  User comments on social media have been recognized as a crucial factor in
distinguishing between fake and real news, with many studies focusing on the
textual content of user reactions. However, the interactions among agents in
the comment sections for fake and real news have not been fully explored. In
this study, we analyze a dataset comprising both fake and real news from Reddit
to investigate agent interaction patterns, considering both the network
structure and the sentiment of the nodes. Our findings reveal that (i) comments
on fake news are more likely to form groups, (ii) compared to fake news, where
users generate more negative sentiment, real news tend to elicit more neutral
and positive sentiments. Additionally, nodes with similar sentiments cluster
together more tightly than anticipated. From a dynamic perspective, we found
that the sentiment distribution among nodes stabilizes early and remains stable
over time. These findings have both theoretical and practical implications,
particularly for the early detection of real and fake news within social
networks.
