arXiv:2608. 15893v1 Announce Type: new Abstract: The rise of social media bots poses a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms.
By Nof Orenstein, Yoni Birman
The paper introduces CSC, a calibrated-simplicity framework for detecting social bots in the era of large language models. CSC combines a simplified prototype-guided graph expert, calibrated simplex-constrained fusion, and a lightweight inconsistency expert to address modality conflict between semantic and structural signals. Experiments on TwiBot-22, TwiBot-20, and MGStBot-large demonstrate that CSC improves calibrated decision quality while maintaining competitive performance across benchmarks.
By Yipeng Qian, Pengjie Zhao, Chaoxi Niu
arXiv:2608.30948v1 Announce Type: new
Abstract: LLMs can imitate how people write, which raises concerns about impersonation, trust, and detection in social settings. These concerns are especially im...
By Dan Schumacher, Pragathi Durga Rajarajan, Haven Kotara, Roman Rendon, Kosi Atupulazi, Deepti Tagare, Ismaila Temitayo Sanusi, Fred G. Martin, Anthony Rios
The paper introduces ASURRE, a benchmark dataset for detecting AI‑assisted responses in online surveys. It evaluates how different LLM usage strategies—ranging from full generation to persona‑grounded agentic completion—affect the performance of existing machine‑generated text detectors. The study finds that while naive AI usage is easily detected, more sophisticated persona‑grounded agents approach chance performance, yet still leave identifiable behavioural traces that can be aggregated to improve detection.
By Qizhou Wang, Bogdan Mamaev, Christopher Leckie
The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.
By Jiyao Yang, Yang Liu, Zhenyue Qin, Qingyu Chen, Xiuzhen Zhang
arXiv:2609.14638v1 Announce Type: cross
Abstract: This paper is an encore submission of our 2026 journal article "Expertise and Information Seeking in the Age of Generative AI: New Procedures, New Pr...
By Alexi Orchard, Shannon Lodoen