  The present paper is about the participation of our team "techno" on
CERIST'22 shared tasks. We used an available dataset "task1.c" related to
covid-19 pandemic. It comprises 4128 tweets for sentiment analysis task and
8661 tweets for fake news detection task. We used natural language processing
tools with the combination of the most renowned pre-trained language models
BERT (Bidirectional Encoder Representations from Transformers). The results
shows the efficacy of pre-trained language models as we attained an accuracy of
0.93 for the sentiment analysis task and 0.90 for the fake news detection task.
