Detecting Misinformation with LLM-Predicted Credibility Signals and Weak
  Supervision
João A. Leite Olesya Razuvayevskaya Kalina Bontcheva Carolina Scarton
Credibility signals represent a wide range of heuristics that are typically
used by journalists and fact-checkers to assess the veracity of online content.
Automating the task of credibility signal extraction, however, is very
challenging as it requires high-accuracy signal-specific extractors to be
trained, while there are currently no sufficiently large datasets annotated
with all credibility signals. This paper investigates whether large language
models (LLMs) can be prompted effectively with a set of 18 credibility signals
to produce weak labels for each signal. We then aggregate these potentially
noisy labels using weak supervision in order to predict content veracity. We
demonstrate that our approach, which combines zero-shot LLM credibility signal
labeling and weak supervision, outperforms state-of-the-art classifiers on two
misinformation datasets without using any ground-truth labels for training. We
also analyse the contribution of the individual credibility signals towards
predicting content veracity, which provides new valuable insights into their
role in misinformation detection.