Find influencers in any niche, analyze their audience quality, and vet them for authentic engagement — all through the API.
Finding the right influencers is time-consuming. Follower counts are misleading — you need to analyze engagement quality, audience authenticity, and content relevance. Manual vetting doesn't scale.
Search for users by niche keywords, then analyze their followers, engagement rates, and content patterns using ScrapeBadger. Build automated influencer scoring and discovery pipelines.
import ScrapeBadger from 'scrapebadger';
const sb = new ScrapeBadger({ apiKey: 'YOUR_API_KEY' });
// Find influencers in a niche
const users = await sb.twitter.users.searchUsers({ query: 'data science' });
for (const user of users.data) {
if (user.followers_count > 5000) {
const tweets = await sb.twitter.users.getLatestTweets(user.username);
const avgLikes = tweets.data.reduce((s, t) => s + t.favorite_count, 0) / tweets.data.length;
const engagementRate = (avgLikes / user.followers_count) * 100;
console.log(`@${user.username}: ${user.followers_count} followers, ${engagementRate.toFixed(2)}% engagement`);
}
}Engagement rate = (average likes + retweets per tweet) / follower count * 100. Rates above 1% are generally good; above 3% is excellent.
Analyze follower profiles: look for default avatars, empty bios, very low follower counts, and recent creation dates. A high ratio of these suggests fake followers.
Yes. Filter search results for users with 1K-50K followers. Micro-influencers often have higher engagement rates and more targeted audiences.
Pull the influencer's recent tweets and analyze topics. Check if their content consistently covers your target niche, not just occasionally.
Monitor competitor accounts, analyze their content strategy, track engagement rates, and benchmark your performance against theirs.
Find potential customers by monitoring purchase-intent keywords, competitor complaints, and industry discussions on Twitter.
Extract tweets, replies, and mentions programmatically and feed them into your NLP pipeline for real-time sentiment classification.