Exposing Pink Slime Journalism: Linguistic Signatures and Robust Detection Against LLM-Generated Threats

TL;DR

This paper investigates linguistic patterns of Pink Slime Journalism, reveals how LLMs undermine detection, and proposes a robust framework to improve detection accuracy against evolving AI-generated deceptive news.

Contribution

It uncovers linguistic signatures of Pink Slime content, analyzes LLM-based adversarial attacks, and introduces a robust detection framework to counter these threats.

Findings

Detection performance drops by up to 40% under LLM attacks.

Proposed framework improves detection accuracy by up to 27%.

Identifies key linguistic features distinguishing Pink Slime articles.

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%.