  This paper presents our submission to the SardiStance 2020 shared task,
describing the architecture used for Task A and Task B. While our submission
for Task A did not exceed the baseline, retraining our model using all the
training tweets, showed promising results leading to (f-avg 0.601) using
bidirectional LSTM with BERT multilingual embedding for Task A. For our
submission for Task B, we ranked 6th (f-avg 0.709). With further investigation,
our best experimented settings increased performance from (f-avg 0.573) to
(f-avg 0.733) with same architecture and parameter settings and after only
incorporating social interaction features -- highlighting the impact of social
interaction on the model's performance.
