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This paper presents our submission to the SardiStance 2020 shared task,describing the architecture used for Task A and Task B. While our submissionfor Task A did not exceed the baseline, retraining our model using all thetraining tweets, showed promising results leading to (f-avg 0.601) usingbidirectional LSTM with BERT multilingual embedding for Task A. For oursubmission 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 onlyincorporating social interaction features -- highlighting the impact of socialinteraction on the model's performance.

QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs