  Misinformation undermines the credibility of social media and poses
significant threats to modern societies. As a countermeasure, Twitter has
recently introduced "Birdwatch," a community-driven approach to address
misinformation on Twitter. On Birdwatch, users can identify tweets they believe
are misleading, write notes that provide context to the tweet and rate the
quality of other users' notes. In this work, we empirically analyze how users
interact with this new feature. For this purpose, we collect all Birdwatch
notes and ratings since the introduction of the feature in early 2021. We then
map each Birdwatch note to the fact-checked tweet using Twitter's historical
API. In addition, we use text mining methods to extract content characteristics
from the text explanations in the Birdwatch notes (e.g., sentiment). Our
empirical analysis yields the following main findings: (i) users more
frequently file Birdwatch notes for misleading than not misleading tweets.
These misleading tweets are primarily reported because of factual errors, lack
of important context, or because they contain unverified claims. (ii) Birdwatch
notes are more helpful to other users if they link to trustworthy sources and
if they embed a more positive sentiment. (iii) The helpfulness of Birdwatch
notes depends on the social influence of the author of the fact-checked tweet.
For influential users with many followers, Birdwatch notes yield a lower level
of consensus among users and community-created fact checks are more likely to
be seen as being incorrect. Altogether, our findings can help social media
platforms to formulate guidelines for users on how to write more helpful fact
checks. At the same time, our analysis suggests that community-based
fact-checking faces challenges regarding biased views and polarization among
the user base.
