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monitoring-instagram-brand-mentions

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Original

English
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Translation

Chinese

Monitoring Instagram Brand Mentions

监测Instagram品牌提及内容

Tracks all public Instagram posts mentioning a brand — via branded hashtags, @mentions, or product name keywords. Classifies mentions by sentiment and type (UGC, complaint, press coverage, competitor comparison).
追踪所有提及品牌的公开Instagram帖子——包括品牌话题标签、@提及或产品名称关键词。会按情感倾向和提及类型（UGC、投诉、媒体报道、竞品对比）对提及内容进行分类。

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
array✅
[]
Instagram URLs — profiles, hashtags, locations, audio pages, reels
until
stringOptional—Scrape posts until this date (YYYY-MM-DD)
maxItems
numberOptionalUnlimitedMaximum posts to return
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✅
[]
Instagram链接——包括主页、话题标签、地点、音频页面、Reels
until
字符串可选—爬取截止日期（格式为YYYY-MM-DD）
maxItems
数字可选无限制返回的最大帖子数量
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: Build hashtag and keyword list
- [ ] Step 2: Run instagram-scraper for each hashtag
- [ ] Step 3: Classify mention type and sentiment
- [ ] Step 4: Identify top advocates and critics
- [ ] Step 5: Deliver brand health report
进度：
- [ ] 步骤1：构建话题标签和关键词列表
- [ ] 步骤2：针对每个话题标签运行instagram-scraper
- [ ] 步骤3：分类提及类型和情感倾向
- [ ] 步骤4：识别核心品牌支持者和批评者
- [ ] 步骤5：生成品牌健康报告

Step 1 & 2: Run instagram-scraper

步骤1 & 2：运行instagram-scraper

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、输出和文件保存）：
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~instagram-scraper"
--input '{"param": "value"}'
node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'

Save as CSV

保存为CSV文件

node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.csv --format csv
node scripts/run_actor.js
--actor "apidojo~instagram-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~instagram-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~instagram-scraper" Input: { "keywords": ["#[BRAND]", "#[BRAND]review", "#[BRAND]community"], "maxItems": 100 }

**REST API fallback:**
```bash
curl -X POST   "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{"keywords": ["#[brand]", "#[brand]review"], "maxItems": 100}'
Run for each hashtag cluster. Merge results and deduplicate by 
postUrl
.
node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json
> 必须在环境变量或`.env`文件中设置`APIFY_TOKEN`。

**如果Apify MCP可用：**
工具：apify:run-actor Actor: "apidojo~instagram-scraper" 输入： { "keywords": ["#[BRAND]", "#[BRAND]review", "#[BRAND]community"], "maxItems": 100 }

**REST API备选方案：**
```bash
curl -X POST   "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{"keywords": ["#[brand]", "#[brand]review"], "maxItems": 100}'
针对每个话题标签组运行爬虫。合并结果并通过
postUrl
去重。

Step 2: Classify Mentions

步骤2：分类提及内容

Mention type:
UGC = post contains product photo + brand mention; author is not verified
COMPLAINT = caption contains negative indicators: "broken", "disappointed", "scam", "refund", "terrible", "never again"
POSITIVE_REVIEW = caption contains: "love", "amazing", "best", "recommend", "obsessed"
PRESS/EDITORIAL = author is verified OR follower_count > 100K
COMPETITOR_COMPARISON = caption mentions competitor brand alongside this brand
Sentiment: Apply same lexical classification as Twitter sentiment skill (positive/negative/neutral indicators).
提及类型：
UGC = 帖子包含产品照片+品牌提及；作者未认证
COMPLAINT = 文案包含负面词汇："broken"、"disappointed"、"scam"、"refund"、"terrible"、"never again"
POSITIVE_REVIEW = 文案包含正面词汇："love"、"amazing"、"best"、"recommend"、"obsessed"
PRESS/EDITORIAL = 作者已认证 或 粉丝数>10万
COMPETITOR_COMPARISON = 文案同时提及本品牌和竞品品牌
情感倾向： 采用与Twitter情感分析工具相同的词汇分类方式（正面/负面/中性词汇）。

Step 3: Score Reach

步骤3：计算传播范围得分

mention_reach = likes + comments * 5 + (followers_of_author / 100)
mention_reach = 点赞数 + 评论数*5 + (作者粉丝数/100)

Step 4: Edge Cases

步骤4：边缘情况处理

  • Official brand account's own posts in results: Drop posts where 
    ownerUsername
    = brand's own handle
  • Hashtag is overloaded (> 1M posts): Use long-tail branded hashtags instead; or filter by date
  • Sentiment misclassified for complex posts: Flag posts with both positive and negative indicators as 
    MIXED
    ; report count
  • Foreign language mentions dominant: Report language distribution; flag non-English mentions separately
  • 结果中包含品牌官方账号自身帖子：过滤掉
    ownerUsername
    等于品牌官方账号的帖子
  • 话题标签过载（帖子数>100万）：改用长尾品牌话题标签；或按日期过滤
  • 复杂帖子情感分类错误：将同时包含正面和负面词汇的帖子标记为
    MIXED
    ；统计这类帖子的数量
  • 外文提及占主导：报告语言分布情况；单独标记非英文提及内容

Output Format

输出格式

undefined
undefined

Instagram Brand Mention Monitor: [BRAND]

Instagram品牌提及监测报告：[BRAND]

Posts collected: [N] | Period: [DATE_RANGE] | Date: [DATE]
收集到的帖子数量：[N] | 监测周期：[DATE_RANGE] | 报告日期：[DATE]

Mention Type Distribution

提及类型分布

UGC: [N] | Positive Reviews: [N] | Complaints: [N] | Press: [N] | Comparisons: [N]
UGC：[N] | 正面评价：[N] | 投诉：[N] | 媒体报道：[N] | 竞品对比：[N]

Sentiment Summary

情感倾向汇总

Positive: [X%] | Negative: [X%] | Neutral: [X%] Weighted by reach: Positive [X%] | Negative [X%]
正面：[X%] | 负面：[X%] | 中性：[X%] 按传播范围加权：正面 [X%] | 负面 [X%]

Top UGC Posts (Most Liked)

热门UGC帖子（点赞量最高）

Creator@HandleLikesTypeCaption ExcerptPost URL
创作者@账号点赞数类型文案摘要帖子链接

Complaints to Address

需要处理的投诉

CreatorLikesComplaint SummaryPost URL
创作者点赞数投诉摘要帖子链接

Top Brand Advocates (Most Frequent Positive Posters)

核心品牌支持者（发布正面内容最多）

  1. @[handle] — [N] positive posts | [N] avg likes
undefined
  1. @[账号] — [N]条正面帖子 | 平均点赞数[N]
undefined

Troubleshooting

故障排除

Hashtag returns generic posts: The brand hashtag may be ambiguous (e.g. "#apple"). Use 
#[brand]official
or 
#[brand][product]
for precision. Mostly competitor posts: This may indicate your brand is being used in comparison posts — analyze 
COMPETITOR_COMPARISON
category for positioning insights. Sentiment skewed by a single viral negative post: Check 
weighted sentiment
vs. raw sentiment; one viral post can shift the raw numbers.
话题标签返回无关帖子：品牌话题标签可能存在歧义（例如"#apple"）。改用
#[brand]official
或
#[brand][product]
来提高精准度。 结果中多为竞品帖子：这可能表明你的品牌被用于对比帖子中——分析
COMPETITOR_COMPARISON
类别以获取品牌定位洞察。 单条 viral 负面帖子导致情感倾向偏差：对比查看“加权情感倾向”和“原始情感倾向”；一条 viral 帖子可能会影响原始数据结果。