Query Rewriting for Effective Misinformation Discovery

TL;DR

This paper presents a reinforcement learning-based query rewriting system that improves the effectiveness of search queries for misinformation detection across social media, achieving up to 42% better retrieval performance.

Contribution

It introduces an adaptable, learned query rewriting strategy using decision transformers to enhance misinformation search queries, with interpretable editing actions.

Findings

Up to 42% improvement in query effectiveness.

Queries remain human interpretable.

Reinforcement learning effectively learns editing actions.

Abstract

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.