Extract structured claim from an X/Twitter tweet

curl --request POST \
  --url https://api.vidnavigator.com/v1/tweet/statement \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "tweet_id": "1234567890123456789"
}
'

import requests

url = "https://api.vidnavigator.com/v1/tweet/statement"

payload = { "tweet_id": "1234567890123456789" }
headers = {
    "X-API-Key": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'X-API-Key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({tweet_id: '1234567890123456789'})
};

fetch('https://api.vidnavigator.com/v1/tweet/statement', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.vidnavigator.com/v1/tweet/statement",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'tweet_id' => '1234567890123456789'
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "X-API-Key: <api-key>"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.vidnavigator.com/v1/tweet/statement"

	payload := strings.NewReader("{\n  \"tweet_id\": \"1234567890123456789\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("X-API-Key", "<api-key>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.vidnavigator.com/v1/tweet/statement")
  .header("X-API-Key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"tweet_id\": \"1234567890123456789\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.vidnavigator.com/v1/tweet/statement")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["X-API-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"tweet_id\": \"1234567890123456789\"\n}"

response = http.request(request)
puts response.read_body

{
  "status": "success",
  "data": {
    "final_statement": "<string>",
    "detailed_analysis": "<string>",
    "topics": [
      "<string>"
    ],
    "entities": [
      "<string>"
    ],
    "tweet_text": "<string>",
    "tweet_media_summary": "<string>",
    "quoted_tweet_text": "<string>",
    "quoted_media_summary": "<string>"
  }
}

{
  "status": "error",
  "error": "<string>",
  "message": "<string>"
}

{
  "status": "error",
  "error": "internal_server_error",
  "message": "<string>"
}

{
  "status": "error",
  "error": "<string>",
  "message": "<string>"
}

Online media

Tweet Claim Analysis

Fetches the tweet content (including any quoted tweet and attached media), then uses AI to extract a rich structured analysis: the core claim, classification axes, topics, entities, and raw tweet data.

Multimodal media handling: attached media is processed by type. Videos are transcribed (platform captions or speech-to-text) and summarized; images are described by a vision model — transcribed text, chart/graph data points, recognisable people and logos, and any claims they make — and folded into the analysis.

Classification axes (claim_type, intent, tone, emotion, authority) reflect only the original tweet text, not quoted content, media, or images.

Billing (consolidated, mirrors /v1/analyze/video):

exactly one analysis_request charge with quantity = ceil(total_tokens / 15000), summing tokens across the optional per-media extraction calls, the optional image vision calls, AND the main statement-extraction call. A typical tweet+quoted tweet with one video or an attached image runs several LLM calls but produces just one billing entry when the combined tokens stay under 15k. Image analysis is billed through this same analysis_request meter.
one standard_request per media URL whose transcript or metadata is fetched (mirrors /v1/transcript).
transcription_hour proportional to the audio duration if speech-to-text runs for a media video that has no platform transcript. Waived for users with users.free_on_cache_hit=true when STT was served from cache (same waiver as /v1/transcribe).

POST

statement

Extract structured claim from an X/Twitter tweet

curl --request POST \
  --url https://api.vidnavigator.com/v1/tweet/statement \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "tweet_id": "1234567890123456789"
}
'

import requests

url = "https://api.vidnavigator.com/v1/tweet/statement"

payload = { "tweet_id": "1234567890123456789" }
headers = {
    "X-API-Key": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'X-API-Key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({tweet_id: '1234567890123456789'})
};

fetch('https://api.vidnavigator.com/v1/tweet/statement', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.vidnavigator.com/v1/tweet/statement",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'tweet_id' => '1234567890123456789'
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "X-API-Key: <api-key>"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.vidnavigator.com/v1/tweet/statement"

	payload := strings.NewReader("{\n  \"tweet_id\": \"1234567890123456789\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("X-API-Key", "<api-key>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.vidnavigator.com/v1/tweet/statement")
  .header("X-API-Key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"tweet_id\": \"1234567890123456789\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.vidnavigator.com/v1/tweet/statement")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["X-API-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"tweet_id\": \"1234567890123456789\"\n}"

response = http.request(request)
puts response.read_body

{
  "status": "success",
  "data": {
    "final_statement": "<string>",
    "detailed_analysis": "<string>",
    "topics": [
      "<string>"
    ],
    "entities": [
      "<string>"
    ],
    "tweet_text": "<string>",
    "tweet_media_summary": "<string>",
    "quoted_tweet_text": "<string>",
    "quoted_media_summary": "<string>"
  }
}

{
  "status": "error",
  "error": "<string>",
  "message": "<string>"
}

{
  "status": "error",
  "error": "internal_server_error",
  "message": "<string>"
}

{
  "status": "error",
  "error": "<string>",
  "message": "<string>"
}

Extract the real meaning behind an X/Twitter post by analyzing the original tweet, any quoted tweet, and media shared in both contexts.

Overview

This endpoint turns a single X post into a machine-readable intelligence object. It does not just retrieve tweet text or metadata. It analyzes what the author actually meant by combining the original tweet, any quoted tweet, and any media attached to either one. That means the output reflects the full communicative intent of the post, not just the visible text. If a tweet makes sense only when read together with a quoted post, an attached video, or media inside the quoted tweet, this endpoint includes that context in the analysis. It is designed for workflows that need more than keyword monitoring or tweet retrieval, such as fact-checking pipelines, narrative tracking, social risk monitoring, and structured ingestion into dashboards or AI systems.

This is not a basic tweet retrieval API. Its core value is intelligence extraction: understanding the author’s intent, the role of the quoted tweet, and the meaning conveyed by media attached to both the original and quoted post.

What This Endpoint Returns

Intent-Aware Claim Extraction

Extract a multi-sentence final_statement that reflects what the author actually meant, not just what the raw tweet text says in isolation.

Quoted Tweet Understanding

Incorporate quoted tweet context when the original post depends on it for meaning, stance, or narrative framing.

Multi-Axis Classification

Classify the original tweet by claim_type, intent, tone, emotion, and authority.

Multimodal Media Intelligence

Analyze media attached to the tweet and the quoted tweet. Videos are transcribed (platform captions or speech-to-text) and summarized; images are described by a vision model — transcribed text, chart/graph data points, recognizable people and logos, and any claims they make.

Why This Endpoint Is Different

Most tweet APIs stop at text retrieval, engagement metrics, or shallow metadata. This endpoint is built for intelligence extraction.

It understands the intent behind the original tweet.
It understands the quoted tweet as part of the message, not as unrelated metadata.
It analyzes shared media in the original tweet — both videos (transcribed) and images (described by a vision model).
It analyzes shared media in the quoted tweet.
It returns a structured output that reflects the combined meaning of all of that context.

This is especially important for X posts where the author’s real point is only clear when you interpret the text together with a quoted post, a reaction clip, a narrated video, or media evidence embedded in either layer.

How It Works

Send a tweet_id for the target X post.
VidNavigator fetches the original tweet, any quoted tweet, and media attached to both.
If video or audio is present in either context, the system retrieves transcripts or falls back to speech-to-text when needed. Attached images are described by a vision model.
The combined context is analyzed to determine the real claim, intent, framing, and supporting media meaning.
The result is returned as structured JSON ready for applications and pipelines.

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

Classification Axes

The classification labels below are derived from the original tweet text only, not from quoted tweets or attached media:

claim_type: factual_claim, opinion, question, call_to_action, satire, news_sharing, personal_experience
intent: educate, inform, analyze, persuade, entertain, inspire, challenge
tone: serious, humorous, provocative, neutral, warm, skeptical, inspirational
emotion: curiosity, urgency, outrage, fear, hope_inspiration, confidence_reassurance, empathy_warmth, awe_wonder
authority: data_driven, expert_led, experience_based, speculative

Example Usage

Best For

Use this endpoint when you need to understand what a post is really saying, especially when meaning depends on:

the author’s underlying intent
a quoted tweet that changes the framing
attached video or audio in the original tweet
attached video or audio in the quoted tweet

If you only need raw tweet metadata, this is more powerful than necessary. This endpoint is for interpretation and intelligence extraction.

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

url = "https://api.vidnavigator.com/v1/tweet/statement"
headers = {
    "X-API-Key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}
data = {
    "tweet_id": "1912345678901234567"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print("Final statement:", result["data"]["final_statement"])
print("Claim type:", result["data"]["claim_type"])
print("Topics:", result["data"]["topics"])

const response = await fetch('https://api.vidnavigator.com/v1/tweet/statement', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    tweet_id: '1912345678901234567'
  })
});

const result = await response.json();
console.log(result.data.final_statement);

Example: Feeding Results into Search or Verification

When you want to take a tweet’s interpreted meaning and use it for search, fact-checking, or evidence retrieval, the structured output is ready to plug into downstream pipelines:

cURL

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"
  }'

Once returned, you can use:

final_statement for human review or as a search/verification query
detailed_analysis for framing and stance context
topics and entities for clustering, tagging, or analytics

Response Example

{
  "status": "success",
  "data": {
    "final_statement": "The author claims that a new city policy will ban gas-powered delivery scooters in the downtown area next month, arguing that the change is being rushed without enough support for affected workers.",
    "detailed_analysis": "The tweet presents a concrete policy claim and frames it as both imminent and harmful to delivery workers. The author positions the decision as poorly planned and emphasizes the lack of transition support. The message appears intended to persuade the audience that the policy is unfair rather than merely to report it. The framing combines factual assertion with rhetorical pressure.",
    "topics": [
      "urban policy",
      "delivery workers",
      "transport regulation",
      "labor impact"
    ],
    "entities": [
      "City Council",
      "downtown"
    ],
    "claim_type": "factual_claim",
    "intent": "persuade",
    "tone": "provocative",
    "emotion": "urgency",
    "authority": "speculative",
    "tweet_text": "Next month the city is banning gas scooters downtown and nobody has explained how delivery workers are supposed to adapt this fast.",
    "tweet_media_summary": "A short attached video shows several delivery riders speaking about costs, route changes, and reduced earnings if the rule takes effect.",
    "quoted_tweet_text": "Official announcement: downtown clean transport rules begin next month.",
    "quoted_media_summary": null
  }
}

Field Guide

Field	Description
`final_statement`	Full normalized claim in the author’s voice, informed by the full contextual analysis
`detailed_analysis`	3-5 sentence explanation covering claim, framing, evidence, and stance
`topics`	Key subjects discussed in the tweet (3-8)
`entities`	Named entities such as people, organizations, or places (0-8)
`claim_type` / `intent` / `tone` / `emotion` / `authority`	Classification axes derived from the original tweet text only
`tweet_text`	Original text of the source tweet
`tweet_media_summary`	Full content summary of media attached to the original tweet: a video transcript summary or a vision-model description of attached image(s). `null` when no media is attached.
`quoted_tweet_text`	Text of a quoted or referenced tweet, when present
`quoted_media_summary`	Full content summary of media attached to the quoted tweet (video transcript or image description). `null` when no media is attached.

Intelligence Model

This endpoint should be thought of as a multi-layer interpretation pipeline:

Read the original tweet text.
Detect whether a quoted tweet changes or completes the meaning.
Analyze media attached to the original tweet.
Analyze media attached to the quoted tweet.
Produce a final claim and classification that reflect the author’s intended message as completely as possible.

That is why the endpoint is effective for tweets where the visible text alone is incomplete, sarcastic, reactive, or dependent on embedded media.

Billing Notes

Billing is consolidated and mirrors /analyze/video. A single call can involve several internal LLM calls (media extraction, image vision, and the main statement extraction) but produces one billing entry per meter:

Exactly one analysis_request charge with quantity = ceil(total_tokens / 15000), summing tokens across the optional per-media extraction calls, the optional image vision calls, and the main statement-extraction call. A typical tweet + quoted tweet with one video or one attached image runs several LLM calls but produces just one billing entry when the combined tokens stay under 15k. Image analysis is billed through this same analysis_request meter.
One standard_request per media URL whose transcript or metadata is fetched (mirrors /transcript).
transcription_hour proportional to the audio duration if speech-to-text runs for a media video with no platform transcript. Waived for users with free_on_cache_hit=true when STT was served from cache.

For cost planning, a text-only tweet is typically simpler than a tweet with embedded media and a quoted post that also contains video or images.

Use Cases

Fact-Checking

Convert noisy social posts into verification-ready claims that analysts can review quickly.

Narrative Tracking

Group similar claims using final_statement, topics, and entities across many tweets.

Reputation Monitoring

Detect accusations, emotionally charged narratives, and emerging risk signals earlier.

AI Product Integration

Feed structured social intelligence into internal tools, LLM workflows, or search systems.

Troubleshooting

Invalid tweet ID: Make sure you are sending only the numeric post ID, not the full X URL.
Upstream fetch failure: If X content cannot be retrieved or parsed, the endpoint may return a 502 response.
Missing media summary: If no media is attached, or media could not be processed, tweet_media_summary and quoted_media_summary may be null.
Unexpected classification: claim_type, intent, tone, emotion, and authority are based only on the original tweet text, while final_statement and media summaries reflect the broader contextual analysis.

Authorizations

X-API-Key

string

header

required

API key authentication. Include your VidNavigator API key in the X-API-Key header.

Body

application/json

tweet_id

string

required

The X/Twitter tweet ID (numeric string)

Example:

"1234567890123456789"

Response

Successful extraction

status

enum<string>

Available options:

success

data

object

Show child attributes

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​Overview

​What This Endpoint Returns

Intent-Aware Claim Extraction

Quoted Tweet Understanding

Multi-Axis Classification

Multimodal Media Intelligence

​Why This Endpoint Is Different

​How It Works

​Classification Axes

​Example Usage

​Best For

​Basic Request

​Example: Feeding Results into Search or Verification

​Response Example

​Field Guide

​Intelligence Model

​Billing Notes

​Use Cases

Fact-Checking

Narrative Tracking

Reputation Monitoring

AI Product Integration

​Troubleshooting

Authorizations

Body

Response

Overview

What This Endpoint Returns

Why This Endpoint Is Different

How It Works

Classification Axes

Example Usage

Best For

Basic Request

Example: Feeding Results into Search or Verification

Response Example

Field Guide

Intelligence Model

Billing Notes

Use Cases

Troubleshooting