  This paper illustrates a detail description of the system and its results
that developed as a part of the participation at CONSTRAINT shared task in
AAAI-2021. The shared task comprises two tasks: a) COVID19 fake news detection
in English b) Hostile post detection in Hindi. Task-A is a binary
classification problem with fake and real class, while task-B is a multi-label
multi-class classification task with five hostile classes (i.e. defame, fake,
hate, offense, non-hostile). Various techniques are used to perform the
classification task, including SVM, CNN, BiLSTM, and CNN+BiLSTM with tf-idf and
Word2Vec embedding techniques. Results indicate that SVM with tf-idf features
achieved the highest 94.39% weighted $f_1$ score on the test set in task-A.
Label powerset SVM with n-gram features obtained the maximum coarse-grained and
fine-grained $f_1$ score of 86.03% and 50.98% on the task-B test set
respectively.
