# Classify video game bug reports on X (Twitter) posts

**Use case:** 

Sort player posts into crash, gameplay, matchmaking or account bugs with a severity score, so studios prioritize fixes from public reports.

## Input

```json
{
  "searchTerms": [
    "(game OR patch) (bug OR crash OR glitch OR \"cant log in\") lang:en -filter:retweets"
  ],
  "texts": [
    "We shipped dark mode today. Try it and tell us what breaks."
  ],
  "maxItems": 100000,
  "analysis": {
    "questions": [
      {
        "id": "bug",
        "type": "choice",
        "version": "1",
        "instructions": "What kind of bug is reported?",
        "categories": {
          "crash": "Crashes.",
          "gameplay": "Gameplay defects.",
          "matchmaking": "Online matchmaking.",
          "account": "Login or account.",
          "none": "No bug.",
          "unclear": "Not enough context."
        }
      },
      {
        "id": "severity",
        "type": "score",
        "version": "1",
        "instructions": "How severe is the problem for the author?",
        "levels": [
          "Cosmetic.",
          "Annoying but playable.",
          "Blocks play."
        ]
      }
    ]
  },
  "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
{
  "tweet": {
    "label": "Original tweet",
    "format": "object"
  },
  "analysis": {
    "label": "Analysis",
    "format": "object"
  },
  "monitor": {
    "label": "Change since baseline",
    "format": "object"
  }
}
```

## About this Actor

This example demonstrates how to use [X Tweet Classifier with AI Analysis | $0.0003/Tweet](https://apify.com/xquik/x-twitter-tweet-classifier.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/xquik/x-twitter-tweet-classifier.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-twitter-tweet-classifier.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`).