  Detecting misinformation threads is crucial to guarantee a healthy
environment on social media. We address the problem using the data set created
during the COVID-19 pandemic. It contains cascades of tweets discussing
information weakly labeled as reliable or unreliable, based on a previous
evaluation of the information source. The models identifying unreliable threads
usually rely on textual features. But reliability is not just what is said, but
by whom and to whom. We additionally leverage on network information. Following
the homophily principle, we hypothesize that users who interact are generally
interested in similar topics and spreading similar kind of news, which in turn
is generally reliable or not. We test several methods to learn representations
of the social interactions within the cascades, combining them with deep neural
language models in a Multi-Input (MI) framework. Keeping track of the sequence
of the interactions during the time, we improve over previous state-of-the-art
models.
