ChatGPT v.s. Media Bias: A Comparative Study of GPT-3.5 and Fine-tuned
  Language Models
Zehao Wen Rabih Younes
In our rapidly evolving digital sphere, the ability to discern media bias
becomes crucial as it can shape public sentiment and influence pivotal
decisions. The advent of large language models (LLMs), such as ChatGPT, noted
for their broad utility in various natural language processing (NLP) tasks,
invites exploration of their efficacy in media bias detection. Can ChatGPT
detect media bias? This study seeks to answer this question by leveraging the
Media Bias Identification Benchmark (MBIB) to assess ChatGPT's competency in
distinguishing six categories of media bias, juxtaposed against fine-tuned
models such as BART, ConvBERT, and GPT-2. The findings present a dichotomy:
ChatGPT performs at par with fine-tuned models in detecting hate speech and
text-level context bias, yet faces difficulties with subtler elements of other
bias detections, namely, fake news, racial, gender, and cognitive biases.