  Social news websites, such as Reddit, have evolved into prominent platforms
for sharing and discussing news. A key issue on social news websites sites is
the formation of echo chambers, which often lead to the spread of highly biased
or uncredible news. We develop a method to identify communities within a social
news website that are prone to uncredible or highly biased news. We employ a
user embedding pipeline that detects user communities based on their stances
towards posts and news sources. We then project each community onto a
credibility-bias space and analyze the distributional characteristics of each
projected community to identify those that have a high risk of adopting beliefs
with low credibility or high bias. This approach also enables the prediction of
individual users' susceptibility to low credibility content, based on their
community affiliation. Our experiments show that latent space clusters
effectively indicate the credibility and bias levels of their users, with
significant differences observed across clusters -- a $34\%$ difference in the
users' susceptibility to low-credibility content and a $8.3\%$ difference in
the users' susceptibility to high political bias.
