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Most X APIs give you tweet text and metadata. That is useful for retrieval, but it often misses the actual message. On X, meaning is frequently split across the main tweet, the quoted tweet, and the media attached to one or both posts. VidNavigator’s Tweet Claim Analysis endpoint is built for that exact problem. It reconstructs intent across all of those layers and returns one structured intelligence object you can use in verification, monitoring, analytics, or AI workflows.

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What This Guide Helps You Do

By the end of this guide, you will know how to:
  • decide when tweet claim analysis is the right tool
  • send a valid tweet_id
  • understand what the API reads before it writes a result
  • use final_statement, statement_query, and the classification axes correctly
  • plug the result into fact-checking, social monitoring, or clustering workflows

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When to Use This Endpoint

Use tweet claim analysis when you care about what the author meant, not just what they typed. Good fits:
  • fact-checking and verification
  • narrative tracking across many posts
  • reputation and risk monitoring
  • clustering similar claims into dashboards
  • turning noisy social posts into structured AI-ready data
If you only need raw tweet text, engagement metrics, or basic metadata, this endpoint is probably more powerful than you need.

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Why This Is Different from Basic Tweet Retrieval

A normal tweet API can tell you:
  • the tweet text
  • the author
  • the timestamp
  • public metrics
This endpoint goes further. It:
  • reads the original tweet
  • checks whether a quoted tweet changes or completes the meaning
  • analyzes media attached to the original tweet
  • analyzes media attached to the quoted tweet
  • returns one structured interpretation of the overall claim
That is the difference between collecting posts and extracting usable intelligence.

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What the API Actually Returns

Final Claim

final_statement is the normalized claim the author appears to be making after the full context is analyzed.

Search Query

statement_query turns that meaning into a concise search-ready sentence you can reuse for retrieval or clustering.

Detailed Analysis

detailed_analysis explains the claim, context, evidence, and stance in plain language.

Classification Axes

claim_type, intent, tone, emotion, and authority classify the original tweet text on fixed taxonomies.
The classification axes are based on the original tweet text only. The broader fields such as final_statement and media summaries can reflect quoted content and attached media.

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How It Works

  1. You send a numeric tweet_id.
  2. VidNavigator fetches the original tweet.
  3. If the tweet quotes another post, VidNavigator fetches that too.
  4. If either layer includes video or audio, VidNavigator retrieves transcripts or falls back to speech-to-text when needed.
  5. The full context is analyzed and returned as a structured claim object.
This is especially useful when the visible text is vague, sarcastic, reactive, or incomplete without the quoted post or media evidence.

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Quickstart

The endpoint accepts a tweet ID, not a full URL. For example, in: https://x.com/user/status/1912345678901234567 the tweet_id is: 1912345678901234567

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Basic Request

curl -X POST "https://api.vidnavigator.com/v1/tweet/statement" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "tweet_id": "1912345678901234567"
  }'

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How to Read the Response

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final_statement

This is the field most teams care about first. It answers: What is this post really claiming once all context is taken into account? Use it for:
  • human review
  • case summaries
  • analyst handoff
  • verification-ready claim normalization

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statement_query

This is a shorter, search-oriented version of the claim. It is useful when you want to:
  • search for corroborating evidence
  • cluster similar claims together
  • build dashboards around recurring narratives
  • pass the claim into another search or retrieval system

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detailed_analysis

This is the explanation layer. It helps reviewers understand why the system interpreted the post the way it did.

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topics and entities

Use these for filtering, tagging, grouping, alerting, or analytics.

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Media summaries

tweet_media_summary and quoted_media_summary explain what the attached media contributed to the meaning, if media exists in either layer.

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Classification Axes

The endpoint returns five classification axes for the original tweet text:
  • claim_type: what kind of post it is, such as factual_claim, opinion, or news_sharing
  • intent: what the author seems to be trying to do, such as inform, persuade, or challenge
  • tone: how the message is delivered, such as serious, skeptical, or provocative
  • emotion: the dominant emotional signal, such as urgency, outrage, or hope_inspiration
  • authority: what kind of backing the tweet appears to rely on, such as data_driven, expert_led, or speculative
These labels are useful for routing, analytics, prioritization, and moderation workflows.

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Common Workflows

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Fact-checking

Use final_statement as the normalized claim to verify. Then use statement_query to search for reporting, source documents, or related video evidence.

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Reputation and crisis monitoring

Track emotionally charged posts, accusations, or fast-moving narratives. The combination of claim_type, tone, emotion, and media summaries gives you more signal than raw keyword alerts.

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Narrative clustering

Store statement_query, topics, and entities for every analyzed tweet. That makes it much easier to group similar claims even when the original wording differs.

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AI product integration

Feed the output into dashboards, RAG pipelines, search systems, or internal tools that need clean structured social intelligence instead of raw tweet text.

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Billing and Media Notes

Billing depends on how much context the endpoint has to analyze:
  • a text-only tweet is the simplest case
  • a tweet with attached media can trigger transcript retrieval or speech-to-text
  • a quoted tweet with its own media can add another layer of processing
In practice, this means some tweets are lightweight and some are multimodal analysis jobs. The endpoint handles that automatically, but it is useful to know why two tweets may not cost the same.

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Troubleshooting

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Invalid tweet ID

Send only the numeric ID. Do not send the full X URL.

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Missing media summaries

If no media exists, or the media could not be processed, tweet_media_summary and quoted_media_summary may be null.

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Unexpected classification

Remember that the five classification axes are based on the original tweet text only, while final_statement reflects the broader contextual interpretation.

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Upstream fetch issues

If X content cannot be fetched or parsed upstream, the endpoint may return an error such as 502.

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Next Steps