building-twitter-prospect-lists

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English

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Chinese

Building Twitter Prospect Lists

构建Twitter潜在客户列表

Searches Twitter/X for profiles matching a target ICP (Ideal Customer Profile) using bio keywords and topic-based tweet search. Delivers a contact-ready list with engagement signals and bio context.

通过个人简介关键词和基于话题的推文搜索，在Twitter/X上查找符合目标ICP（理想客户画像）的个人资料。提供包含互动信号和个人简介背景信息、可直接用于联系的列表。

Prerequisites

前置条件

```
APIFY_TOKEN
```
environment variable set
Optional: Apify MCP server installed

已设置
```
APIFY_TOKEN
```
环境变量
可选：已安装Apify MCP服务器

Inputs

输入参数

Parameter	Type	Required	Default	Notes
`searchTerms`	array	✅	`[]`	Twitter advanced search queries (e.g. `["#AI lang:en", "from:NASA"]` )
`sort`	string	Optional	`Top`	Sort order: `Latest` , `Top` , or `Latest+Top`
`tweetLanguage`	string	Optional	—	ISO 639-1 language code (e.g. `en` )
`maxItems`	number	Optional	Unlimited	Maximum tweets to return
`onlyVerifiedUsers`	boolean	Optional	`false`	Only tweets from verified users
`onlyTwitterBlue`	boolean	Optional	`false`	Only Twitter Blue subscribers
`onlyImage`	boolean	Optional	`false`	Only tweets with images
`onlyVideo`	boolean	Optional	`false`	Only tweets with videos
`onlyQuote`	boolean	Optional	`false`	Only quote tweets
`author`	string	Optional	—	Filter to a specific author handle
`inReplyTo`	string	Optional	—	Tweets replying to a specific handle
`mentioning`	string	Optional	—	Tweets mentioning a specific handle
`geotaggedNear`	string	Optional	—	Tweets near a location
`withinRadius`	string	Optional	—	Radius around geotaggedNear
`geocode`	string	Optional	—	Lat/lng + radius string
`placeObjectId`	string	Optional	—	Tweets tagged with a place
`minimumRetweets`	number	Optional	—	Minimum retweet count
`minimumFavorites`	number	Optional	—	Minimum like count
`minimumReplies`	number	Optional	—	Minimum reply count
`start`	string	Optional	—	Tweets after this date (YYYY-MM-DD)
`end`	string	Optional	—	Tweets before this date (YYYY-MM-DD)
`includeSearchTerms`	boolean	Optional	`false`	Add the matched search term to each tweet
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

参数	类型	是否必填	默认值	说明
`searchTerms`	数组	✅	`[]`	Twitter高级搜索查询（例如： `["#AI lang:en", "from:NASA"]` ）
`sort`	字符串	可选	`Top`	排序方式： `Latest` （最新）、 `Top` （热门）或 `Latest+Top` （最新+热门）
`tweetLanguage`	字符串	可选	—	ISO 639-1语言代码（例如： `en` ）
`maxItems`	数字	可选	无限制	返回的最大推文数量
`onlyVerifiedUsers`	布尔值	可选	`false`	仅返回已验证用户的推文
`onlyTwitterBlue`	布尔值	可选	`false`	仅返回Twitter Blue订阅用户的推文
`onlyImage`	布尔值	可选	`false`	仅返回带图片的推文
`onlyVideo`	布尔值	可选	`false`	仅返回带视频的推文
`onlyQuote`	布尔值	可选	`false`	仅返回引用推文
`author`	字符串	可选	—	筛选特定作者的账号
`inReplyTo`	字符串	可选	—	回复特定账号的推文
`mentioning`	字符串	可选	—	提及特定账号的推文
`geotaggedNear`	字符串	可选	—	特定地点附近的推文
`withinRadius`	字符串	可选	—	`geotaggedNear` 周边的半径范围
`geocode`	字符串	可选	—	纬度/经度 + 半径字符串
`placeObjectId`	字符串	可选	—	标记了特定地点的推文
`minimumRetweets`	数字	可选	—	最低转发量
`minimumFavorites`	数字	可选	—	最低点赞量
`minimumReplies`	数字	可选	—	最低回复量
`start`	字符串	可选	—	此日期之后的推文（格式：YYYY-MM-DD）
`end`	字符串	可选	—	此日期之前的推文（格式：YYYY-MM-DD）
`includeSearchTerms`	布尔值	可选	`false`	为每条推文添加匹配的搜索关键词
`customMapFunction`	字符串	可选	—	用于转换每个输出对象的JavaScript函数

Workflow

工作流程

Progress:
- [ ] Step 1: Define ICP and search strategy
- [ ] Step 2: Run tweet-scraper for keyword/topic tweets
- [ ] Step 3: Extract unique authors from results
- [ ] Step 4: Enrich with twitter-user-scraper for bio + follower data
- [ ] Step 5: Filter, rank, and deliver prospect list

进度:
- [ ] 步骤1: 定义ICP和搜索策略
- [ ] 步骤2: 运行tweet-scraper抓取关键词/话题相关推文
- [ ] 步骤3: 从结果中提取唯一作者
- [ ] 步骤4: 使用twitter-user-scraper补充个人简介+粉丝数据
- [ ] 步骤5: 筛选、排序并交付潜在客户列表

Step 1: Define ICP and Strategy

步骤1: 定义ICP与策略

Ask the user:

Job title keywords for Twitter bio search (e.g., "Head of Growth", "Founder", "CTO")
Topic keywords — what topics does the ICP tweet about? (e.g., "SaaS metrics", "PLG", "RevOps")
Industry signals — keywords that suggest the right industry in bio (e.g., "SaaS", "fintech", "healthcare")
Follower range (optional) — e.g., 1,000–50,000 (avoids both nobodies and celebrities)
Location (optional) — e.g., "San Francisco", "London"
List size — how many prospects needed?

询问用户:

职位头衔关键词 用于Twitter个人简介搜索（例如："Head of Growth", "Founder", "CTO"）
话题关键词 — 你的ICP会关注哪些话题？（例如："SaaS metrics", "PLG", "RevOps"）
行业信号 — 个人简介中能体现目标行业的关键词（例如："SaaS", "fintech", "healthcare"）
粉丝数量范围 (可选) — 例如1,000–50,000（避开无影响力用户和名人）
所在地 (可选) — 例如"San Francisco", "London"
列表规模 — 需要多少个潜在客户？

Step 2: Search for Topic-Based Tweets

步骤2: 搜索基于话题的推文

Search Twitter for tweets about topics your ICP cares about. People who actively tweet about a topic are warmer prospects.

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

bash

undefined

在Twitter上搜索ICP关注的话题相关推文。积极讨论相关话题的用户是更优质的潜在客户。

推荐方式 — run_actor.js (自动处理等待、输出和文件保存):

bash

undefined

Quick answer (prints table to chat)

快速输出（在聊天中打印表格）

node scripts/run_actor.js
--actor "apidojo~tweet-scraper"
--input '{"param": "value"}'

Save as CSV

保存为CSV文件

node scripts/run_actor.js
--actor "apidojo~tweet-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.csv --format csv

Save as JSON

保存为JSON文件

node scripts/run_actor.js
--actor "apidojo~tweet-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": ["[TOPIC_KEYWORD_1]", "[TOPIC_KEYWORD_2]"], "maxItems": 200, "tweetLanguage": "en" }


**If Apify MCP is not available:**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchTerms": ["[TOPIC_KEYWORD]"],
    "maxItems": 200
  }'

Run for each topic keyword. Collect all

author.username

values. Deduplicate. This gives you a candidate pool.

node scripts/run_actor.js
--actor "apidojo~tweet-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json

> `APIFY_TOKEN` 必须在环境变量或 `.env` 文件中设置。

**如果Apify MCP可用:**

工具: apify:run-actor Actor: "apidojo~tweet-scraper" 输入: { "searchTerms": ["[TOPIC_KEYWORD_1]", "[TOPIC_KEYWORD_2]"], "maxItems": 200, "tweetLanguage": "en" }


**如果Apify MCP不可用:**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchTerms": ["[TOPIC_KEYWORD]"],
    "maxItems": 200
  }'

为每个话题关键词执行一次。收集所有

author.username

值并去重，得到候选用户池。

Step 3: Enrich Candidates with Profile Data

步骤3: 补充候选用户的个人资料数据

Take the top 100-200 unique usernames from Step 2. Fetch full profile data to filter by bio keywords and follower count.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input:
{
  "usernames": ["[username1]", "[username2]", "..."],
  "maxItems": 100
}

If Apify MCP is not available:

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "usernames": ["[username1]", "[username2]"]
  }'

从步骤2中选取前100-200个唯一用户名，获取完整的个人资料数据，以便根据个人简介关键词和粉丝数量进行筛选。

如果Apify MCP可用:

工具: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
输入:
{
  "usernames": ["[username1]", "[username2]", "..."],
  "maxItems": 100
}

如果Apify MCP不可用:

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "usernames": ["[username1]", "[username2]"]
  }'

Step 4: Filter Against ICP Criteria

步骤4: 根据ICP标准筛选

From profile data, keep only users where ALL of these are true:

Bio contains at least one job title keyword OR industry signal keyword
Follower count is within the specified range (if given)
Location matches (if specified) — check
```
location
```
field
Account is not a bot (has profile picture, has >10 tweets, account age >6 months)

Remove:

Accounts with default profile images
Accounts with 0 tweets
Verified mega-influencers (follower count above range)
Obviously automated accounts

从个人资料数据中，仅保留满足以下所有条件的用户:

个人简介包含至少一个职位头衔关键词或行业信号关键词
粉丝数量在指定范围内（若有设置）
所在地匹配（若有指定）——检查
```
location
```
字段
账号非机器人（有头像、推文数量>10、账号创建时长>6个月）

移除:

使用默认头像的账号
推文数量为0的账号
已验证的超级网红（粉丝数超出范围）
明显的自动化账号

Step 5: Rank and Format

步骤5: 排序与格式化

Rank filtered prospects by:

Relevance score = number of ICP keywords matched in bio
Engagement proxy = (likes + retweets on recent tweets) / follower count

按照以下规则对筛选后的潜在客户进行排序:

相关性得分 = 个人简介中匹配的ICP关键词数量
互动代理值 = (近期推文的点赞数+转发数) / 粉丝数

Output Format

输出格式

undefined

undefined

Twitter Prospect List: [ICP DESCRIPTION]

Twitter潜在客户列表: [ICP描述]

Generated: [N] prospects | Filters applied: [summary] | Date: [DATE]

#	Name	Handle	Followers	Job / Bio	Location	Last Active	Profile
1	[name]	@[handle]	[N]	[bio excerpt]	[city]	[date]	[url]
2	[name]	@[handle]	[N]	[bio excerpt]	[city]	[date]	[url]

生成时间: [N]个潜在客户 | 应用的筛选条件: [摘要] | 日期: [DATE]

#	姓名	账号	粉丝数	职位/个人简介	所在地	最后活跃时间	个人资料链接
1	[name]	@[handle]	[N]	[个人简介节选]	[城市]	[日期]	[链接]
2	[name]	@[handle]	[N]	[个人简介节选]	[城市]	[日期]	[链接]

Top 10 Highest-Priority Prospects

优先级最高的10位潜在客户

@[handle] — "[bio]" | [N] followers | Recently tweeted about: [topic]
@[handle] — "[bio]" | [N] followers | Recently tweeted about: [topic] ...

@[handle] — "[个人简介]" | [N]粉丝 | 近期推文话题: [topic]
@[handle] — "[个人简介]" | [N]粉丝 | 近期推文话题: [topic] ...

Notes

备注

[N] candidates found in topic search
[N] filtered out (didn't match ICP criteria)
[N] final prospects delivered
Engagement signals are 24-48h delayed

undefined

在话题搜索中找到[N]个候选用户
[N]个用户被筛选掉（不符合ICP标准）
最终交付[N]个潜在客户
互动信号存在24-48小时延迟

undefined

Personalizing Outreach

个性化客户开发

For each top prospect, the recent tweet sample can be used to personalize outreach. Note their recent topics to reference in a first message.

对于每位高优先级潜在客户，可利用其近期推文样本来个性化开发内容。在首次沟通中提及他们近期关注的话题。

Troubleshooting

故障排除

Too few results after filtering: Broaden bio keywords (use OR logic, not AND). Try more topic keywords in Step 2. Too many irrelevant accounts: Add industry-specific keywords to bio filter (e.g., require "SaaS" or "B2B" in bio). Location filter not working: Twitter location is self-reported and inconsistent — treat it as a soft signal, not a hard filter.

筛选后结果过少: 放宽个人简介关键词（使用OR逻辑，而非AND）。在步骤2中尝试更多话题关键词。 无关账号过多: 在个人简介筛选中添加行业特定关键词（例如要求个人简介包含"SaaS"或"B2B"）。 所在地筛选无效: Twitter的所在地为用户自行填写，存在不一致性——将其视为软信号，而非硬性筛选条件。