Google Sheets Content Moderation Template with Google Cloud Natural Language API for Toxic Content Detection

Manually reviewing thousands of user comments, social media mentions, or community posts for inappropriate content is time-intensive and exposes moderators to harmful material—this automated Google Sheets template integrates Google Cloud Natural Language API’s content moderation capabilities to scan text at scale across 16 safety categories (Toxicity, Severe Toxicity, Insult, Profanity, Threat, Identity Attack, Sexual, Violence, and others) and returns individual confidence scores (0-100% scale) indicating the likelihood each category applies to the analyzed content, with conditional formatting automatically highlighting high-risk entries in red and safe content in green. Created by Lazarina Stoy for MLforSEO, this no-code solution enables community managers, content moderators, and digital marketers to process user-generated content, social media mentions, website comments, or forum posts through Google’s machine learning content safety models without programming expertise—requiring only API key setup and a simple formula to transform qualitative text into quantitative safety assessments with visual color-coding that enables instant identification of flagged content requiring human review or automated filtering.
The template implements a multi-column assessment framework with Google Apps Script automation. Users paste text content into the designated column, configure their Google Cloud Natural Language API key in the Apps Script editor (accessed via Extensions > Appscript menu), and execute the content moderation formula to trigger batch analysis. The API processes each text entry and returns confidence percentages across all 16 moderation categories simultaneously—for example, a toxic comment might show 85% Toxicity, 72% Insult, 45% Profanity, while benign content displays 0.5% across all categories. The template includes sophisticated conditional formatting rules that automatically color-code cells: green shading indicates safe content with low confidence scores (typically under 30%), while red highlighting flags potentially problematic content with high confidence scores (typically 50%+)—enabling moderators to instantly scan hundreds of entries and focus attention on red-flagged items requiring immediate action. The visual heat map effect created by the color gradient makes pattern recognition effortless, revealing clusters of toxic content, specific problematic users, or time periods with elevated moderation issues.
Use this for:
‧ Social media mention monitoring by analyzing brand mentions, comment threads, or tagged posts to identify toxic engagement requiring response or account blocking
‧ Website comment moderation for blog posts, product reviews, or community forums by automatically flagging inappropriate submissions before they appear publicly
‧ Community code of conduct enforcement by processing forum interactions, Discord exports, Reddit threads, or Slack messages against defined safety standards
‧ Content creator protection by screening YouTube comments, Instagram replies, or TikTok interactions to filter harassment before it reaches creators
‧ User-generated content pre-screening for marketplaces, classified ad sites, or social platforms to prevent policy-violating content publication
‧ Crisis detection and response by monitoring sudden spikes in toxic content across multiple categories indicating coordinated harassment campaigns or community issues
‧ Moderation team efficiency by color-coded visual prioritization allowing human moderators to focus on high-confidence flags rather than reviewing every submission
‧ Compliance documentation providing timestamped, categorized safety assessments for platform safety audits, legal requirements, or trust and safety reporting
‧ Sentiment and safety correlation by combining this template with sentiment analysis to understand whether negative sentiment correlates with safety violations
‧ Competitor community health analysis by scraping and analyzing competitor comment sections, reviews, or forums to benchmark moderation quality or identify reputation risks
This is perfect for community managers, social media moderators, digital marketers managing user-generated content, and platform safety teams who need scalable, consistent content moderation without exposing human moderators to harmful content review—particularly valuable when processing large comment datasets (100+ entries), when establishing objective moderation thresholds across 16 distinct safety categories rather than subjective human judgment, when creating audit trails of moderation decisions with confidence scores, when identifying which specific safety categories are most prevalent in community violations, or when visually demonstrating content safety improvements to stakeholders through before/after heat map comparisons showing reduction in red-flagged entries.