Easy access to social media enables users to express their opinions and ideology about various topics like news, videos, and personalities freely, without any fear, and often in an offensive manner. It is a vital task to detect comments with offensive language on social media platforms and relies on a complete and comprehensive tagged Dataset. Therefore, in this paper, we introduce and make publicly available PerBOLD, a new Persian comment Dataset collected from Instagram as a popular platform among Iranian. We follow a two-level manual annotation process in order to determine whether a comment has offensive language or not and fine-grained tags of different types of offensive language. Furthermore, we present some interesting aspects of data and analysis them.

Stance detection aims to identify an author's stance towards a specific topic which has become a critical component in applications such as fake news detection, claim validation, author profiling, etc. However, while the stance is easily detected by humans, machine learning models are falling short of this task. In the English language, due to having large and appropriate e Datasets, relatively good accuracy has been achieved in this field, but in the Persian language, due to the lack of data, we have not made significant progress in stance detection. So, in this paper, we present a stance detection Dataset that contains 3813 labeled tweets. We provide a detailed description of the newly created Dataset and develop deep learning models on it. Our best model achieves a macro-average F1-score of 58%. Moreover, our Dataset can facilitate research in some fields in Persian such as cross-lingual stance detection, author profiling, etc.

An information retrieval system tries to retrieve documents related to a question/query. The retrieval is done from a large collection of documents, and the size of this collection can be from a few thousand documents to millions of documents. In recent years, a lot of research has been done to develop information retrieval systems using language models. However, in this research field, no research has been done for the Persian language. One of its main reasons is the lack of a suitable Persian Dataset for training language models. In this research, first, a Persian Dataset for information retrieval is presented. After that, methods for enriching this data set are investigated. This enrichment is done by defining multi-level relationships between a document and a question. In this regard, the new Dataset can show the relationship between question and document in four levels (unrelated - related - highly related - completely related) instead of two levels (completely unrelated - completely related). The name of the generated Dataset is PersianMLIR. Experiments show that by using multi-level relationships, the performance of the system improves for both Persian and English languages, where the improvement is 1.87% for the Persian language.

Introduction: Access to quality information plays an important role in disasters. Due to lack of minimum Dataset necessary for health management in disasters, this study aimed to design a minimum Dataset for health management in disasters in Iran. Methods: This descriptive study was conducted in 2016 using Delphi technique. 18 individuals were selected and enrolled the study, using heterogeneous sampling method. The Delphi technique was performed in three steps, and mean calculation was used to analyze the data. Results: The minimum health management Dataset of before, during, and after the disaster included 9 data classes and 84 data elements, 9 data classes and 45 data elements, and 6 data classes and 54 data elements, respectively. Conclusion: Disaster managers and policymakers can use the results of this study to collect and process the correct health information, which facilitates health management during the disasters.