Star Trek: Starfleet Academy — r/television Tracker

A lightweight Python project for observing general-audience and industry-level discussion of Star Trek: Starfleet Academy on Reddit by tracking discussion behavior, not subscriber counts.

This tracker is intentionally scoped to r/television, where conversation reflects premieres, trailers, media coverage, and cultural reaction rather than deep fandom participation. It complements (but does not replace) separate trackers for Star Trek–specific subreddits.

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What This Project Does

This first-pass tracker focuses on where and when Starfleet Academy enters mainstream conversation.

Specifically, it:

• Searches r/television for posts related to Star Trek: Starfleet Academy

• Identifies and classifies posts into:

  • episode discussion threads (e.g. 1x03, S01E01, Episode 4)
  • official trailers and teasers
  • high-engagement, non-episode posts (premieres, media articles, controversy)

• Captures post-level metadata:

  • comment count
  • score (net upvotes)
  • creation timestamp
  • subreddit source

• Appends comment counts to a time-series dataset on each run

• Generates:

  • CSV exports for offline analysis
  • line graphs showing comment growth over time
  • a local, static HTML dashboard for review

The result is a reproducible snapshot of when Starfleet Academy becomes a cultural event, rather than an attempt to measure fandom loyalty.

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What This First Pass Shows

Early runs consistently show that r/television engagement is event-driven:

• Trailers and first-look teasers generate short-term spikes
• The series premiere dominates total engagement
• Weekly episode discussion exists, but is fragmented and low-volume
• Conversation is often framed through media narratives, ratings, or franchise discourse

This confirms that r/television functions as a general audience + industry sentiment space, not a sustained episodic discussion hub.

That distinction is intentional and central to the project’s design.

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Why Comments Instead of Subscribers

Reddit’s visible membership counts and “active users” metrics now vary by:

• interface (old vs new Reddit)
• aggregation context
• subreddit configuration

This makes them unreliable for longitudinal analysis.

Comments, by contrast:

• represent active participation
• accumulate over time
• capture both positive and negative engagement
• remain accessible via public JSON endpoints

For cultural and media analysis, comment growth provides a clearer signal of attention and response than subscriber totals alone.

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Project Structure

starfleet_academy_tracker/
├─ src/
│  ├─ starfleet_academy_tracker.py
│
├─ data/
│  └─ starfleet_academy_comment_history.csv
│
├─ out/
│  ├─ starfleet_academy_all_posts.csv
│  ├─ starfleet_academy_episode_posts.csv
│  ├─ starfleet_academy_selected_posts.csv
│  ├─ starfleet_academy_episode_comment_growth.png
│  ├─ starfleet_academy_non_episode_comment_growth.png
│  └─ dashboard_starfleet_academy.html
│
├─ logs/
│  └─ starfleet_academy_tracker.log
│
├─ README.md
├─ requirements.txt
└─ .gitignore

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Requirements

• Python 3.11 or newer

Install dependencies with:

pip install -r requirements.txt

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How to Run

From the project root:

python src/starfleet_academy_tracker.py

The script will:

• fetch current Reddit data
• append to the comment history file
• regenerate CSVs, plots, and the HTML dashboard

Open the dashboard locally:

out/dashboard_starfleet_academy.html

(No web server required.)

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Notes on Data Use

• Uses only Reddit’s public JSON search endpoints
• No API keys or authentication required
• Designed for infrequent polling (6–12 hours recommended)
• Comment trends become meaningful over repeated runs
• Absence of discussion is treated as a signal, not a failure

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Relationship to Other Trackers

This tracker is intentionally limited to r/television.

Separate trackers exist (or are planned) for:

• r/startrek (general fandom)
• r/DaystromInstitute (hard science and canon analysis)

Together, these dashboards map different layers of audience attention, rather than blending them into a single, misleading metric.

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Part of the RewindOS project — tracking cultural signals where traditional audience metrics fall short.