  Most prior and current research examining misinformation spread on social
media focuses on reports published by 'fake' news sources. These approaches
fail to capture another potential form of misinformation with a much larger
audience: factual news from mainstream sources ('real' news) repurposed to
promote false or misleading narratives. We operationalize narratives using an
existing unsupervised NLP technique and examine the narratives present in
misinformation content. We find that certain articles from reliable outlets are
shared by a disproportionate number of users who also shared fake news on
Twitter. We consider these 'real' news articles to be co-shared with fake news.
We show that co-shared articles contain existing misinformation narratives at a
significantly higher rate than articles from the same reliable outlets that are
not co-shared with fake news. This holds true even when articles are chosen
following strict criteria of reliability for the outlets and after accounting
for the alternative explanation of partisan curation of articles. For example,
we observe that a recent article published by The Washington Post titled
"Vaccinated people now make up a majority of COVID deaths" was
disproportionately shared by Twitter users with a history of sharing
anti-vaccine false news reports. Our findings suggest a strategic repurposing
of mainstream news by conveyors of misinformation as a way to enhance the reach
and persuasiveness of misleading narratives. We also conduct a comprehensive
case study to help highlight how such repurposing can happen on Twitter as a
consequence of the inclusion of particular narratives in the framing of
mainstream news.
