The main task of this paper is to analyse and explicate the SOCIAL REALITY of citizenship in post-Revolutionary Iran. It aims to provide a sociological analysis concerning the formation process of citizenship in Iran along with citizenship-forming mechanisms. It is the configuration of citizenship, as a modern phenomenon in post-Revolutionary Iran, in its SOCIAL type, SOCIAL content, SOCIAL form and SOCIAL domain, that is considered here, seen through its civil, political and SOCIAL aspects. The SOCIAL theory of citizenship, constitutes the theoretical framework, and the method is historical sociology. The article sets forth three questions concerning the nature of processes affecting citizenship formation and examines mechanisms influencing it, among which are structures of citizenship configuration, and three related hypotheses. These hypotheses would indicate the efficacy impact of the discourse between the substantive (legislative) and constitutional laws. They also look at the orientation of developement paIns, and formation of modern socio-political demnads.

In the present paper, subsequent to expressing the theoretical approaches which explain the episteme-methodological basis of encountering and interpreting the SOCIAL events, and by taking advantage of the SOCIAL constructivism approach which culminated with publication of the well-known work by Peter Burger and Thomas Luckman, and combining its common aspects with those of other interpretive approaches and considering SOCIAL linguistics topics and using citational method, function of public libraries has been elucidated in forming desirable structures such as national identity, self-belief, national unity, and preventing westernization with the aim of determining the strategic situation of public libraries for making policy in this field.

Introduction: Fear of emergence in society and SOCIAL discourse is one of the most common types of SOCIAL fears and this disorder is recognized as a multifaceted disorder. Therefore, the present study was conducted to determine the effectiveness of virtual REALITY package on SOCIAL panic and SOCIAL lectures. Methods: This study was a quasi-experimental study with a pretest-posttest design and a two-month follow-up with a control group. The statistical population of the present study consisted of managers of the IT department in Tehran in 1399, from which 30 people were selected by convenience sampling and randomly divided into two groups of experimental (15 people) and control (15 people). The experimental group underwent virtual REALITY treatment protocol for 12 sessions (15 minutes each session). But no intervention was done for the control group. Both groups responded to Conner et al. 's (2000) list of SOCIAL phobias at different stages. Analysis of variance mixed with repeated measures and Bonferroni post hoc test using SPSS software version 23 were used to analyze the data. Results: The results showed that the virtual REALITY package was able to fear (F = 23. 08; P <0. 001), avoidance (F = 13. 590; P <0. 001), physiological discomfort (F = 23. 88; 001 P <0. 05) and SOCIAL phobia in general (F = 67. 54; P <0. 001) significantly improved and the results were lasting in the follow-up period (P <0. 05). Conclusions: The results showed the effectiveness of virtual REALITY therapy protocol in reducing the symptoms of SOCIAL phobia and SOCIAL speeches. Therefore, this method can be used as a new treatment method to treat SOCIAL phobia and other similar disorders.

Organizations, community groups, elites, and government policies in many ways influence the process of future construction, while they are heavily influenced by various scholars. Based on the theories of SOCIAL status and SOCIALization, and the policy model for the elite of the masses, the future is continually being influenced by the individual and organizational elements that are active in the society, and these individuals and organizations, usually they formulate their actions with scientific justifications, which are taught in interaction with scientists and elites. Moreover, the scientific findings from the efforts of various scholars may be influenced by a set of theories focused on the implementation of the future world. Because these interdisciplinary sciences are the theoretical outcomes of various sciences and technologies, they affect theories and strategies of change and future transformation programs; the effect of the influence of knowledge in the general language, the logic of government agents, and the procedures Behavioral and constructive structure in the process of development of organizations and SOCIAL institutions. This article discusses how the future is made. The process of constructing the future introduces the ultimate SOCIAL components, linguistic facts, decision-making and active actors of actors (organizations, people, and elites). These components shape the future REALITY by influencing interdisciplinary sciences and their mutual influence on it.

Using Karl Popper's critique of positivism and inductivism, we attempted to criticize the main bases of grounded theory. According to grounded theory, SOCIAL REALITY can be understood by emptying our mind from any prejudgements and ethical valuation in the research process, but emphasizing on observation, paying attention to the differences and similarities of data, and dedcting abstract concepts. We argue that, in practice, this claim is not true and it leads to a distorted SOCIAL REALITY. Following these principles makes SOCIAL researchers become an information worker and not a critic of SOCIAL REALITY. Therefore, not only the science of sociology loses its critical aspect but also justifies the status quo.

Named Entity Recognition (NER) is one of the essential prerequisites for many natural language processing tasks. All public corpora for Persian named entity recognition such as ParsNERCorp and ArmanPersoNERCorpus are based on the Bijankhan corpus, which is originated from the Hamshahri newspaper in 2004. Correspondingly, most of the published named entity recognition models in Persian are specially tuned for the news data and are not flexible enough to be applied in different text categories such as SOCIAL media texts. In this work, we introduce ParsNER-SOCIAL, a corpus for training named entity recognition models in the Persian language built from SOCIAL media sources. This corpus consists of 205, 373 tokens, and their NER tags crawled from SOCIAL media contents, including 10 Telegram channels in 10 different categories. Furthermore, three supervised methods are introduced and trained based on the ParsNER-SOCIAL corpus: two conditional random field models as baseline models and one state-of-the-art deep learning model with six different configurations are evaluated on the basis of the proposed dataset. The experiments performed show that the Mono-Lingual Persian models based on Bidirectional Encoder Representations from Transformers (MLBERT) outperform the other approaches on the ParsNER-SOCIAL corpus. Among the different configurations of the MLBERT models, the ParsBERT+BERT-TokenClass model has obtained an F1-score of 89. 65%.