Automated Twitter Thread Generator: From Blog Post to Viral Thread
Twitter (X) hates links. If you just tweet "New blog post! [Link]", nobody will see it. The algorithm suppresses traffic that leaves the platform. The solution? Threads.
But writing threads is tedious. In this guide, we'll build a "Style-Aware" Thread Generator. It doesn't just chop up text; it analyzes your past successful tweets to mimic your unique voice before formatting your blog post.
The "Style-Match" Algorithm
Before generating content, our script asks:
- Brevity: Do you write short, punchy lines or long paragraphs?
- Formatting: Do you use emojis? Bullet points?
- Hooks: How do you start your viral tweets?
Prerequisites
You'll need a SociaVault API Key and your Twitter Handle.
The Generator Script
This Node.js script first "learns" your style, then applies it to a raw blog post.
generate-thread.js
const axios = require('axios');
// CONFIGURATION
const API_KEY = 'YOUR_SOCIAVAULT_API_KEY';
const TWITTER_HANDLE = 'shl'; // Replace with your handle
const BLOG_CONTENT = `
The 30-Day Content Challenge is the best way to grow your audience.
Most people fail because they rely on motivation instead of systems.
By pre-planning 30 days of topics, you remove decision fatigue.
This allows you to focus purely on execution.
The result is a consistent feedback loop that trains the algorithm to recognize you.
`;
const headers = {
'x-api-key': API_KEY,
'Content-Type': 'application/json'
};
async function generateThread() {
try {
console.log(`๐ง Learning style from: @${TWITTER_HANDLE}`);
// --- STEP 1: Analyze Past Performance ---
const tweetsUrl = `https://api.sociavault.com/v1/scrape/twitter/user-tweets?handle=${TWITTER_HANDLE}&trim=true`;
const tweetsResponse = await axios.get(tweetsUrl, { headers });
const tweets = tweetsResponse.data.data.tweets || [];
// Simple Style Analysis
let useEmojis = 0;
let avgLength = 0;
let useBullets = 0;
tweets.forEach(tweet => {
if (tweet.text.match(/[\u{1F600}-\u{1F64F}]/u)) useEmojis++;
if (tweet.text.includes('โข') || tweet.text.includes('- ')) useBullets++;
avgLength += tweet.text.length;
});
const styleProfile = {
emojiHeavy: useEmojis > (tweets.length / 2),
bulletHeavy: useBullets > (tweets.length / 3),
avgLength: Math.floor(avgLength / tweets.length)
};
console.log(`\n๐จ Style Profile Detected:`);
console.log(` - Emojis: ${styleProfile.emojiHeavy ? 'Yes โ
' : 'No โ'}`);
console.log(` - Bullets: ${styleProfile.bulletHeavy ? 'Yes โ
' : 'No โ'}`);
console.log(` - Avg Length: ${styleProfile.avgLength} chars`);
// --- STEP 2: Generate Thread (Rule-Based Mock AI) ---
console.log(`\n๐ Generating Thread...`);
const sentences = BLOG_CONTENT.split('.').filter(s => s.trim().length > 0);
const thread = [];
// Tweet 1: The Hook
let hook = sentences[0].trim();
if (styleProfile.emojiHeavy) hook = "๐งต " + hook + " ๐";
thread.push(hook);
// Tweet 2: The Problem
let problem = sentences[1].trim();
if (styleProfile.bulletHeavy) problem = "โ " + problem;
thread.push(problem);
// Tweet 3: The Solution (Body)
let solution = sentences.slice(2, 4).join('. ').trim() + ".";
thread.push(solution);
// Tweet 4: The CTA
const cta = "Read the full breakdown here: [LINK]";
thread.push(cta);
// --- STEP 3: Output ---
console.log(`\nโจ Your Custom Thread (${thread.length} tweets):`);
console.log(`----------------------------------------`);
thread.forEach((t, i) => {
console.log(`\n[${i + 1}/${thread.length}]`);
console.log(t);
console.log(`(${t.length} chars)`);
});
console.log(`----------------------------------------`);
} catch (error) {
console.error('Error generating thread:', error.response ? error.response.data : error.message);
}
}
generateThread();
Why This Works
1. Consistency
By analyzing your own tweets, the script ensures the new content doesn't sound like a generic bot. If you never use emojis, it won't force them on you.
2. The "Hook-Body-CTA" Structure
The script enforces a proven structure:
- Tweet 1: Hook (Statement of value).
- Tweet 2: Agitation (Why the current way fails).
- Tweet 3: Solution (The core insight).
- Tweet 4: CTA (Link to blog).
3. Character Limits
While this simple script splits by sentences, a production version (using an LLM API) would rewrite sentences to perfectly fit the 280-character limit while maximizing impact.
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