@inproceedings{neves-etal-2026-news,
    title = "News Credibility Assessment by {LLM}s and Humans: Implications for Political Bias",
    author = "Neves, Pia Wenzel  and
      Jakob, Charlott  and
      Schmitt, Vera",
    editor = "Barnes, Jeremy  and
      Barriere, Valentin  and
      De Clercq, Orph{\'e}e  and
      Klinger, Roman  and
      Nouri, C{\'e}lia  and
      Nozza, Debora  and
      Singh, Pranaydeep",
    booktitle = "The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis ({WASSA} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.wassa-1.15/",
    pages = "172--207",
    ISBN = "979-8-89176-378-4",
    abstract = "In an era of rapid misinformation spread, LLMs have emerged as tools for assessing news credibility at scale. However, the assessments are influenced by social and cultural biases. Studies investigating political bias, compare model credibility ratings with expert credibility ratings. Comparing LLMs to the perceptions of political camps extends this approach to detecting similarities in their biases.We compare LLM-generated credibility and bias ratings of news outlets with expert assessments and stratified political opinions collected through surveys. We analyse three models (Llama 3.3 70B, Mixtral 8x7B, and GPT-OSS 120B) across 47 news outlets from two countries (U.S. and Germany).We found that models demonstrated consistently high alignment with expert ratings, while showing weaker and more variable alignment with public opinions. For US-American news outlets all models showed stronger alignment with center-left perceptions, while for German news outlets the alignment is more diverse."
}