  The spread of online misinformation threatens public health, democracy, and
the broader society. While professional fact-checkers form the first line of
defense by fact-checking popular false claims, they do not engage directly in
conversations with misinformation spreaders. On the other hand, non-expert
ordinary users act as eyes-on-the-ground who proactively counter misinformation
-- recent research has shown that 96% counter-misinformation responses are made
by ordinary users. However, research also found that 2/3 times, these responses
are rude and lack evidence. This work seeks to create a counter-misinformation
response generation model to empower users to effectively correct
misinformation. This objective is challenging due to the absence of datasets
containing ground-truth of ideal counter-misinformation responses, and the lack
of models that can generate responses backed by communication theories. In this
work, we create two novel datasets of misinformation and counter-misinformation
response pairs from in-the-wild social media and crowdsourcing from
college-educated students. We annotate the collected data to distinguish poor
from ideal responses that are factual, polite, and refute misinformation. We
propose MisinfoCorrect, a reinforcement learning-based framework that learns to
generate counter-misinformation responses for an input misinformation post. The
model rewards the generator to increase the politeness, factuality, and
refutation attitude while retaining text fluency and relevancy. Quantitative
and qualitative evaluation shows that our model outperforms several baselines
by generating high-quality counter-responses. This work illustrates the promise
of generative text models for social good -- here, to help create a safe and
reliable information ecosystem. The code and data is accessible on
https://github.com/claws-lab/MisinfoCorrect.
