Exposing Pink Slime Journalism: Linguistic Signatures and Robust Detection Against LLM-Generated Threats
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915655937163264 |
|---|---|
| author | Shahriar, Sadat Ayoobi, Navid Mukherjee, Arjun Musharrat, Mostafa Vamsi, Sai Vishnu |
| author_facet | Shahriar, Sadat Ayoobi, Navid Mukherjee, Arjun Musharrat, Mostafa Vamsi, Sai Vishnu |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exposing Pink Slime Journalism: Linguistic Signatures and Robust Detection Against LLM-Generated Threats Shahriar, Sadat Ayoobi, Navid Mukherjee, Arjun Musharrat, Mostafa Vamsi, Sai Vishnu Computation and Language Machine Learning 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%. |
| title | Exposing Pink Slime Journalism: Linguistic Signatures and Robust Detection Against LLM-Generated Threats |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2512.05331 |