  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 between the introduction of the feature in early 2021 and end
of July 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 treat unverified
claims as facts. (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 social influence of the author of the source tweet is associated with
differences in the level of user consensus. 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 and
argumentative. 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 opinion speculation and polarization among the user base.
