Twitter account health check, growth strategy, and engagement optimization
Systematic approach to growing Twitter presence based on the open-source algorithm analysis. Check account health, find engagement opportunities, optimize content strategy.
pip install "rnet>=3.0.0rc20" --pre)TWITTER_COOKIES_PATH env var
Format: [{"name": "auth_token", "value": "..."}, {"name": "ct0", "value": "..."}]x.com -> log inhttps://x.comauth_token and ct0 values| Metric | Healthy Range | Impact |
|---|---|---|
| Following/Follower Ratio | < 0.6 | TweepCred score |
| Avg Views/Tweet | 20-40% of followers | Algorithm favor |
| Media Tweet % | > 50% | 10x engagement |
| Link Tweet % | < 20% | Avoid algorithm penalty |
| Reply Rate | Reply to 100% of comments | +75 weight boost |
import asyncio, os
from rnet_twitter import RnetTwitterClient
async def analyze(username: str):
client = RnetTwitterClient()
cookies_path = os.environ.get("TWITTER_COOKIES_PATH", "twitter_cookies.json")
client.load_cookies(cookies_path)
user = await client.get_user_by_screen_name(username)
followers = user.get("followers_count", 0)
following = user.get("friends_count", 0)
ratio = following / max(followers, 1)
tweets = await client.get_user_tweets(user["rest_id"], count=20)
return {
"username": username,
"followers": followers,
"following": following,
"ratio": round(ratio, 2),
"tweet_count": user.get("statuses_count", 0),
"recent_tweets": len(tweets),
}
Manual check: shadowban.yuzurisa.com
Recommends accounts to unfollow based on:
async def find_opportunities(niche_keywords: list[str]):
client = RnetTwitterClient()
cookies_path = os.environ.get("TWITTER_COOKIES_PATH", "twitter_cookies.json")
client.load_cookies(cookies_path)
opportunities = []
for keyword in niche_keywords:
tweets = await client.search_tweets(
f"{keyword} lang:en -filter:replies",
count=50, product="Top"
)
for t in tweets:
if t["favorite_count"] >= 50 and t["reply_count"] < 20:
opportunities.append(t)
return sorted(opportunities, key=lambda t: t["favorite_count"], reverse=True)
Based on Twitter's open-source algorithm:
Score = PageRank x (1 / max(1, following/followers))
| Ratio | Estimated TweepCred | Algorithm Treatment |
|---|---|---|
| < 0.6 | 65+ (healthy) | All tweets considered |
| 0.6 - 2.0 | 40-65 | Limited consideration |
| 2.0 - 5.0 | 20-40 | Severe penalty |
| > 5.0 | < 20 | Only 3 tweets max |
Week 1: Unfollow 30 inactive accounts
Week 2: Unfollow 30 non-engagers
Week 3: Unfollow 30 low-value follows
Week 4: Evaluate ratio improvement
| Type | Weight | Recommendation |
|---|---|---|
| Tweet that gets author reply | +75 | ALWAYS reply to comments |
| Tweet with replies | +13.5 | Ask questions |
| Tweet with profile clicks | +12.0 | Be intriguing |
| Tweet with long dwell time | +10.0 | Use threads |
| Retweet | +1.0 | Low value |
| Like | +0.5 | Lowest value |
Every tweet should have ONE of: Image, Video (< 2:20), Poll, or Thread (7-10 tweets).
ZIP package — ready to use