Social Media Monitoring Agent

🤖 Explain with AI

The Social Media Monitoring Agent is a vigilant brand intelligence tool designed to oversee digital conversations 24/7. It automates the tracking of brand mentions, analyzes public sentiment, and identifies emerging trends, ensuring marketing teams can respond proactively to both opportunities and potential PR crises without monitoring streams manually.

Capabilities & Features¶

The Social Media Monitoring Agent is optimized for the following monitoring tasks:

• Real-Time Mention Tracking: Continuously scans social platforms (e.g., X/Twitter, LinkedIn, Reddit) for direct tags, keywords, or hashtags associated with the brand.
• Sentiment Analysis: Evaluates the tone of user comments (Positive, Neutral, Negative) to gauge overall brand health.
• Crisis Alerting: Detects spikes in negative sentiment or specific “risk” keywords and immediately triggers notifications to the support or PR team.
• Trend Identification: Aggregates recurring themes in customer feedback to highlight product requests or common complaints.

Integration Guide¶

Unlike standard API-integrated tools, the Social Media Monitoring Agent is deployed and managed directly through 4Geeks’ support and engineering teams.

1. Request Access: Contact your assigned 4Geeks account manager to request the Social Media Monitoring Agent.
2. Configuration: A 4Geeks staff member will work with you to define your tracked keywords, negative sentiment thresholds, and alert recipients.
3. Deployment: The agent is connected to your social listening streams or data pipelines by 4Geeks technicians to begin active monitoring.

Workflow Scenarios & Token Usage¶

The following scenarios illustrate how the agent interacts with users and estimates the associated costs based on the 4Geeks token model.

Scenario 1: Negative Sentiment Alert (Crisis Prevention)¶

The agent detects a sudden influx of negative comments regarding a specific service outage. It filters the noise and sends a concise alert summary to the Community Manager.

sequenceDiagram
  autonumber
  actor User as Social Stream
  participant Agent as Monitoring Agent
  participant Slack as Team Alert Channel

  User->>Agent: Influx of mentions (Data Stream)
  Note right of User: Input processed: ~400 words

  Agent->>Agent: Analyze Sentiment (Negative)
  Agent->>Agent: Identify Root Cause (Service Down)

  Agent-->>Slack: High Priority Alert + Summary
  Note left of Agent: Output: ~100 words

Cost Estimation¶

• Input Data: A batch of ~20 user comments/tweets processed for context (~400 words).
• Output Data:
  • Alert Header (Severity Level)
  • Summary of Issue
  • Sample Quotes
  • Output Total: ~100 words.
• Total Volume: ~500 words.
• Token Calculation: 500 words ÷ 0.75 words/token = ~666 tokens.
• Estimated Cost: ~650 - 700 Tokens

Scenario 2: Weekly Brand Health Report¶

A Marketing Director requests a summary of the week’s engagement on a new product launch campaign to understand how the audience is receiving it.

sequenceDiagram
  autonumber
  actor Director as Marketing Director
  participant Agent as Monitoring Agent
  participant DB as Sentiment Database

  Director->>Agent: Request "Campaign X" Weekly Report
  Note right of Director: Input: ~50 words

  Agent->>DB: Retrieve Week's High-Engagement Posts
  Agent->>Agent: Summarize Themes & Sentiment

  Agent-->>Director: Returns Structured Report
  Note left of Agent: Output: ~600 words

Cost Estimation¶

• Input Data: User Prompt + Contextual Data retrieved (Top 10 comments/posts analyzed) (~300 words).
• Output Data:
  • Executive Summary
  • Sentiment Breakdown (Percentage)
  • Top 3 Positive/Negative Themes
  • Output Total: ~600 words.
• Total Volume: ~900 words.
• Token Calculation: 900 words ÷ 0.75 words/token = ~1,200 tokens.
• Estimated Cost: ~1,150 - 1,250 Tokens

• Login or signup into Console.
• Check out the changelog.
• Contact our enterprise support team.