  Understanding human interactions and social structures is an incredibly
important task, especially in such an interconnected world. One task that
facilitates this is Stance Detection, which predicts the opinion or attitude of
a text towards a target entity. Traditionally, this has often been done mainly
via the use of text-based approaches, however, recent work has produced a model
(CT-TN) that leverages information about a user's social network to help
predict their stance, outperforming certain cross-target text-based approaches.
Unfortunately, the data required for such graph-based approaches is not always
available. This paper proposes two novel tools for Stance Detection: the Ego
Network Model (ENM) and the Signed Ego Network Model (SENM). These models are
founded in anthropological and psychological studies and have been used within
the context of social network analysis and related tasks (e.g., link
prediction). Stance Detection predictions obtained using these features achieve
a level of accuracy similar to the graph-based features used by CT-TN while
requiring less and more easily obtainable data. In addition to this, the
performances of the inner and outer circles of the ENM, representing stronger
and weaker social ties, respectively are compared. Surprisingly, the outer
circles, which contain more numerous but less intimate connections, are more
useful for predicting stance.
