Online propaganda is a mechanism to influence the opinions of social media users. It is a growing menace to public health, democratic institutions, and public society. The present study proposes a propaganda detection framework as a binary classification model based on a news repository. Several feature models are explored to develop a robust model such as part-of-speech, LIWC, word uni-gram, Embeddings from Language Models (ELMo), FastText, word2vec, latent semantic analysis (LSA), and char tri-gram feature models. Moreover, fine-tuning of the BERT is also performed. Three oversampling methods are investigated to handle the imbalance status of the Qprop dataset. SMOTE Edited Nearest Neighbors (ENN) presented the best results. The fine-tuning of BERT revealed that the BERT-320 sequence length is the best model. As a standalone model, the char tri-gram presented superior performance as compared to other features. The robust performance is observed against the combination of char tri-gram + BERT and char tri-gram + word2vec and they outperformed the two state-of-the-art baselines. In contrast to prior approaches, the addition of feature selection further improves the performance and achieved more than 97.60% recall, f1-score, and AUC on the dev and test part of the dataset. The findings of the present study can be used to organize news articles for various public news websites.

Threatening content detection on social media has recently gained attention. There is very limited work regarding threatening content detection in low-resource languages, especially in Urdu. Furthermore, previous work explored only mono-lingual approaches, and multi-lingual threatening content detection was not studied. This research addressed the task of Multi-lingual Threatening Content Detection (MTCD) in Urdu and English languages by exploiting transfer learning methodology with fine-tuning techniques. To address the multi-lingual task, we investigated two methodologies: 1) Joint multi-lingual, and 2) Joint-translated method. The former approach employs the concept of building a universal classifier for different languages whereas the latter approach applies the translation process to transform the text into one language and then perform classification. We explore the Multilingual Representations for Indian Languages (MuRIL) and Robustly Optimized BERT Pre-Training Approach (RoBERTa) with fine-tuning that already demonstrated state-of-the-art in capturing the contextual and semantic characteristics within the text. For hyper-parameters, manual search and grid search strategies are utilized to find the optimum values. Various experiments are performed on bi-lingual English and Urdu datasets and findings revealed that the proposed methodology outperformed the baselines and showed benchmark performance. The RoBERTa model achieved the highest performance by demonstrating 92% accuracy and 90% macro f1-score with the joint multi-lingual approach.