In the age of user-generated content (UGC), platforms face an evolving challenge: moderating toxic content that hides behind slang, emojis, and cultural nuance. In 2025, this problem is amplified by the internet-native behavior of Gen Z—who regularly bend language and meaning in new, unpredictable ways.

Traditional keyword-based moderation is no longer enough. To protect online communities, businesses need smarter systems that adapt to coded speech, leetspeak, emojis, and multilingual slang. This blog explores how modern moderation engines like ModEngine are evolving to detect this new wave of toxicity.

Why Gen Z Content is Harder to Moderate

Gen Z, born into the internet era, communicates with:

• Emojis as language

• Irony, sarcasm, and memes

• Abbreviations and euphemisms

• Coded language to evade filters

Their linguistic style allows toxic intent to hide behind seemingly innocent words. For example:

• "I feel unalive" may reference suicidal ideation

• "He has major rizz" might seem harmless but can imply grooming

• "She dropped a thirst trap" often precedes sexually explicit content

Real Examples of Gen Z Slang & Toxic Use Cases

These are examples of conversational, intent-driven keywords that moderation systems must interpret contextually.

How Toxic Keyword Filtering Works in 2025

Modern moderation engines like ModEngine combine keyword lists, contextual AI, image analysis, structured metadata, and user behavior signals to create a multi-layered filter. Here’s how it works:

1. Basic Keyword Matching

• Scans for known toxic terms (e.g., "porn", "blowjob", "kill yourself")

• Uses regex for variations: \b(porn|p0rn|pr0n)\b

• Unicode normalization to catch stylized text: e.g., "nʎʀəs"

2. Gen Z Slang Detection

• Trained NLP models on TikTok, Reddit, Discord language

• Maps emerging slang (e.g., "rizz", "thirst trap") to risk profiles

• Retrains models regularly to adapt to evolving language

3. Emoji Pattern Recognition

• Detects emoji-only messages or emoji-text combos

• Flags NSFW (🍑🍆💦), violent (🔫🩸), drug-related (💊🧃) emoji sequences

• AI models assess meaning from emoji clusters and placement

4. Contextual Sentiment & Toxicity Analysis

• Identifies sarcasm, coded threats, or bullying tone

• Uses LLMs fine-tuned for moderation tasks

• Differentiates “he ate” as praise or insult based on context

5. Behavioral Flags & Triggers

• Detects anomalies like account spikes or repeated borderline content

• Correlates image-text metadata (e.g., a selfie captioned "DTF?")

6. Structured Schema & Media Optimization

• Applies structured data tags (JSON-LD schema) for flagged content

• Optimizes flagged images and alt text for visual search moderation

• Enables AI-overview-ready summaries in moderation reports

Real-Time Moderation Flow: ModEngine Example

1. User posts: "She just posted a thirst trap 😍🍑"

2. System tokenizes the message

3. Matches "thirst trap" + 🍑 as NSFW pattern

4. Contextual behavior: user has posted flirty comments repeatedly

5. Flag generated: sent for review or action based on policy thresholds

Download: 2025 Toxic Keyword & Gen Z Slang Filter List

To support developers and Trust & Safety teams, we've curated a list of 300+ terms:

• Gen Z slang and emojis

• Harassment and hate language

• Self-harm references

• Drug mentions

• NSFW triggers and intent-driven keyword patterns

Download the PDF Here

Best Practices for Content Moderation in 2025

• Regularly update keyword lists and NLP models with latest slang

• Tag all moderated content with structured schema markup

• Use conversational AI to analyze context, not just keywords

• Customize filters by community norms and regional languages

• Optimize all images and videos with moderation-friendly metadata

• Provide user transparency through flags and feedback loops

Closing Thoughts

In 2025, moderation isn’t just technical—it’s cultural, visual, and contextual. Gen Z’s fluid use of language requires platforms to go beyond legacy filters.

Protect your platform—and your users—with ModEngine.