What we are building

A script that pulls daily mention counts for a keyword over the last 30 days and plots them, so you can see trend, spikes, and quiet periods at a glance. Same approach works for a brand name, a competitor, a product category, or a topic you are researching.

The data source

Redlisten is an API for public Reddit posts and comments. Redlisten is not affiliated with Reddit, Inc.; it indexes public posts and comments and returns them through a normal REST API, which includes a trends endpoint built specifically for this: mention counts per day for a keyword, without you having to pull every raw post and count them yourself.

The trends endpoint is on the Pro plan and above; a free key gets a plan-required error here, so grab a Pro key before running the script below.

Before writing any code, you can see the shape of this for free at the preview page: https://redlisten.com/reddit/<keyword>?format=json.

The script

import requests
import matplotlib.pyplot as plt

API_KEY = "rl_live_YOUR_KEY"
KEYWORD = "notion"

resp = requests.get(
    "https://api.redlisten.com/v1/trends",
    params={"q": KEYWORD, "interval": "day", "days": 30},
    headers={"Authorization": f"Bearer {API_KEY}"},
)
resp.raise_for_status()
data = resp.json()

rows = data["data"]
dates = [row["t"][:10] for row in rows]
counts = [row["posts"] + row["comments"] for row in rows]

plt.figure(figsize=(10, 4))
plt.plot(dates, counts, marker="o")
plt.xticks(rotation=45, ha="right")
plt.title(f'Reddit mentions of "{KEYWORD}" per day')
plt.ylabel("Mentions")
plt.tight_layout()
plt.savefig("mentions.png")
print(f"Saved chart with {sum(counts)} total mentions over {len(dates)} days")

The response body is data["data"], a list of daily rows. Each row has a timestamp in t (sliced to the date here) plus separate posts and comments counts, which the script adds together for a single mentions line. If you only care about posts, drop the comments term.

Run it, and you get a line chart saved to mentions.png along with a total count printed to the terminal. Swap the keyword for your brand, a competitor, or a feature name and rerun.

Comparing two keywords

A single line is useful, but it gets a lot more useful next to a competitor's. On the Team tier and above, a dedicated comparison endpoint handles this in one call instead of two:

resp = requests.get(
    "https://api.redlisten.com/v1/trends/compare",
    params={"q": "notion,obsidian", "interval": "day", "days": 30},
    headers={"Authorization": f"Bearer {API_KEY}"},
)
compare_data = resp.json()

for series in compare_data["series"]:
    keyword = series["q"]
    rows = series["data"]
    counts = [row["posts"] + row["comments"] for row in rows]
    plt.plot([row["t"][:10] for row in rows], counts, marker="o", label=keyword)

plt.legend()
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
plt.savefig("compare.png")

The response comes back as series, a list of one object per keyword, each holding its own q and data in the same shape as the single-keyword endpoint above. Plot both on the same axes and you have a quick, honest picture of relative conversation volume over time, useful for a board update or a competitive research doc without the usual multi hour pull from several tools.

Turning this into a standing dashboard

The script above is a one off pull. Wire it to cron (or a scheduled task if you are already running something like Airflow), write the daily counts to a small database table instead of plotting to a file, and you have a lightweight mentions dashboard that updates itself every morning. The trends endpoint returns aggregated counts rather than individual posts, so it is cheap on requests once you are past the free tier and running this daily.

Current plan limits and which trend endpoints are included at each tier are listed at redlisten.com/#pricing, and full endpoint documentation is at redlisten.com/docs.

Get a Pro key to run this yourself: app.redlisten.com.