  This study investigates echo chambers in social networks through an analysis
of Twitter news accounts. Utilizing bias labels from the AllSides website, we
construct a dataset representing six dimensions of news bias. Through manual
extraction of follower/following relationships, we analyze interactions among
65 active Twitter news accounts. Despite the relatively small size of the
network node data utilized, results reveal distinct clustering patterns
indicative of echo chambers, with limited interaction between conflicting
ideologies. This study underscores the potential impact of bias on information
dissemination and democratic expression. These findings offer valuable insights
into the dynamics of echo chambers in contemporary social media environments.
