LLM: Bias, Toxicity, and Misinformation Mesh

Inspiration

I’ve always been passionate about ethical AI and the ripple effect of harmful content online. With growing concerns around misinformation, bias, and toxic discourse on social platforms, I wanted to create something actionable — a system that could analyze tweet dynamics, classify ethical risks, and make it all observable in graph form. WeaveHacks’ focus on agentic protocols and traceable multi-agent reasoning inspired me to push toward a graph-native moderation pipeline with modular intelligence at its core.

What it does

LLM BiasMesh performs three key tasks: Classifies tweets for toxicity, bias, and misinformation using modular LLM agents. Maps those classifications into a Neo4j graph structure to show ethical relationships and retweet cascades. Enables traceable moderation by linking tweet content with agent decisions, making bias detection and content risk observable at scale. In short, it turns raw social data into a living network of ethical insights — helping developers, researchers, and moderators see harmful patterns as they emerge and evolve. Want a diagram or graph schema mockup to visualize it? I can whip up one fast.

How we built it

I built BiasMesh, a protocol-driven system of agents powered by CrewAI that performs tweet classification along three axes: Toxicity Misinformation Bias The data pipeline starts in BigQuery, where tweets are ingested, batched, and enriched via LLM agents. Each agent plays a unique role: analyzing statements for bias, misinformation, or toxicity. Their outputs are written to Neo4j, where I visualize cascades and their ethical profiles. Batching followed an offset function:

Challenges we ran into

Challenges I Faced Setting up the pipeline across Neo4j, BigQuery, and LLM agents took more time than expected. Aligning credentials, schema compatibility, data access policies, and driver configurations was a nontrivial hurdle — especially under hackathon pressure. Other key challenges included: ⚡️ Connectivity woes: My Neo4j Aura instance was paused mid-hack, which required troubleshooting, reactivation, and graph reconstruction before any classification could begin. 🧪 Label consistency: Tweets often blurred the line between misinformation and bias, pushing me to reengineer prompts and add validation logic for borderline outputs. Batch tuning: Running smaller batches (e.g. 20 or 100 tweets) revealed more nuanced LLM behavior, but slowed throughput. I had to fine-tune offsets to skip already-labeled entries. Query design: Structuring Cypher to exclude previously classified tweets avoided duplication but added complexity to my update logic. LLM ambiguity: Despite robust prompts, some outputs were too vague or contextually ambiguous. I built safeguards to catch Unknown cases and log them for human review. OpenAI key management: Midway through testing, I hit a RateLimitError due to insufficient quota, despite having added funds. The quota didn’t refresh until I regenerated a new API key — a frustrating twist that cost precious time and required rewriting parts of the client initialization logic. Each challenge became an opportunity to deep

Accomplishments that we're proud of

What we learned

I explored: CrewAI’s agent-task orchestration in sequential mode Prompt tuning for consistent classification across nuanced content Neo4j schema design to represent tweet interactions and enrichment Observability layers in moderation pipelines Precision tracking to measure LLM agent performance I also learned how fragile label interpretation can be without tight output constraints — so I built fail-safes for ambiguous responses.

What's next for LLM: Bias, Toxicity, and Misinformation Mesh

🧠 Fine-Tune Agents with Human-In-The-Loop Feedback Integrate human reviewers for edge cases and unknown classifications, enabling agent retraining through zero-shot validation and LLM score aggregation. 🔗 Expand Graph Schema Add node types for: User profiles with behavioral flags External citations for misinformation traceability Ethical escalation pathways and issue categorization Bring BiasMesh to Mobile Port the pipeline to a lightweight Core ML implementation, making real-time moderation possible for mobile platforms and localized reporting apps. Streamlit Visual Dashboard Build a GUI overlay for interactive moderation views — letting users explore toxicity clusters, retweet influence zones, and bias propagation trees. Community Collaboration Open-source the protocol and organize a Weave-style mini-hackathon to test new agent strategies and cross-platform extensions — bridging into Reddit, YouTube, or news APIs. Real-Time Stream Integration Adapt the ingestion to support Twitter/X firehose streams or batch fetches via push-scheduler — moving toward near-live detection and alerting.

Built With

• .env
• 3
• and-ingestion-logic-neo4j-auradb-for-graph-based-tweet-visualization-and-ethical-label-embedding-apis-&-models-openai-gpt-4o-for-bias
• anthropic
• auradb
• batch-filtering
• bigquery
• claude
• cloud
• credential
• crewai
• cypher
• fallback)
• for
• google
• gpt-4o
• neo4j
• openai
• optional
• platform
• python
• python-dotenv
• sdk
• toxicity
• v1.x
• v3.13)