Freedom of expression on social media has provided oppressed people with many opportunities to raise their voices against violence and injustice, but this freedom is being misused to spread various forms of hate speech. Several studies have been conducted to identify hate speech in high-resource languages, however, work on under-resource languages is very limited, especially for Nastaliq Urdu. Pakistan has been dealing with the issue of hateful and violence incitation content for the last two decades. Therefore, this study handled the problem of detecting hate speech and fine-grained multi-class target community identification in Nastaliq Urdu. Using the transfer learning paradigm, two benchmark Urdu transformer models are explored with fine-tuning. A Nastaliq Urdu Hate Speech and Target Community (HSTC) corpus is designed by collecting posts from Pakistani Facebook accounts. In particular, the strengths of the Urdu Robustly Optimized BERT Pre-Training Approach (Urdu-RoBERTa) and Urdu Distillated Bidirectional Encoder Representations from Transformers (Urdu-DistilBERT) are explored to design an automated system instead of hand-crafted features. The proposed framework consists of four steps: 1) Data cleaning and preprocessing, 2) Data transformation, 3) Utilization of Grid search for fine-tuning process, and 4) Classification (binary and multi-class). The results on the Nastaliq Urdu corpus showed that the proposed system achieved benchmark performance for binary classification task (hate speech) and target community detection (multi-class classification) on hateful Facebook posts. In particular, fine-tuned DistilBERT achieved 86.58% accuracy and 86.52% f1-score for binary classification and outperformed sixteen baselines. Furthermore, it demonstrated 84.17% accuracy and 83.91% f1-score for target community (religious, political, and gender-based) identification and outperformed all baselines. The findings of this study can be beneficial in detecting and filtering out hate speech in Nastaliq Urdu on the Facebook platform.

Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making.

Designing natural language processing (NLP) based systems for under-resource languages opens up new opportunities for marginalized communities. However, these NLP systems usually face several issues concerning under-resource languages such as lack of annotated data and available processing resources that result in poor NLP performance. These concerns can be addressed with cross-lingual learning (CLL) approach and multilingual learning is one of the special cases of CLL. Hope expression identification is a new emerging research direction that supports and promotes positive material on social media to encourage positivity and harmony in society. However, it is rarely handled as a multilingual paradigm and the Urdu language is ignored by the literature. This paper deals with the task of detecting multilingual hope expression using a transfer learning technique with fine-tuning. For this purpose, the joint multilingual methodology is considered which suggests designing a universal classifier for different languages. The potential of multilingual bidirectional encoder representations from transformers (mBERT) is exploited with fine-tuning and a grid search approach is used to find optimal values of hyperparameters. An annotated Urdu corpus is also designed. The experiments conducted on the bilingual hope expression corpus revealed that the proposed methodology presented benchmark performance and outperformed the nine baselines. Furthermore, it improved hope class identification by 13.44%, not-hope by 0.07%, and macro by 6.66% considering the F1-score.