Spam Detection

Detect spam comments in your discussions.

What it detects:

• Promotional/advertising content
• Bot-generated text patterns
• Link spam and SEO manipulation
• Repeated/duplicate comments
• Off-topic noise

Reference

See the Spam Detection API Reference for method signatures and parameters.

Response: SpamDetectionResult

By Example

Basic Usage

Python

Blocking Client

# With article context (more accurate)
result = client.check_spam(
    "Buy cheap watches at example.com!",
    article_id
)

# Without context (standalone check)
result = client.check_spam("Buy cheap watches at example.com!")

print(f"Is spam: {result.is_spam}")
print(f"Confidence: {result.confidence:.2f}")
print(f"Reasoning: {result.reasoning}")

Async Client

# With article context (more accurate)
result = await client.check_spam(
    "Buy cheap watches at example.com!",
    article_id
)

# Without context (standalone check)
result = await client.check_spam("Buy cheap watches at example.com!")

print(f"Is spam: {result.is_spam}")
print(f"Confidence: {result.confidence:.2f}")
print(f"Reasoning: {result.reasoning}")

PHP

$client->checkSpam("Buy cheap watches at example.com!", $articleId)
    ->then(function ($result) {
        echo "Is spam: " . ($result->isSpam ? 'Yes' : 'No') . "\n";
        echo "Confidence: " . number_format($result->confidence, 2) . "\n";
        echo "Reasoning: " . $result->reasoning . "\n";
    });

$client->run();

REST API

Try it out live →

# With article context (more accurate)
curl -X POST https://app.respectify.ai/v0.2/antispam \
  -H "X-User-Email: your@email.com" \
  -H "X-API-Key: your-api-key" \
  -d "comment=Buy cheap watches at example.com!" \
  -d "article_context_id=550e8400-e29b-41d4-a716-446655440000"

# Without context (standalone check)
curl -X POST https://app.respectify.ai/v0.2/antispam \
  -H "X-User-Email: your@email.com" \
  -H "X-API-Key: your-api-key" \
  -d "comment=Buy cheap watches at example.com!"

Response:

{
  "is_spam": true,
  "confidence": 0.95,
  "reasoning": "Detected repetitive phrasing and promotional language with external links."
}

Best Practices

• Use article context when available for better accuracy.
• Set confidence thresholds based on your needs:
  • High confidence (above 0.9): Consider auto-rejection or flag for review
  • Medium (0.7 to 0.9): Flag for human review
  • Low (below 0.7): Allow through
• Log false positives to understand patterns.
• Consider showing feedback even for spam, to help users understand why their comment was flagged.