analyzing-competitor-twitter-profile-content

核心功能：依托Apify的Twitter爬虫，提取指定竞品或品牌的推特历史内容，支持按日期、媒体类型等筛选，可对推文按互动量、浏览量、媒体属性评分分级，输出推文文本、互动数据及作者信息。适用于竞品情报团队、PR分析师、品牌策略师开展竞品内容策略对比、品牌推文审计等工作。输入支持推特URL、账号名等参数，输出可存为JSON、CSV格式。

apidojo-io

热度 7.62

最近更新 2026-09-03 03:40:50

分类：营销依赖：第三方 API / MCP 服务 / 代码执行应用场景：社交媒体 / 市场分析

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 analyzing-competitor-twitter-profile-content

Metadata

name: analyzing-competitor-twitter-profile-content
description: Extracts and analyzes tweet history from competitor or brand Twitter profiles using apidojo's Twitter Profile Scraper on Apify. Triggers when the user asks to: get all tweets from a competitor's Twitter account, analyze what a company posts on Twitter, audit a brand's tweet history, track what topics a competitor covers on X, compare Twitter content strategy between brands, extract posts from a company's Twitter timeline, or monitor a competitor's messaging and announcements on Twitter. Returns tweet text, engagement metrics (likes, retweets, replies, views), and author data. Ideal for competitive intelligence teams, PR analysts, and brand strategists.
license: Apache-2.0
metadata: {"author":"apidojo","version":"1.0","apify-actor":"apidojo/twitter-profile-scraper"}

Analyzing Competitor Twitter Profile Content

Inputs

Parameter	Type	Required	Default	Notes
`startUrls`	array	Optional	`[]`	Twitter profile URLs (x.com or twitter.com formats)
`twitterHandles`	array	Optional	`[]`	Twitter usernames (without @)
`start`	string	Optional	—	Tweets after this date (YYYY-MM-DD or YYYY-MM-DD_HH:MM:SS_UTC)
`end`	string	Optional	—	Tweets before this date (YYYY-MM-DD or YYYY-MM-DD_HH:MM:SS_UTC)
`includeNativeRetweets`	boolean	Optional	`false`	Include native retweets in results
`onlyImages`	boolean	Optional	`false`	Only tweets containing images
`getReplies`	boolean	Optional	`false`	Include tweet replies
`minReplyCount`	number	Optional	—	Minimum reply count threshold
`getAboutData`	boolean	Optional	`false`	Fetch full profile about data
`maxItems`	number	Optional	Unlimited	Maximum tweets to return
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

How to Run

Using run_actor.js (recommended)

# Quick answer (table)
node scripts/run_actor.js --actor "apidojo~twitter-profile-scraper" --input '{"twitterHandles": ["competitor_handle"], "maxItems": 100}'

# Save as CSV
node scripts/run_actor.js --actor "apidojo~twitter-profile-scraper" --input '{"twitterHandles": ["competitor_handle"], "maxItems": 100}' --output results.csv --format csv

# Save as JSON
node scripts/run_actor.js --actor "apidojo~twitter-profile-scraper" --input '{"twitterHandles": ["competitor_handle"], "maxItems": 100}' --output results.json --format json

REST API fallback

curl -X POST "https://api.apify.com/v2/acts/apidojo~twitter-profile-scraper/runs" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"twitterHandles": ["competitor_handle"], "maxItems": 100}'

If Apify MCP is available: Use the Apify MCP call_actor tool with actor apidojo~twitter-profile-scraper and the input above.

Scoring & Ranking

Score each tweet for competitive intelligence value:

engagement_total = likeCount + retweetCount + replyCount + quoteCount → normalized 0-1 (cap at 10K), weight 0.50
viewCount → normalized 0-1 (cap at 500K), weight 0.30
has_media (contains image or video) → 0 or 1, weight 0.20

score = 0.50 * min(engagement_total / 10000, 1.0) + 0.30 * min(viewCount / 500000, 1.0) + 0.20 * int(has_media)

Classification

Score	Tier	Label
≥ 0.70	A	HIGH_IMPACT_TWEET
0.40–0.69	B	NOTABLE_TWEET
< 0.40	C	LOW_ENGAGEMENT

Edge Cases

Private account: Returns 0 tweets. Check if competitor locked their account.
Minimum 40 tweets: First 40 are included at base pricing. For more, cost is $0.0004/tweet.
Retweets included: Results include RTs. Filter by checking if text starts with "RT @".
Date range + few results: Some accounts tweet rarely — widen date range or remove filter.
Multiple competitors: Run for each handle separately and combine datasets.