In this paper we present UPAppliedCL's contribution to the GermEval 2021 Shared Task. In particular, we participated in Subtasks 2 (Engaging Comment Classification) and 3 (Fact-Claiming Comment Classification). While acceptable results can be obtaine
d by using unigrams or linguistic features in combination with traditional machine learning models, we show that for both tasks transformer models trained on fine-tuned BERT embeddings yield best results.
This study comes to reveal about the psychological and social effects of the use of
special young university for social networking sites, and by knowing the motives of
undergraduates to use these sites, and the various factors that led them to enga
ge in, as well
as to identify the psychological and social effects of the use of specifically Facebook site,
through a study field on a sample of university students Tishreen users of Facebook, where
we relied on the analytical descriptive approach and we used the questionnaire to collect
data from a sample of the tool (150) students from the University of Tishreen.
