes remained the largest error source. We recommend combining platform-specific filters, manual fact-checking, and provenance signals when using social listening for trading or reporting."

Deliverables checklist

• Raw data archive & README (collection steps, timestamps, API keys redacted)
• Jupyter notebook or Colab with code and model artifacts
• Annotation files and inter-annotator stats
• Visualizations (PNG or interactive dashboard link)
• Final report (1,500–2,000 words) and 10-minute presentation
• Ethics statement

Instructor notes & scaling tips

• For larger classes, provide one common dataset and assign different analytical angles (bot detection, rumor path analysis, sentiment model tuning).
• Use peer review: swap reports between groups for blind scoring of interpretation and methodology.
• Bring in guest speakers (journalists, compliance officers) to discuss real-world accuracy and legal risks.

Final thoughts

Social media conversations about stocks are noisy and fast-moving in 2026. Platform changes—like Bluesky’s new cashtags and LIVE badges—and greater AI-driven content risks make disciplined, cross-platform research essential. This assignment trains students to collect ethically, analyze rigorously, and interpret cautiously—skills that are directly transferable to careers in data journalism, compliance, marketing intelligence, and finance.

Call to action

Ready to run this assignment in your class or as a capstone? Download a free, editable assignment packet, sample dataset, and starter Colab notebook from GooClass. Sign up for templates, rubrics, and instructor notes—get students doing reproducible, cross-platform social listening today.

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