# Viral score of Conference recap tweets on X (Twitter)

**Use case:** 

Estimate a Viral Score from 0 to 100 for conference recap tweets. Find recaps that inform readers & open with a strong first line.

## Input

```json
{
  "searchTerms": [
    "(conference OR summit OR keynote) (recap OR takeaways OR learned) lang:en -filter:retweets"
  ],
  "texts": [
    "We shipped dark mode today. Try it and tell us what breaks."
  ],
  "maxItems": 100000,
  "analysis": {
    "targets": [],
    "context": "Judge each post as a general reader who does not follow the author."
  },
  "monitor": {
    "maxBaselineRows": 100000
  },
  "twitterContent": "web scraping OR #datascience",
  "twitterHandles": [
    "elonmusk",
    "@nasa"
  ],
  "tweetIds": [
    "1846987139428634858"
  ],
  "listIds": [
    "1748648376080666720"
  ],
  "mode": "legacy",
  "queryType": "Latest",
  "lang": "en",
  "filter:has_engagement": false,
  "include:nativeretweets": false,
  "filter:twimg": false,
  "filter:native_video": false,
  "filter:vine": false,
  "filter:consumer_video": false,
  "filter:pro_video": false,
  "filter:spaces": false,
  "filter:mentions": false,
  "filter:hashtags": false,
  "includeSearchTerms": true,
  "respectProfileSubpages": false,
  "min_faves": 0,
  "-min_faves": 0,
  "min_retweets": 0,
  "-min_retweets": 0,
  "min_replies": 0,
  "-min_replies": 0,
  "filter:blue_verified": false,
  "filter:nativeretweets": false,
  "filter:replies": false,
  "filter:quote": false,
  "filter:media": false,
  "filter:images": false,
  "filter:videos": false,
  "filter:links": false,
  "filter:news": false,
  "filter:safe": false,
  "within": "10km",
  "geocode": "37.7749,-122.4194,10km",
  "includeRaw": false,
  "includeArticles": false,
  "includeUnavailableFields": false,
  "includeOriginalTweet": false
}
```

## Output

```json
{
  "viralScore": {
    "label": "Viral Score",
    "format": "integer"
  },
  "viralVerdict": {
    "label": "Verdict",
    "format": "string"
  },
  "viralAlgorithmScore": {
    "label": "Algorithm Score",
    "format": "number"
  },
  "viralActualEngagementRate": {
    "label": "Actual engagement rate",
    "format": "number"
  },
  "answers": {
    "label": "Answers",
    "format": "object"
  },
  "tweet": {
    "label": "Original tweet",
    "format": "object"
  },
  "sourceDomains": {
    "label": "Linked domains",
    "format": "array"
  },
  "cashtags": {
    "label": "Cashtags",
    "format": "array"
  }
}
```

## About this Actor

This example demonstrates how to use [X Tweet Viral Score Analyzer with AI | $0.0003/Tweet](https://apify.com/xquik/x-tweet-viral-score-analyzer.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/xquik/x-tweet-viral-score-analyzer.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/xquik/x-tweet-viral-score-analyzer.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).