Content Moderation

Content moderation is the NLP task of automatically flagging text that may be harmful, offensive or sensitive. A moderation model reads a comment, review or post and returns scores for categories such as toxicity, insults or violence, so a person can decide what to hide, report or reply to. It helps brands keep user-generated content in line with their standards without reading every word by hand.

How it works

Moderation is a multi-label classification problem: one piece of text can score highly on several categories at once. The Google Cloud Natural Language API has a moderateText method that checks text against 16 safety attributes, including Toxic, Derogatory, Violent, Sexual, Insult and Profanity, plus sensitive topics such as Health, Finance, Politics and Legal. Each attribute comes back with a confidence score between 0 and 1. You decide the thresholds that trigger an action.

Large language models can also moderate text when given a clear policy in the prompt, which is useful for brand-specific rules the fixed categories do not cover.

How it’s used in SEO and marketing

• Comments and forums. Screen user comments on your site before or after they go live.
• Brand mentions. Score social posts and articles that mention your brand to find the ones needing a response.
• Reviews. MLforSEO’s customer review project flags reviews to report, using the direct report link that a Google Business Profile scrape provides.
• Scraped inputs. Check third-party text before it feeds programmatic pages or AI-generated summaries.

Example

MLforSEO’s Google Sheets template wraps the API in Apps Script. You paste your API key into the script, paste content into the working sheet, and run the function. Each row gets a score per category. Sorting by Toxic and Insult puts the comments worth checking at the top, while a high Finance or Health score on a bank or clinic review simply confirms the topic.

Limitations and things to watch

• The topic categories describe subject matter rather than harm, so read them differently from the harmful ones.
• Sarcasm, reclaimed language, dialect and quoted text all cause false positives and misses.
• Scores are probabilities. Keep a human in the loop for anything that removes content or affects a customer.
• Check language support before running non-English content, and think about privacy before sending user content to a third-party API.

Learn it on MLforSEO

• How to do content moderation with Google’s Natural Language API in Google Sheets
• Google Sheets Content Moderation Template
• Semantic Analysis of Customer Reviews with Machine Learning
• Introduction to Machine Learning for SEO course