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Design a Research Project on Franchise Fatigue: Using Star Wars & Rivals as Case Studies

UUnknown

2026-02-13

9 min read

A step-by-step project blueprint for studying "franchise fatigue" using Star Wars and Rivals—methods, data sources, and 2026 trends.

Hook: Turn franchise fatigue into a publishable research project — fast

Are you an upper-level media studies student staring down a semester-long project and worrying about tight deadlines, messy datasets, and how to show rigorous methods without reinventing the wheel? You’re not alone. In 2026, scholars and students face a particular pressure: capture meaningful insights about rapidly shifting audience behavior while navigating tightened API policies, streaming consolidation, and the continuing churn of legacy franchises like Star Wars. This guide gives you a ready-to-run, IRB-aware research design using Star Wars and Rivals (a Disney/streaming reality format) as paired case studies of franchise expansion and audience response.

Executive summary (most important first)

This project proposes a mixed-methods, comparative case study that combines: 1) quantitative audience analytics (social sentiment, viewing metrics, Google Trends, box office/ratings); 2) qualitative audience research (interviews, focus groups, fan ethnography); and 3) content/discourse analysis of media coverage and official communications. It centers on three research questions, supplies precise data sources and collection scripts adapted for 2026 data-access realities, and gives templates for surveys, interview guides, and coding schemes you can reuse.

Why Star Wars and Rivals? Case selection rationale

Pick these two franchises to study contrasting expansion strategies and audience ecosystems:

Star Wars — A global, transmedia mega-franchise with film, TV (now under Dave Filoni’s leadership as of Jan 2026), games, and merchandising. It exemplifies legacy-IP saturation and institutional responses to audience backlash (source: Forbes, Jan 16, 2026).
Rivals — A contemporary reality competition format (strong presence on Disney+/Hulu and European markets) that illustrates serialized, internationalized franchise building and platform-level promotion strategies (source: Deadline coverage of Disney+ EMEA promotions, 2024–2026).

Core research questions and hypotheses

Primary research question

How does franchise expansion strategy influence audience response and perceptions of brand fatigue across different media formats in 2026?

Secondary research questions

Do long-running franchises like Star Wars show measurable signs of audience fatigue (declining engagement, heightened negative sentiment, decreased willingness to pay) compared with newer serialized formats like Rivals?
How do platform-level practices (release cadence, localization, algorithmic promotion) shape the intensity and duration of audience backlash or enthusiasm?
Which audience segments (core fans, casual viewers, regionally localized viewers) drive negative versus positive discourse about franchise expansion?

Hypotheses (examples)

H1: Increased output (more films/series in short time) correlates with higher negative sentiment per post about the franchise.
H2: Reality formats with frequent casting refreshes and local adaptations (e.g., Rivals in EMEA) show less sustained fatigue than IP-heavy cinematic franchises.
H3: Platform-driven discovery (algorithmic promotion) moderates the relationship between output and audience fatigue.

Methodological overview — Mixed methods, phased

Use a three-phase design:

Phase 1 — Macro analytics: Time-series and comparative metrics to quantify audience engagement trends.
Phase 2 — Qualitative depth: Interviews, focus groups, and participant observation in fandom spaces to interpret the why behind metrics.
Phase 3 — Integration: Triangulate findings through content analysis and synthesis to answer how and why fatigue emerges.

Data sources and access (2026 realities)

Data access in 2026 is shaped by platform consolidation, tightened APIs (notably on X/Twitter and TikTok), and an increase in paid analytics. Plan budgets and IRB requests accordingly.

Quantitative sources

Social media: Twitter/X (official API or paid reseller), Reddit (pushshift / API), TikTok (official API via approved academic partnerships or paid data vendors), YouTube comments & views (YouTube Data API).
Streaming & viewership: Nielsen Streaming Meter (where available), Parrot Analytics demand expressions, platform announcements (Disney press releases), and third-party aggregators (FlixPatrol, JustWatch for regional availability trends).
Box office & ratings: Box Office Mojo for theatrical releases, Rotten Tomatoes/Metacritic for critic and audience scores, IMDb ratings and review counts.
Search & interest: Google Trends (regional and temporal queries), YouGov and Pew datasets (for broader public opinion where available).

Qualitative sources

Fan forums and subreddits (r/StarWars, r/television, country-specific Rivals forums)
Discord servers and private fan groups (with consent for research participation)
Interviews & focus groups with fans, casual viewers, and industry professionals (casting directors, platform commissioners)
Mainstream press coverage and trade reporting (Forbes, Deadline, Variety) — useful for tracing production & leadership changes (e.g., Dave Filoni’s appointment in Jan 2026)

Archival and industry data

Press releases (Disney/Lucasfilm announcements)
Historical release schedules (studio reports, Wayback archive)
Marketing materials and official social posts (Instagram, X, YouTube)

Concrete data collection plan

Time frame

Choose a rolling 36-month window centered on major events — e.g., Jan 2023–Dec 2025 — to capture pre- and post-announcement reactions, and extend into early 2026 for post-Filoni dynamics.

Examples of boolean queries and search strategies:

Twitter/X full-text search (paid API): ("Star Wars" OR #StarWars OR #TheMandalorian OR "Dave Filoni") lang:en since:2023-01-01 until:2026-01-31
Reddit (Pushshift): subreddit:"starwars" OR subreddit:"rivals" AND title/selftext contains: (fatigue OR "too many" OR "overkill" OR "tired of")
TikTok & YouTube: collect engagement metrics (views, likes, comments) for top 200 posts tagged #StarWars or #Rivals over selected windows.

Surveys — sample and instrument

Recruit via Prolific (for diversity) and fandom communities (for core fans). Target N=500+ for quantitative power (split by franchise affinity).

Sample survey items (Likert 1–7):

"I feel there are too many new Star Wars releases in recent years."
"I am likely to watch the next season of Rivals."
"I believe the franchise's quality has declined due to rapid expansion."
Include demographic and consumption questions: age, region, subscription services, fandom tenure (years).

Interviews & focus groups

Interview guide (30–45 mins):

"Describe the last time you felt fatigued with a franchise. What triggered it?"
"How do you discover new franchise content (friends, algorithm, advertising)?"
"What would reduce your interest in future franchise content?"

Analysis plan

Quantitative techniques

Time-series analysis: ARIMA/ETS to model engagement trends and identify structural breaks around major announcements.
Sentiment analysis: fine-tune a RoBERTa/BERT model on annotated social posts for domain-specific sentiment and emotion detection (use 2,000–5,000 hand-labeled posts for training).
Topic modeling: dynamic topic models (DTM) or BERTopic to identify shifting themes (quality, oversaturation, casting) over time.
Inferential stats: regression models to test the relationship between release rate and sentiment, controlling for platform, region, and fandom intensity.

Qualitative coding & interpretation

Use software like NVivo or Atlas.ti. Start with an open coding round on 200 posts/interviews, derive axial codes (e.g., "quality concerns", "brand nostalgia", "algorithm fatigue"). Report inter-coder reliability (Cohen’s kappa ≥ .7).

Mixed-methods integration

Use a convergent design: run quantitative and qualitative analyses in parallel, then compare and contrast results. For instance, if time-series shows a sentiment dip after an announcement, use interview/focus group data to unpack the emotional logic and narratives driving that dip.

Ethics, IRB, and data protection in 2026

Key points:

Get IRB approval for surveys, interviews, and any scraping of private groups (Discord). Public social media posts may be permissible, but anonymize direct quotes unless you have consent.
Comply with GDPR for European respondents (especially for Rivals’ EMEA audience): store EU data in-region or use approved processors.
Document data provenance and provide opt-out instructions for participants in fandom spaces.

Tools & reproducibility

Suggested tool stack:

Data collection: Python (Tweepy/X API clients), Pushshift, TikTok API via vendor, YouTube Data API
Analysis: pandas, statsmodels, scikit-learn, Hugging Face Transformers, BERTopic, Gephi for social network maps
Qualitative: NVivo/Atlas.ti, Dedoose for mixed-methods coding
Visualization & reproducibility: Jupyter Notebooks, GitHub, OSF preregistration (recommended)

Sampling, reliability, and validity

Practical tips:

Triangulate platforms — don’t rely on one network (TikTok-only narratives will miss longform fan essays on Reddit).
Use stratified sampling to ensure representation of core fans vs. casual viewers, and by region (US, UK, EMEA).
Report confidence intervals and effect sizes, and include robustness checks (alternative sentiment lexicons, manual verification).

Limitations and how to mitigate them

API restrictions — budget for paid data where necessary or partner with your university’s licensed vendors.
Self-selection bias in fan forums — counteract with probability-based surveys (Prolific) to measure generalizability.
Sentiment model drift — annotate recent samples and retrain periodically to maintain accuracy.

Expected findings and scholarly value (what you can reasonably claim)

Anticipated outcomes might include:

A measurable correlation between production acceleration and increased negative sentiment, but with important moderators like narrative quality and marketing tone.
Evidence that reality formats with modular, localized production (e.g., Rivals across EMEA) sustain audience enthusiasm because they offer novelty and local identification.
Qualitative accounts illuminating how algorithmic promotion and “event fatigue” interact — e.g., too many simultaneous releases overwhelm expectant audiences, while staggered local adaptations maintain freshness.

Practical templates to get started (copy-paste friendly)

Survey screener

"Have you watched any Star Wars film or Disney+ series in the past 24 months?" (Yes/No) — use screener to classify respondents into fan tiers.

Short interview opener

"Tell me about the last time a franchise release disappointed you. What did you expect, and what actually happened?"

Sample coding scheme (qualitative)

Code A: Overexposure (mentions of "too many", "saturation")
Code B: Quality decline (mentions of "writing", "characters", "plot")
Code C: Algorithmic frustration (mentions of "recommendations", "ads")

2026 trends and implications for your project

Contextualize findings in light of late-2025/early-2026 developments: leadership shifts at Lucasfilm (Dave Filoni), executive promotions at Disney+ EMEA affecting Rivals' commissioning, and platform changes that affect data access and recommendation logic. Expect your project to speak directly to debates about corporate strategy (IP overexpansion) and platform governance (how algorithms shape perceived fatigue).

Timeline & deliverables (compact semester schedule)

Weeks 1–3: Finalize proposal, IRB submission, preregistration.
Weeks 4–8: Data collection (social scraping, survey fielding).
Weeks 9–12: Coding and model training (sentiment/topic).
Weeks 13–15: Integration, write-up, and presentation prep.

Reporting & output formats

Produce: a written report with methods appendix, reproducible code on GitHub (sensitive data excluded), a short presentation, and optionally a data visualization dashboard (Tableau or Observable) for stakeholder communication.

Final tips for success

Pre-plan for data access limits in 2026 — secure vendor contracts early.
Balance ambition and feasibility: prefer deeper analysis on fewer variables over shallow coverage of every possible metric.
Document everything — your professor and future readers will value transparent, reproducible methods.

Call to action

If you want a ready-to-run project package (preregistration template, survey and interview guides, scraping scripts adapted for 2026 APIs, and a grading-friendly write-up template), we can prepare one tailored to your syllabus. Visit essaypaperr.com or contact our research coaching team for editing, tutoring, or project setup help — get the reproducible materials you need and finish your project with confidence.

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