Tweet Claim Analysis - VidNavigator API

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.

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, detailed_analysis, and the classification axes correctly
plug the result into fact-checking, social monitoring, or clustering workflows

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.

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 (videos are transcribed; images are described by a vision model)
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.

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.

Detailed Analysis

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

Topics & Entities

topics and entities surface the key subjects and named people/organizations/places for tagging and clustering.

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.

How It Works

You send a numeric tweet_id.
VidNavigator fetches the original tweet.
If the tweet quotes another post, VidNavigator fetches that too.
If either layer includes video or audio, VidNavigator retrieves transcripts or falls back to speech-to-text when needed. Attached images are described by a vision model.
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.

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

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"
  }'

import requests

response = requests.post(
    "https://api.vidnavigator.com/v1/tweet/statement",
    headers={
        "X-API-Key": "YOUR_API_KEY",
        "Content-Type": "application/json",
    },
    json={"tweet_id": "1912345678901234567"},
)

data = response.json()["data"]
print(data["final_statement"])
print(data["detailed_analysis"])
print(data["claim_type"])

How to Read the Response

`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 (use it directly as a search/verification query)

`detailed_analysis`

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

`topics` and `entities`

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

Media summaries

tweet_media_summary and quoted_media_summary explain what the attached media contributed to the meaning, if media exists in either layer. For videos this is a transcript summary; for images it is a vision-model description (transcribed text, chart/graph data, recognizable people and logos, and any claims they make).

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.

Common Workflows

Fact-checking

Use final_statement as the normalized claim to verify — it doubles as a search query for reporting, source documents, or related video evidence.

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.

Narrative clustering

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

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.

Billing and Media Notes

Billing is consolidated — a single call may run several internal LLM calls (media extraction, image vision, and the main statement extraction) but produces just one analysis_request charge per request:

a text-only tweet usually consumes analysis_request units only
a tweet with attached media can also trigger standard_request or residential_request (per media URL fetched) and transcription_hour (if speech-to-text runs)
a quoted tweet with its own media can add another layer of processing
image analysis is billed through the same analysis_request meter (no separate meter)

Analysis Request billing is dynamic: 1 analysis_request unit covers up to 15,000 total tokens, and larger contexts use ceil(total_tokens / 15000) units. 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.

Troubleshooting

Invalid tweet ID

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

Missing media summaries

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

Unexpected classification

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

Upstream fetch issues

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

​What This Guide Helps You Do

​When to Use This Endpoint

​Why This Is Different from Basic Tweet Retrieval

​What the API Actually Returns

Final Claim

Detailed Analysis

Topics & Entities

Classification Axes

​How It Works

​Quickstart

​Basic Request

​How to Read the Response

​final_statement

​detailed_analysis

​topics and entities

​Media summaries

​Classification Axes

​Common Workflows

​Fact-checking

​Reputation and crisis monitoring

​Narrative clustering

​AI product integration

​Billing and Media Notes

​Troubleshooting

​Invalid tweet ID

​Missing media summaries

​Unexpected classification

​Upstream fetch issues

​Next Steps

What This Guide Helps You Do

When to Use This Endpoint

Why This Is Different from Basic Tweet Retrieval

What the API Actually Returns

How It Works

Quickstart

Basic Request

How to Read the Response

`final_statement`

`detailed_analysis`

`topics` and `entities`

Media summaries

Classification Axes

Common Workflows

Fact-checking

Reputation and crisis monitoring

Narrative clustering

AI product integration

Billing and Media Notes

Troubleshooting

Invalid tweet ID

Missing media summaries

Unexpected classification

Upstream fetch issues

Next Steps