Twitter Intel — Keyword Search & Trend Monitor

Search Twitter by keyword, collect high-engagement tweets, analyze trends over time, and generate structured reports. Powered by rnet_twitter.py GraphQL search (no browser automation needed).

Architecture

Phase 1: On-demand Search (user-triggered)
  User says "search OpenAI on twitter" -> search -> filter -> report

Phase 2: Keyword Monitoring (cron-driven)
  Config defines keywords -> scheduled search -> diff with last run -> alert on new high-engagement tweets

Phase 3: Trend Analysis (on-demand or weekly)
  Aggregate saved searches -> group by week -> detect topic shifts -> generate narrative

Prerequisites

# Install rnet
pip install "rnet>=3.0.0rc20" --pre

# Required: rnet_twitter.py + cookies
# - rnet_twitter.py (included in this repo, has search_tweets method)
# - twitter_cookies.json (auth_token + ct0, valid ~2 weeks)

Cookie refresh: When search returns 403, cookies need refresh. Get new auth_token + ct0 from Chrome DevTools -> Application -> Cookies -> x.com.

Phase 1: On-demand Search

When user says "search [keyword] on twitter", "twitter intel [topic]", "find tweets about [X]":

Step 1 — Run Search

import asyncio
from rnet_twitter import RnetTwitterClient

async def search(query, count=200):
    client = RnetTwitterClient()
    client.load_cookies("twitter_cookies.json")
    tweets = await client.search_tweets(query, count=count, product="Top")
    return tweets

Search modes:

Mode	`product=`	Use case
High-engagement	`"Top"`	Find influential tweets, content analysis
Real-time	`"Latest"`	Monitor breaking discussions, live tracking

Useful Twitter search operators:

Operator	Example	Effect
`lang:en`	`OpenAI lang:en`	English only
`since:` / `until:`	`since:2026-01-24 until:2026-02-24`	Date range
`-filter:replies`	`OpenAI -filter:replies`	Original tweets only
`min_faves:N`	`min_faves:50`	Minimum likes (only works with Latest)
`from:`	`from:karpathy`	Specific author
`"exact"`	`"AI agent"`	Exact phrase

Step 2 — Filter & Enrich

After raw search, filter for quality:

# Filter: relevant + has engagement
filtered = [
    t for t in tweets
    if keyword.lower() in t["text"].lower()  # actually mentions keyword
    and (t["favorite_count"] >= 10 or t["retweet_count"] >= 5)  # has engagement
    and not t["is_reply"]  # original tweets preferred
]

Step 3 — Report

Output a structured summary:

## Twitter Intel: [keyword]
**Period:** [date range] | **Tweets found:** N | **After filter:** N

### Top Tweets (by engagement)
1. @author (X likes, Y RTs, Z views) — date
   "tweet text..."
   [link]

2. ...

### Key Themes
- Theme 1: [description] (N tweets)
- Theme 2: [description] (N tweets)

### Notable Authors
| Author | Followers | Tweets in set | Total engagement |
|--------|-----------|---------------|-----------------|

Phase 2: Keyword Monitoring (Cron)

Config File

{
  "monitors": [
    {
      "id": "my-product-en",
      "query": "MyProduct lang:en -filter:replies",
      "product": "Top",
      "count": 100,
      "min_likes": 10,
      "alert_threshold": 100,
      "enabled": true
    },
    {
      "id": "competitor-mentions",
      "query": "CompetitorName OR \"brand consistency\" lang:en",
      "product": "Latest",
      "count": 50,
      "min_likes": 5,
      "alert_threshold": 50,
      "enabled": true
    }
  ]
}

State File

{
  "my-product-en": {
    "last_run": "2026-02-24T12:00:00Z",
    "last_tweet_ids": ["id1", "id2", "..."],
    "total_collected": 450
  }
}

Cron Workflow

Read config -> iterate enabled monitors
For each monitor:
- Run search_tweets(query, count, product)
- Filter by min_likes
- Diff against last_tweet_ids -> find NEW tweets only
- If any new tweet has favorite_count >= alert_threshold -> immediate alert
- Save all new tweets to daily file {monitor_id}/YYYY-MM-DD.json
- Update state file
Send summary notification (if there are new notable tweets)

Phase 3: Trend Analysis

When user says "analyze twitter trend for [keyword]", "twitter trend report":

Workflow

Load all saved daily files from {monitor_id}/
Group tweets by week
For each week, extract:
- Total tweet count + total engagement
- Top 5 tweets by likes
- Dominant themes (use LLM to categorize)
- New authors that appeared
- Sentiment shift
Generate a week-by-week narrative

Output Format

## Trend Report: [keyword]
**Period:** Week 1 (Jan 24-26) to Week 5 (Feb 17-23)
**Total tweets:** N | **Total engagement:** X likes, Y RTs

### Week-by-Week Evolution

#### Week 1 (Jan 24-26): [Theme title]
- Dominant narrative: ...
- Top tweet: @author — "..."
- Key signal: ...

#### Week 2 (Jan 27-Feb 2): [Theme title]
...

### Trend Shifts Detected
1. [Shift description] — happened in Week X
2. ...

### Top Authors Across Period
| Author | Appearances | Total Likes | First seen |

Commands

User Says	Agent Does
`/twitter-intel [keyword]`	Search + filter + report (Top, 200 tweets)
`/twitter-intel "[phrase]" --latest`	Search Latest mode
`monitor "[keyword]" on twitter`	Add to monitoring config
`twitter intel status`	Show all active monitors + last run
`twitter trend report [keyword]`	Analyze saved data, generate trend narrative
`refresh twitter cookies`	Guide user through cookie refresh

Technical Notes

SearchTimeline requires POST (GET returns 404) — this is handled by rnet_twitter.py
GraphQL query IDs rotate — if search returns 404, re-extract the SearchTimeline ID from https://abs.twimg.com/responsive-web/client-web/main.*.js
User data path (2026-02): screen_name is now at core.user_results.result.core.screen_name (not .legacy)
Rate limits: ~300 requests/15min window. With 20 tweets per page, 200 tweets = 10 requests. Safe for cron every 4 hours.
Cookie lifetime: auth_token expires after ~2 weeks. Monitor for 403 errors.

twitter-intel