Exposing Pink Slime Journalism: Linguistic Signatures and Robust Detection Against LLM-Generated Threats
Year: 2025
Type: conference-paper
Abstract: The local news landscape, a vital source of reliable information for 28 million Americans, faces a growing threat from Pink Slime Journalism, a low-quality, auto-generated articles that mimic legitimate local reporting.Detecting these deceptive articles requires a fine-grained analysis of their linguistic, stylistic, and lexical characteristics.In this work, we conduct a comprehensive study to uncover the distinguishing patterns of Pink Slime content and propose detection strategies based on these insights.Beyond traditional generation methods, we highlight a new adversarial vector: modifications through large language models (LLMs).Our findings reveal that even consumer-accessible LLMs can significantly undermine existing detection systems, reducing their performance by up to 40% in F1-score.To counter this threat, we introduce a robust learning framework specifically designed to resist LLM-based adversarial attacks and adapt to the evolving landscape of automated pink slime journalism, and showed and improvement by up to 27%.
Institution: University of Houston
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W7124829562