Reddit Mentions Dataset

Track how often stock tickers are discussed across Reddit investing communities. Monitor retail investor attention on WallStreetBets, r/stocks, and other subreddits with mention counts collected every 4 hours.

What’s Included

The Reddit Mentions dataset provides:

Per-Subreddit Counts: Mention counts for each tracked subreddit (wallstreetbets, stocks, etc.)
Aggregate Total: Combined mentions across all subreddits via the _all entry
Intraday Snapshots: Data collected every 4 hours (6 snapshots per day)
Cross-Market Screening: Screener endpoint to compare mentions across tickers

Coverage

Source	Description	Update Frequency
Reddit	WallStreetBets, r/stocks, and other investing subreddits	Every 4 hours

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.reddit_mentions.ticker("TSLA", as_dataframe=True)
print(df)

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.finbrain.tech/v2"

response = requests.get(
    f"{BASE_URL}/reddit-mentions/TSLA",
    headers={"Authorization": f"Bearer {API_KEY}"}
)

data = response.json()
for d in data["data"]["data"]:
    print(f"{d['date']} {d['subreddit']}: {d['mentions']} mentions")

For complete code examples in Python, JavaScript, C++, Rust, and cURL, see the API Reference.

Key Fields

Field	Description	Example
`date`	Snapshot timestamp (ISO 8601)	2026-03-16T14:00:00.000Z
`subreddit`	Subreddit name, or `_all` for aggregate total	wallstreetbets
`mentions`	Number of ticker mentions in this snapshot	45

Use Cases

Most Mentioned Tickers

Use the screener to find the most-discussed stocks across a market:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.screener.reddit_mentions(market="S&P 500", as_dataframe=True)

# Sort by total mentions
top = df.sort_values("totalMentions", ascending=False).head(10)

for symbol, row in top.iterrows():
    print(f"{symbol}: {row['totalMentions']} total mentions")

Subreddit Breakdown

See where discussion concentrates for a single ticker:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.reddit_mentions.ticker("TSLA", as_dataframe=True)

# Filter out the aggregate _all rows and get latest snapshot
latest = df[df["subreddit"] != "_all"].sort_values("date", ascending=False)
latest_date = latest["date"].iloc[0]
snapshot = latest[latest["date"] == latest_date]

for _, row in snapshot.iterrows():
    print(f"r/{row['subreddit']}: {row['mentions']} mentions")

Mention Velocity

Detect sudden spikes in retail attention by comparing snapshots over time:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.reddit_mentions.ticker("GME", as_dataframe=True)

# Focus on aggregate totals
totals = df[df["subreddit"] == "_all"].sort_values("date")

# Calculate change between consecutive snapshots
totals["prev_mentions"] = totals["mentions"].shift(1)
totals["change_pct"] = (
    (totals["mentions"] - totals["prev_mentions"]) / totals["prev_mentions"] * 100
)

for _, row in totals.dropna().iterrows():
    if abs(row["change_pct"]) > 50:
        print(f"{row['date']}: {row['mentions']} mentions "
              f"({row['change_pct']:+.0f}% change)")

Reddit Mentions API Reference - Endpoint details, parameters, and response schema
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Stock Screener API - Screen data across tickers