building-twitter-industry-watchlist

核心功能：依托Apify的Twitter爬虫，筛选各行业高信号Twitter账号，通过多维度公式计算影响力评分并按账号类型分类。适用场景：商务分析师、投资者、高管等开展行业情报收集时使用。输入支持搜索词、账号ID/用户名等参数，可自定义结果数量；输出为含账号handle、粉丝数、影响力评分等的分类观察列表，支持CSV/JSON等格式。

apidojo-io

热度 8.64

最近更新 2026-09-03 04:44:53

分类：数据依赖：代码执行 / 第三方 API / MCP 服务应用场景：市场分析 / 报告撰写 / 研究 / 社交媒体 ...

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add building-twitter-industry-watchlist

Metadata

name: building-twitter-industry-watchlist
description: Builds a curated Twitter industry watchlist of key voices using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: build a Twitter watchlist for an industry, find key Twitter accounts to follow in a niche, create a curated list of thought leaders in a sector on X, identify the most influential Twitter accounts in a business category, build a Twitter list for industry monitoring, find the signal-to-noise accounts in a topic area, or compile the must-follow accounts for staying current in an industry. Returns account list with handle, follower count, engagement rate, topic focus, and influence score. Ideal for business analysts, investors, executives, and professionals doing industry intelligence.
license: Apache-2.0
metadata: {"author":"apidojo","version":"1.0","apify-actors":"apidojo/twitter-user-scraper, apidojo/tweet-scraper"}

Building a Twitter Industry Watchlist

Identifies highest-signal Twitter accounts in an industry — people whose tweets consistently generate discussion, surface new information, or shape thinking in the space.

Prerequisites

APIFY_TOKEN environment variable set
Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
`startUrls`	array	Optional	`[]`	Twitter profile or tweet URLs
`twitterHandles`	array	Optional	`[]`	Twitter usernames (without @)
`twitterUserIds`	array	Optional	`[]`	Twitter user IDs
`getFollowers`	boolean	Optional	`false`	Extract follower lists
`getFollowing`	boolean	Optional	`false`	Extract following lists
`getRetweeters`	boolean	Optional	`false`	Extract retweeters of a tweet URL
`includeUnavailableUsers`	boolean	Optional	`false`	Include unavailable/suspended users
`maxItems`	number	Optional	Unlimited	Maximum users to return
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Search for high-engagement industry tweets
- [ ] Step 2: Collect influential account handles
- [ ] Step 3: Enrich and score
- [ ] Step 4: Classify by account type
- [ ] Step 5: Deliver curated watchlist

Step 1: Search Industry Conversations

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["[INDUSTRY]", "#[industry]", "[INDUSTRY] trends", "[INDUSTRY] analysis"],
  "maxItems": 500
}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["venture capital", "#vc", "VC trends 2026"], "maxItems": 500}'

Collect authors with likeCount + replyCount >= 10 on their industry tweets.

Step 2: Score Influence

signal_score = (retweets / followers * 1000) * 0.35
             + (replies / followers * 1000) * 0.30
             + min(followers / 100000, 1) * 0.20
             + (tweeted_industry_content >= 3 in 30 days ? 1 : 0) * 0.15

Account type from bio:

FOUNDER: "founder", "CEO", "built"
INVESTOR: "partner", "VC", "investor"
ANALYST: "analyst", "researcher", "writer"
JOURNALIST: known pub or "reporter", "journalist"
PRACTITIONER: role title at company

Step 3: Edge Cases

Bot accounts: retweetCount >> likeCount → flag if retweets > 5× likes
Ambiguous type: Use PRACTITIONER as default when unclear
Multiple accounts from same company: Keep the most influential one

Output Format

# [INDUSTRY] Twitter Watchlist
Accounts: [N] | Date: [DATE]

## Founders & Operators
| Name | @Handle | Role | Followers | Avg Likes | Signal Score |
|------|---------|------|-----------|----------|-------------|

## Investors & Analysts
| Name | @Handle | Role | Followers | Signal Score |
|------|---------|------|-----------|-------------|

## Press & Media
| Name | @Handle | Publication | Followers | Signal Score |
|------|---------|------------|-----------|-------------|

## How to Create Twitter List
Go to Twitter → Lists → Create List → Add members by username

Troubleshooting

Results are news not insiders: Use #[industry] hashtag to find community members vs. general readers. Too many promotional accounts: Filter accounts where > 50% of tweets include external links. Watchlist too large: Apply score cutoff ≥ 0.60; keep ≤ 40 accounts for daily readability.