  In recent years, the proliferation of misinformation on social media
platforms has become a significant concern. Initially designed for sharing
information and fostering social connections, platforms like Twitter (now
rebranded as X) have also unfortunately become conduits for spreading
misinformation. To mitigate this, these platforms have implemented various
mechanisms, including the recent suggestion to use crowd-sourced non-expert
fact-checkers to enhance the scalability and efficiency of content vetting. An
example of this is the introduction of Community Notes on Twitter.
  While previous research has extensively explored various aspects of Twitter
tweets, such as information diffusion, sentiment analytics and opinion
summarization, there has been a limited focus on the specific feature of
Twitter Community Notes, despite its potential role in crowd-sourced
fact-checking. Prior research on Twitter Community Notes has involved empirical
analysis of the feature's dataset and comparative studies that also include
other methods like expert fact-checking. Distinguishing itself from prior
works, our study covers a multi-faceted analysis of sources and audience
perception within Community Notes. We find that the majority of cited sources
are news outlets that are left-leaning and are of high factuality, pointing to
a potential bias in the platform's community fact-checking. Left biased and low
factuality sources validate tweets more, while Center sources are used more
often to refute tweet content. Additionally, source factuality significantly
influences public agreement and helpfulness of the notes, highlighting the
effectiveness of the Community Notes Ranking algorithm. These findings showcase
the impact and biases inherent in community-based fact-checking initiatives.
