social-media-intelligence

# Social Media Intelligence

> This skill integrates financial-intelligence collection methods and quantitative applications across Twitter/X, Telegram, Discord, and Reddit.
> Inspired by `himself65/finance-skills` modules such as `discord-reader`, `telegram-reader`, and `twitter-reader`.

---

## 1. Overview of the Four Major Financial Social Platforms

### 1.1 Twitter/X — The FinTwit Ecosystem

**Core roles**

| Role Type | Representative Account Traits | Signal Value |
|---------|------------|---------|
| Sell-side analyst | Institutional backing, dense posting around earnings | Medium, somewhat lagging |
| Fund manager | Holdings views, industry judgment | High, but mixed with subjective opinion |
| Macro commentator | Fed interpretation, macro-data reaction | High, a good sentiment barometer |
| Crypto KOL | On-chain interpretation, project endorsement | Highly volatile, high manipulation risk |
| Retail noise | Meme spread, herd sentiment | Contrarian signal value at extremes |

**Core FinTwit circles**
- `$TICKER` cashtag system directly maps discussion to the asset
- Earnings-season sentiment patterns before and after reports
- Real-time reaction speed to policy / macro events, often 15-60 minutes ahead of traditional media

---

### 1.2 Telegram — The Core Venue for Crypto Intelligence

**Channel types**

| Channel Type | Content Traits | How to Use |
|---------|---------|---------|
| Signal channels | Specific buy/sell levels, stop-loss / take-profit | Use as a sentiment thermometer, not for blind copy-trading |
| Research push channels | Institutional PDF reports, on-chain data | Aggregate information and extract key numbers |
| Macro flash channels | Real-time interpretation of FOMC, CPI, etc. | Event-driven signals |
| Official project channels | Tokenomics updates, partnership announcements | Potential alpha, but requires filtering |
| Whale alert channels | Large on-chain transfer alerts | Capital-flow signal |

---

### 1.3 Discord — Quant Communities and Project Ecosystems

**Important community types**
- Quant / DeFi research communities such as Degen Spartan and Messari Research
- Official crypto project Discords with governance discussion and development progress
- Trader communities focused on options flow and on-chain analysis
- NFT / GameFi projects with floor-price alerts and activity monitoring

**Distinctive value of Discord**
- Community activity directly reflects project health
- Developer channels such as `#dev` and `#build` show implementation activity
- Governance participation indicates the willingness of token holders to stay involved

---

### 1.4 Reddit — A Barometer of Retail Sentiment

**Core subreddits**

| Subreddit | Core User Base | Main Signal |
|-------|---------|---------|
| r/wallstreetbets | Retail options traders | Meme-stock heat, abnormal options chatter |
| r/investing | Value-oriented retail investors | Long-horizon sentiment, ETF flow |
| r/cryptocurrency | Crypto retail | BTC / ETH cycle sentiment |
| r/stocks | General stock discussants | Earnings-season sentiment |
| r/options | Options-strategy community | Unusual IV-related topics |

---

## 2. Data Collection Methods

### 2.1 Twitter/X Data Collection

**Tooling options**

```python
# Option A: Official API v2 (paid, basic tier starts at $100/month)
# Best for: production environments where compliance is the priority
from tweepy import Client

client = Client(bearer_token=os.getenv("TWITTER_BEARER_TOKEN"))

# Search tweets discussing a cashtag over the last 7 days
def fetch_cashtag_tweets(ticker: str, max_results: int = 100) -> list[dict]:
    """Collect Twitter discussion data for a given ticker.

    Args:
        ticker: Ticker symbol such as AAPL or BTC
        max_results: Max number of returned tweets, between 10 and 100

    Returns:
        List of tweets, each containing id / text / created_at / public_metrics
    """
    query = f"${ticker} -is:retweet lang:en"
    tweets = client.search_recent_tweets(
        query=query,
        max_results=max_results,
        tweet_fields=["created_at", "public_metrics", "author_id"],
    )
    return [t.data for t in tweets.data or []]


# Option B: ntscraper (unofficial, free, rate-limited)
# Best for: research / historical backtesting
# pip install ntscraper
from ntscraper import Nitter

scraper = Nitter()
tweets = scraper.get_tweets("$AAPL", mode="term", number=50)
```

**Data schema (Twitter JSON Schema)**

```json
{
  "platform": "twitter",
  "collected_at": "2026-03-29T08:00:00Z",
  "query": "$AAPL",
  "items": [
    {
      "id": "tweet_id_string",
      "text": "tweet text",
      "created_at": "ISO8601 timestamp",
      "author": {
        "id": "user_id",
        "username": "handle",
        "followers_count": 50000,
        "verified": false
      },
      "metrics": {
        "like_count": 120,
        "retweet_count": 45,
        "reply_count": 23,
        "quote_count": 8
      },
      "sentiment_score": null,
      "tags": ["$AAPL", "#earnings"]
    }
  ]
}
```

**Suggested collection frequency**
- Earnings season / major events: real time, poll every 5 minutes
- Routine monitoring: hourly
- Historical backfill: daily batch

---

### 2.2 Telegram Data Collection

**Tooling**

```python
# Telethon — official MTProto client, requires API_ID + API_HASH
# pip install telethon
from telethon.sync import TelegramClient
from telethon import functions

API_ID = int(os.getenv("TELEGRAM_API_ID"))
API_HASH = os.getenv("TELEGRAM_API_HASH")

async def fetch_channel_messages(
    channel_username: str,
    limit: int = 200,
    offset_date: datetime | None = None,
) -> list[dict]:
    """Collect historical messages from a Telegram channel.

    Args:
        channel_username: Channel username without @, e.g. "whale_alert"
        limit: Maximum number of messages
        offset_date: Start time to backtrack from

    Returns:
        List of messages containing id / text / date / views / forwards
    """
    async with TelegramCl