arXiv (All)
open access
CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era
Social bot detection is essential for protecting online platforms from misinformation amplification, coordinated manipulation, and distorted public discourse. However, large language models have made social bots much harder to detect from text alone because semantic camouflage is now cheap, fluent,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- FISSION: Label Augmentation for Bot Detection#1062178
- Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection#799031
- Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection#797616
- Process Matters more than Output for Distinguishing Humans from Machines#1130545
- CoDeL: Co-Evolutionary Defense against Indirect Prompt Injection in LLM-based Agents#1128733
Record · ID 1049063
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive