/apify-ultimate-scraper

Trend and keyword research workflows

Google Trends analysis

When: User wants to analyze search demand trends for keywords or topics.

Pipeline

1. Get trend data -> apify/google-trends-scraper
   • Key input: searchTerms, timeRange, geo (country code)

Output fields

Step 1: term, timelineData[] (date, value), relatedQueries[], relatedTopics[]

Cross-platform hashtag research

When: User wants to evaluate a hashtag's reach and usage across platforms.

Pipeline

1. Cross-platform overview -> apify/social-media-hashtag-research
   • Key input: hashtags, platforms (instagram, youtube, tiktok, facebook)

Output fields

Step 1: hashtag, platform, postsCount, topPosts[], relatedHashtags[]

TikTok trend discovery

When: User wants to find trending content, sounds, or hashtags on TikTok.

Pipeline

1. Trending content -> clockworks/tiktok-trends-scraper
   • Key input: channel (trending category)
2. Explore categories -> clockworks/tiktok-explore-scraper
   • Key input: exploreCategories

Output fields

Step 1: videoUrl, description, likes, shares, views, author, music Step 2: category, posts[], authors[], music[]

Reddit trend and community insight mining

When: User wants to surface emerging trends, product feedback themes, or competitor mentions from Reddit communities.

Pipeline

1. Scrape subreddits -> trudax/reddit-scraper-lite
   • Key input: startUrls (subreddit URLs), maxItems, sort (hot/rising/new)

Output fields

Step 1: title, body, subreddit, score, numberOfComments, url, createdAt, comments[]

Gotcha

Use sort: rising for early trend signals, sort: hot for confirmed trending topics. Filter by score threshold (e.g., >50) to reduce noise. Comments array provides qualitative context for AI sentiment analysis.

YouTube outlier video discovery

When: User wants to identify breakout videos in a niche with disproportionate views vs. channel subscriber count - a signal for content strategy pivots.

Pipeline

1. Search niche videos -> streamers/youtube-scraper
   • Key input: searchTerms, maxResults, sort (viewCount), uploadDate (filter range)
2. Get channel context -> streamers/youtube-channel-scraper
   • Pipe: results[].channelUrl -> channelUrls

Output fields

Step 1: title, viewCount, likeCount, commentCount, channelName, publishedAt, url Step 2: subscriberCount, videoCount, viewCount (channel totals)

Gotcha

Outlier score = video viewCount / channel subscriberCount. Ratios > 10x indicate breakout potential. Run Step 2 on a filtered shortlist only - no need to fetch channel data for every result.

Content topic validation

When: User wants to validate whether a topic has demand before creating content.

Pipeline

1. Search demand -> apify/google-trends-scraper
   • Key input: searchTerms (topic keywords)
2. Social reach -> apify/social-media-hashtag-research
   • Key input: hashtags (topic hashtags)

Output fields

Step 1: timelineData[] (trending up/down), relatedQueries[] Step 2: postsCount per platform, topPosts[]

Gotcha

Google Trends shows relative interest (0-100 scale), not absolute volume. Combine with hashtag post counts for a fuller picture.