🎙️ SpeakEasy

Reddit Topic → Subtopic → Credibility Analysis (AI Hackathon Project)

SpeakEasy is a web-based analytics platform that explores large-scale Reddit discussions (r/Canada) to uncover:

• What people are talking about
• How conversations break into meaningful subtopics
• Public sentiment trends
• Credibility & fake-news risk using Gemini (with minimal API usage)

Built for the AI Hackathon in the North (2026).

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🚀 What SpeakEasy Does

1️⃣ Topic Exploration

Users input:

• A main topic (e.g. immigration, housing, healthcare)
• A date range

The system then:

1. Filters millions of Reddit comments by date
2. Filters again using the main keyword
3. Uses Gemini to generate ~20 realistic sub-keywords
4. Keeps comments matching at least 2 sub-keywords
5. Extracts the Top 5 subtopics

📊 Results are shown as an interactive bar chart.

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2️⃣ Subtopic Deep-Dive (Click a Bar)

When a user clicks a subtopic (e.g. international students):

• A new dashboard page opens
• All analysis is now strictly scoped to that subtopic

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3️⃣ Dashboard Analytics (Subtopic-Specific)

📈 Trend Over Time

• Mentions per day
• Displayed as a clean line chart

💬 Sentiment Analysis (Offline)

• Uses VADER (no API calls)
• Pie chart: Positive / Neutral / Negative

🏆 Top Contributors

• Bar chart showing most active authors

🛡️ Credibility & Fake News Analysis (Gemini)

• Uses ONE Gemini API call
• Classifies sampled comments into:
  • Opinion
  • Needs verification
  • Likely rumor / misleading
  • Unclear
• Displays:
  • Overall credibility score (%)
  • Interactive filled percentage bars

Designed to stay well under the 1,000 API call limit.

🧾 Top 10 Reddit Comments

• Most recent comments matching the subtopic
• Shows:
  • Author
  • Timestamp
  • Content snippet

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🧠 Why This Approach Works

• ✅ Handles millions of rows efficiently
• ✅ Uses Gemini only where it adds real value
• ✅ No per-comment LLM calls
• ✅ Transparent, explainable outputs
• ✅ Easily extendable to other subreddits or regions

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🛠️ Tech Stack

Backend

• Python
• FastAPI
• Pandas
• VADER Sentiment
• Gemini API (hackathon endpoint)

Frontend

• HTML + CSS (custom dark UI)
• Vanilla JavaScript
• Chart.js

Data

• Reddit r/Canada dataset (3.1M+ rows)
• Columns used:
  • author
  • body
  • created_utc

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🔑 Gemini API Usage

• Used only for:

  1. Sub-keyword generation
  2. Credibility / misinformation labeling (batched)

• Max calls per analysis: 2

• Typical dashboard load: 1 call

API key is stored securely in .env and never committed.

GEMINI_API_KEY=your_api_key_here

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📂 Project Structure

Hackathon_26/
│
├── main.py              # FastAPI server
├── utils_funcs.py       # Data processing + Gemini logic
├── Data/
│   └── sample_data.csv  # Trimmed Reddit dataset
│
├── static/
│   └── assets/          # Images, logos
│
├── templates/
│   ├── index.html       # Topic → subtopic explorer
│   └── dashboard.html   # Deep-dive analytics page
│
├── .env                 # API key (not committed)
└── README.md

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▶️ How to Run Locally

1️⃣ Create virtual environment

python -m venv .venv source .venv/bin/activate # macOS / Linux

or .venv\Scripts\activate # Windows

2️⃣ Install dependencies

pip install fastapi uvicorn pandas requests python-dotenv vaderSentiment

3️⃣ Add API key

Create a .env file:

GEMINI_API_KEY=your_key_here

4️⃣ Start server

uvicorn main:app --reload

5️⃣ Open browser

http://127.0.0.1:8000

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🧪 Example Workflow 1. Enter topic: immigration 2. Select date range 3. Click Analyze 4. Click a subtopic (e.g. international students) 5. Explore: • Trend over time • Sentiment distribution • Top contributors • Credibility score • Recent Reddit posts

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⚠️ Limitations • Credibility labeling is assistive, not absolute fact-checking • Reddit data is inherently opinion-heavy • Gemini outputs depend on prompt quality • Dataset is trimmed for performance

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🌟 Future Improvements • User-controlled credibility thresholds • Cross-subtopic comparison views • Multi-subreddit expansion • Gemini response caching • Exportable reports (PDF / CSV)

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🏁 Hackathon Context

Built for AI Hackathon in the North – 2026 Goal: Demonstrate how AI + large public datasets can help users understand what’s being said, what matters, and what needs verification.

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