Back to Details

analyzing-tiktok-hashtag-performance

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

Analyzing TikTok Hashtag Performance

TikTok话题标签表现分析

Scrapes TikTok hashtag pages to pull top-performing videos, engagement data, and creator information. Compares multiple hashtags side-by-side to identify which ones deliver the best reach for a given content category.
爬取TikTok话题标签页面，获取表现最佳的视频、参与度数据及创作者信息。可将多个话题标签进行横向对比，找出在特定内容类别中触达效果最好的标签。

Prerequisites

前提条件

  • 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

ParameterTypeRequiredDefaultNotes
startUrls
arrayOptional
[]
TikTok URLs — user profiles, hashtags, music pages, search, locations
keywords
arrayOptional
[]
Search keywords/terms to find posts
sortType
stringOptional
RELEVANCE
Sort order for keyword results: 
RELEVANCE
, 
MOST_LIKED
, 
DATE_POSTED
location
stringOptional—ISO 3166-1 alpha-2 country code for regional filtering (e.g. 
US
, 
GB
)
maxItems
numberOptionalUnlimitedMaximum posts to return across the run
includeSearchKeywords
booleanOptional
false
Add the matched search keyword field to each post
customMapFunction
stringOptional—JavaScript function to transform each output object
参数类型是否必填默认值说明
startUrls
数组可选
[]
TikTok链接——用户主页、话题标签、音乐页面、搜索结果、地点页面
keywords
数组可选
[]
用于查找帖子的搜索关键词/术语
sortType
字符串可选
RELEVANCE
关键词搜索结果的排序方式：
RELEVANCE
（相关性）、
MOST_LIKED
（最受欢迎）、
DATE_POSTED
（发布日期）
location
字符串可选—用于区域过滤的ISO 3166-1 alpha-2国家代码（例如
US
、
GB
）
maxItems
数字可选无限制本次运行返回的最大帖子数量
includeSearchKeywords
布尔值可选
false
为每个帖子添加匹配的搜索关键词字段
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 hashtags to analyze
- [ ] Step 2: Run tiktok-scraper for each hashtag
- [ ] Step 3: Calculate hashtag-level metrics
- [ ] Step 4: Identify top content and creators
- [ ] Step 5: Deliver strategy recommendations
进度:
- [ ] 步骤1：定义需要分析的话题标签
- [ ] 步骤2：为每个话题标签运行tiktok-scraper
- [ ] 步骤3：计算话题标签层面的指标
- [ ] 步骤4：识别热门内容与顶级创作者
- [ ] 步骤5：输出策略建议

Step 1: Clarify Parameters

步骤1：明确参数

Ask the user for:
  • Hashtags to analyze — up to 10 (without #)
  • Posts per hashtag (default: 30 — enough for reliable stats)
  • Goal — choosing hashtags for a post, auditing a competitor's hashtag strategy, or general research
If the user hasn't provided hashtags yet and wants recommendations, ask for:
  • Content niche (e.g., "fitness", "cooking", "personal finance")
  • Then generate a mix of: 2 mega hashtags (100M+ views), 3 mid-tier (10M–100M), 3 niche (1M–10M), 2 micro (<1M)
向用户确认以下信息：
  • 待分析的话题标签——最多10个（无需添加#）
  • 每个话题标签的采样帖子数（默认值：30——足以获取可靠数据）
  • 目标——为帖子选择话题标签、审核竞争对手的话题标签策略，或进行通用研究
若用户尚未提供话题标签且需要推荐，请获取：
  • 内容细分领域（例如："健身"、"烹饪"、"个人理财"）
  • 然后生成组合标签：2个超级话题标签（浏览量1亿+）、3个中量级标签（1000万-1亿）、3个细分领域标签（100万-1000万）、2个微型标签（100万以下）

Step 2: Run the Actor Per Hashtag

步骤2：为每个话题标签运行Actor

Recommended — run_actor.js (handles waiting, output, and file saving automatically):
bash
undefined
推荐方式——run_actor.js（自动处理等待、输出及文件保存）：
bash
undefined

Quick answer (prints table to chat)

快速输出（在聊天窗口打印表格）

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

Save as CSV

保存为CSV格式

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

Save as JSON

保存为JSON格式

node scripts/run_actor.js
--actor "apidojo~tiktok-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~tiktok-scraper" Input: { "keywords": ["[hashtag1]", "[hashtag2]", "[hashtag3]"], "shouldDownloadCovers": false }

**REST API fallback:**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "keywords": ["[hashtag1]", "[hashtag2]", "[hashtag3]"]
  }'
Wait for 
SUCCEEDED
. Fetch dataset.
node scripts/run_actor.js
--actor "apidojo~tiktok-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json
> 需在环境变量或`.env`文件中设置`APIFY_TOKEN`。

**若Apify MCP可用：**
工具: apify:run-actor Actor: "apidojo~tiktok-scraper" 输入: { "keywords": ["[hashtag1]", "[hashtag2]", "[hashtag3]"], "shouldDownloadCovers": false }

**REST API备选方案：**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "keywords": ["[hashtag1]", "[hashtag2]", "[hashtag3]"]
  }'
等待任务状态变为
SUCCEEDED
，然后获取数据集。

Step 3: Calculate Hashtag Metrics

步骤3：计算话题标签指标

For each hashtag, from its video results:
total_views_sampled = sum(video.playCount)
avg_views_per_video = total_views_sampled / num_videos
avg_likes_per_video = sum(video.diggCount) / num_videos
avg_comments_per_video = sum(video.commentCount) / num_videos
engagement_rate = (avg_likes + avg_comments) / avg_views * 100
competition_level = num_videos_per_day (estimate from timestamps)
Use 
challengeInfo.stats.videoCount
(if available) as total hashtag size proxy.
针对每个话题标签，从其视频结果中计算：
sampled_total_views = sum(video.playCount)
avg_views_per_video = sampled_total_views / num_videos
avg_likes_per_video = sum(video.diggCount) / num_videos
avg_comments_per_video = sum(video.commentCount) / num_videos
engagement_rate = (avg_likes + avg_comments) / avg_views * 100
competition_level = num_videos_per_day (根据时间戳估算)
若可用，使用
challengeInfo.stats.videoCount
作为话题标签总规模的参考值。

Step 4: Identify Top Content and Creators

步骤4：识别热门内容与顶级创作者

For each hashtag, surface:
  • Top 3 videos by play count (with creator handle and video URL)
  • Top 3 creators by frequency in the hashtag's top content
  • Common content formats in top videos (based on descriptions/captions)
针对每个话题标签，筛选出：
  • 播放量排名前三的视频（包含创作者账号及视频链接）
  • 在话题标签热门内容中出现频率最高的三位创作者
  • 热门视频中的常见内容形式（基于描述/标题）

Step 5: Format Output

步骤5：格式化输出

Output Format

输出格式

undefined
undefined

TikTok Hashtag Performance Analysis

TikTok话题标签表现分析

Hashtags analyzed: [N] | Posts sampled per hashtag: [30] | Date: [DATE]
分析的话题标签数量: [N] | 每个话题标签的采样帖子数: [30] | 日期: [DATE]

Hashtag Comparison Table

话题标签对比表

HashtagAvg ViewsAvg LikesEng. RateCompetitionVerdict
#[name][N][N][X.X%][Low/Med/High][Use / Test / Avoid]
话题标签平均浏览量平均点赞量参与率竞争程度结论
#[name][N][N][X.X%][低/中/高][推荐使用 / 测试 / 避免使用]

Detailed Breakdown

详细分析

#[hashtag1]

#[hashtag1]

  • Avg views per post: [N]
  • Engagement rate: [X.X%]
  • Competition level: [Low / Medium / High] — approx [N] new posts/day
  • Top video: "[creator]" — [N] views | [url]
  • Dominant content format: [e.g., tutorial, reaction, storytelling]
  • Recommendation: [Use as primary / Layer with broader tags / Avoid — too saturated]
  • 单帖平均浏览量: [N]
  • 参与率: [X.X%]
  • 竞争程度: [低/中/高] — 每日新增帖子约[N]条
  • 热门视频: "[创作者]" — [N]次浏览 | [链接]
  • 主流内容形式: [例如：教程、reaction、故事讲述]
  • 建议: [作为主标签使用 / 搭配更宽泛的标签 / 避免使用——过于饱和]

#[hashtag2]

#[hashtag2]

[same structure]
[相同结构]

Recommended Hashtag Strategy

推荐话题标签策略

For maximum reach on [CONTENT NICHE], use this combination:
  • Primary (1-2 hashtags): [#hashtag] — broad reach driver
  • Secondary (2-3 hashtags): [#hashtag] — niche relevance
  • Micro (1-2 hashtags): [#hashtag] — community engagement
针对[内容细分领域]实现最大触达，可使用以下组合：
  • 主标签（1-2个）: [#hashtag] — 广泛触达驱动
  • 次标签（2-3个）: [#hashtag] — 细分领域相关性
  • 微型标签（1-2个）: [#hashtag] — 社区参与

Top Creators in These Hashtags

这些话题标签中的顶级创作者

[Creators who appear most in top-performing content across all analyzed hashtags]
  1. @[handle] — [N] top videos found | [N] followers
undefined
[在所有分析话题标签的热门内容中出现频率最高的创作者]
  1. @[账号] — 发现[N]条热门视频 | [N]位粉丝
undefined

Troubleshooting

故障排除

Very low view counts: Hashtag may be misspelled or very new. Verify spelling and try alternate versions. All results look the same: Mega-hashtags (#fyp, #foryou) surface algorithmically promoted content, not organic. Use niche hashtags for better signal. Engagement rate seems too high or low: Engagement rate varies heavily by content type — compare within the same content format for fair benchmarking.
浏览量极低: 话题标签可能拼写错误或非常新。请验证拼写并尝试替代版本。 所有结果看起来相同: 超级话题标签（#fyp、#foryou）展示的是算法推荐内容，而非自然流量内容。使用细分领域话题标签可获得更有效的数据信号。 参与率过高或过低: 参与率因内容类型差异极大——对比相同内容形式的标签才能得到公平的基准数据。