Abstract Universities are influential and important organizations in society, where large amounts of data and information are produced and used. Considering the importance of using big data technology in universities, it is necessary to check how far Iranian universities have progressed in the subject of big data. The purpose of this article is to evaluate the maturity of big data in Iran's public universities. To achieve this goal, the TDWI model has been used to evaluate the maturity of big data in Iranian public universities. The questionnaire was sent to the entire statistical sample, and finally 50 completed questionnaires were received. Field method and standard questionnaire tools were used to collect data. Face validity was used to evaluate the validity of the questionnaire and Cronbach's alpha coefficient was used to evaluate the reliability.The results of the analysis showed that 2 universities are in the nascent stage of maturity, 27 universities are in the early stage, 16 universities are in the established stage and 5 universities are in the mature stage and there is no university at the advanced level of the maturity model. No relationship was found between the number of university students and big data maturity in universities, and the average level of big data maturity is significantly different between Tehran and city universities.In this study, based on the results, recommendations were provided to each of the universities participating in the survey to promote big data maturity.

Abstract Abstract
   Due to the global attention to cryptocurrencies especially bitcoin, governments, banks, and other industries are expected to use cryptocurrencies in their daily transactions. Therefore, as any other financial field, there is a need to identify the challenges in this field to safe investment. On the other hand, by the expansion of social networks, unstructured data is increasing, which can be used to create added value in various areas such as sentiment analysis. Therefore, this study was conducted to investigate the impact of perceived risk by social network users on the price of Bitcoin. According to the research findings, the identified risks in the field of bitcoin include social, economic, security, technological and legal risks., The conversations on the Bitcoin Talk Site were used to extract the bitcoin risks. After collecting these conversations by the web crawler, the conversations were clustered into thematic clusters using the Latent Dirichlet Allocation algorithm, which is one of the most popular methods in Topic Modeling. were analyzed using vocabulary-based method and AFINN dictionary. NARX Neural Network was used to measure the effect of Userschr('39') Sentiment on the price of Bitcoin. The results show a correlation of 0.99 and a mean square error of 0.001, which means that there is a correlation between the actual price and the predicted price of Bitcoin. The findings of this study can attract the attention of financial actors and businessmen in the field of bitcoin to plan a safe investment and reduce risk.