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educational January 16, 2026

How to Use Mallary.ai Webhooks to Build Real-Time Social Monitoring

Introduction

Real-time social monitoring is essential for brands that want to respond quickly to customer feedback, protect reputation, and spot opportunities the moment they happen. Webhooks are the backbone of any high-performance monitoring system because they push events the instant they occur instead of relying on slow polling. In this post we'll walk through how to use Mallary.ai webhooks to build a robust, scalable real-time social monitoring pipeline — from basic setup to production-grade best practices.

Why use webhooks for real-time social monitoring?

Webhooks deliver immediate notifications when a social mention, comment, or trend is detected. Compared to periodic polling they offer several advantages:

  • Instant delivery: Events are pushed immediately, reducing time-to-insight and enabling faster responses.
  • Lower resource use: No constant API polling; you only process events when something happens.
  • Scalability: Easier to scale downstream processing independently from the source.
  • Flexibility: Route events to different systems — dashboards, Slack, CRMs, or serverless functions.

When combined with Mallary.ai’s powerful social listening and enrichment capabilities, webhooks make it possible to act on mentions, sentiment changes, and emerging trends in real time.

Overview: How Mallary.ai Webhooks Work

Mallary.ai webhooks send JSON payloads to a URL you provide whenever a subscribed event occurs. Typical event types include new mentions, sentiment shifts, influencer activity, or alert thresholds being crossed.

Typical webhook flow

  1. User configures webhook endpoint in the Mallary.ai dashboard or API.
  2. Mallary.ai detects a relevant social event based on your queries and filters.
  3. The platform sends a signed POST request to your endpoint with a JSON payload.
  4. Your server verifies the signature, processes the event, and returns a 200 OK.
  5. Your downstream logic (alerts, enrichment, storage) handles the event asynchronously.

Setting up your first Mallary.ai webhook

Follow these steps to get started quickly:

  1. Create a Mallary.ai account and configure your monitoring queries (keywords, brands, hashtags).
  2. In the dashboard, navigate to Webhooks and add a new endpoint URL (your publicly accessible HTTPS endpoint).
  3. Select which event types to subscribe to (e.g., mention.created, mention.updated, alert.triggered).
  4. Save the webhook and copy the shared secret for signature verification.
  5. Implement a handler on your server that accepts POST requests and validates the signature.

Example JSON payload

{
  "event": "mention.created",
  "id": "evt_12345",
  "timestamp": "2026-01-15T12:34:56Z",
  "data": {
    "platform": "twitter",
    "author": "@example",
    "text": "I love the new product!",
    "sentiment": "positive",
    "engagement": { "retweets": 5, "likes": 20 },
    "url": "https://twitter.com/example/status/12345"
  }
}

Best practices for webhook endpoints

To build reliable real-time social monitoring you should treat your webhook receiver as a critical production service. Here are key best practices:

1. Validate signatures and authenticate requests

Always verify the signature Mallary.ai provides using your shared secret. This prevents spoofing and ensures the payload is legitimate.

2. Respond quickly

Return a 200 OK as quickly as possible. Offload heavy processing to a job queue or serverless function so the webhook response is not delayed.

3. Handle retries and idempotency

Webhooks can be retried. Ensure your processing is idempotent by checking event IDs or storing processed event IDs for deduplication.

4. Rate limiting and backpressure

Prepare for bursts of events (e.g., when a post goes viral). Use buffering, queuing, and auto-scaling to avoid losing events.

5. Monitor and alert on failures

Track failed deliveries and set alerts so you can quickly resolve misconfigurations or downtime.

Tip: Use a small, fast endpoint that acknowledges webhooks immediately and queues work for asynchronous processing. This minimizes lost events and keeps retries predictable.

Designing your real-time monitoring pipeline

A robust pipeline has multiple stages: ingestion, enrichment, storage, routing, and action. Here's a practical architecture using Mallary.ai webhooks:

Pipeline stages

  • Ingestion: Mallary.ai posts events to your webhook endpoint.
  • Queueing: Push events into a durable queue (e.g., Redis Streams, SQS, Pub/Sub) for reliable processing.
  • Enrichment: Add context such as company CRM data, location lookup, or additional NLP (entity extraction, intent).
  • Storage: Write enriched events to a time-series or search store (Elasticsearch, BigQuery) for analytics and historical queries.
  • Routing & Actions: Trigger alerts (Slack, email), create tickets in helpdesk systems, or kick off marketing workflows.

Integration examples

Common ways to use Mallary.ai webhooks in real-time workflows:

  • Send a Slack alert when a mention with negative sentiment exceeds a threshold.
  • Auto-create a support ticket in your CRM when a mention contains words like "refund" or "broken."
  • Notify product teams about trending feature requests via a dashboard.
  • Trigger influencer outreach when high-authority accounts mention your brand.

Security, scaling, and reliability considerations

Operational excellence is vital for social monitoring. Here’s how to make your webhook-based system production-ready.

Security

  • Use HTTPS for all webhook endpoints.
  • Verify payload signatures using the Mallary.ai secret.
  • Rotate secrets periodically and support multiple active secrets during rotation.
  • Limit access with IP allowlists if applicable.

Scaling

  • Design consumers to be stateless and horizontally scalable.
  • Use managed queues and serverless workers to scale with traffic spikes.
  • Partition processing by region or topic to balance load.

Reliability

  • Implement retries with exponential backoff for transient failures.
  • Track delivery and processing metrics (latency, errors, retry count).
  • Maintain an audit log of all inbound events for debugging and compliance.

Common pitfalls and how to avoid them

Many teams face similar challenges when moving from prototype to production. Here are pitfalls and mitigations:

  • Pitfall: Synchronous heavy processing in the webhook handler. Fix: Use quick-acknowledge + async processing.
  • Pitfall: Not validating signatures. Fix: Enforce signature checks in middleware.
  • Pitfall: Missing deduplication leading to duplicate actions. Fix: Persist event IDs and make handlers idempotent.
  • Pitfall: No alerting on delivery failures. Fix: Instrument and monitor webhook delivery metrics.

Real-world use cases

Here are concrete scenarios where Mallary.ai webhooks power immediate business value:

  • Reputation management: Catch a brewing PR issue and respond before it escalates.
  • Customer support: Turn social complaints into support tickets instantly.
  • Sales & lead gen: Identify product interest signals and route leads to sales reps.
  • Product feedback: Aggregate and surface feature requests to product teams in near real time.

Getting started with Mallary.ai webhooks

To implement these concepts quickly, we recommend starting small and iterating:

  1. Configure a webhook in the Mallary.ai dashboard for a single high-value keyword or brand mention.
  2. Build a simple endpoint that validates signatures, queues events, and logs for visibility.
  3. Add a Slack notification for negative sentiment mentions to validate workflow and responsiveness.
  4. Expand subscriptions and add enrichment and storage as your confidence grows.

Mallary.ai’s flexible webhook model and rich social intelligence make it easy to scale from a simple alerting system to a full-featured real-time monitoring platform.

Conclusion

Webhooks are a powerful, efficient way to build real-time social monitoring systems. By using Mallary.ai webhooks you can receive instant notifications about mentions, sentiment shifts, and emerging trends — and then enrich, store, and route those events to the teams and systems that need them. Focus on secure, fast webhook handling, reliable queuing, idempotent processing, and scalable enrichment to turn social signals into actions.

Ready to get real-time with your social monitoring? Sign up for free today and start building webhook-driven workflows that react the moment the conversation happens.