# TRACE-Bot: Detecting Emerging LLM-Driven Social Bots via Implicit Semantic Representations and AIGC-Enhanced Behavioral Patterns

arXiv: https://arxiv.org/abs/2604.02147 · HTML: https://mlsanity.com/paper/2604.02147/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark](https://mlsanity.com/paper/2106.13088/) (2021)
- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks](https://mlsanity.com/paper/2106.13092/) (2021)
- [Detection of Novel Social Bots by Ensembles of Specialized Classifiers](https://mlsanity.com/paper/2006.06867/) (2020)
- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [Machine Learning Security in Industry: A Quantitative Survey](https://mlsanity.com/paper/2207.05164/) (2022)
- [BotSim: LLM-Powered Malicious Social Botnet Simulation](https://mlsanity.com/paper/2412.13420/) (2024)
- [BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts](https://mlsanity.com/paper/2304.06280/) (2023)
- [Unsupervised Social Bot Detection via Structural Information Theory](https://mlsanity.com/paper/2404.13595/) (2024)
- [LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection](https://mlsanity.com/paper/2306.17408/) (2023)
- [SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection](https://mlsanity.com/paper/2405.11225/) (2024)
- [FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection](https://mlsanity.com/paper/2303.07113/) (2023)
- [BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers](https://mlsanity.com/paper/2404.15070/) (2024)
- [Enhancing LLM-Based Social Bot via an Adversarial Learning Framework](https://mlsanity.com/paper/2508.17711/) (2025)
- [CACL: Community-Aware Heterogeneous Graph Contrastive Learning for Social Media Bot Detection](https://mlsanity.com/paper/2405.10558/) (2024)
- [LGB: Language Model and Graph Neural Network-Driven Social Bot Detection](https://mlsanity.com/paper/2406.08762/) (2024)
- [Confidence and Stability of Global and Pairwise Scores in NLP Evaluation](https://mlsanity.com/paper/2507.01633/) (2025)
- [Efficient Function Orchestration for Large Language Models](https://mlsanity.com/paper/2504.14872/) (2025)

## Built on by (most cited first, 2 of 2)

- [SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection](https://mlsanity.com/paper/2605.30166/) (2026)
- [CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era](https://mlsanity.com/paper/2609.23320/) (2026)

## Abstract (arXiv)

Large Language Model-driven (LLM-driven) social bots pose a growing threat to online discourse by generating human-like content that evades conventional detection. Existing methods suffer from limited detection accuracy due to overreliance on single-modality signals, insufficient sensitivity to the specific generative patterns of Artificial Intelligence-Generated Content (AIGC), and a failure to adequately model the interplay between linguistic patterns and behavioral dynamics. To address these limitations, we propose TRACE-Bot, a unified dual-channel framework that jointly models implicit semantic representations and AIGC-enhanced behavioral patterns. TRACE-Bot constructs fine-grained representations from heterogeneous sources, including personal information data, interaction behavior data and tweet data. A dual-channel architecture captures linguistic representations via a pretrained language model and behavioral irregularities via multidimensional activity features augmented with signals from state-of-the-art (SOTA) AIGC detectors. The fused representations are then classified through a lightweight prediction head. Experiments on two public LLM-driven social bot datasets demonstrate SOTA performance, achieving accuracies of 98.46% and 97.50%, respectively. The results further indicate strong robustness against advanced bot strategies, highlighting the effectiveness of jointly leveraging implicit semantic representations and AIGC-enhanced behavioral patterns for emerging LLM-driven social bot detection.