  We propose a novel system to help fact-checkers formulate search queries for
known misinformation claims and effectively search across multiple social media
platforms. We introduce an adaptable rewriting strategy, where editing actions
for queries containing claims (e.g., swap a word with its synonym; change verb
tense into present simple) are automatically learned through offline
reinforcement learning. Our model uses a decision transformer to learn a
sequence of editing actions that maximizes query retrieval metrics such as mean
average precision. We conduct a series of experiments showing that our query
rewriting system achieves a relative increase in the effectiveness of the
queries of up to 42%, while producing editing action sequences that are human
interpretable.
