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With Twitter's growth and popularity, a huge number of views are shared by users on various topics, making this platform a valuable information source on various political, social, and economic issues. This paper investigates English tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions and sentiments regarding the conflict. The tweets' positive and negative sentiments are analyzed using a BERT-based model, and the time series associated with the frequency of positive and negative tweets for various countries is calculated. Then, we propose a method based on the neighborhood average for modeling and clustering the time series of countries. The clustering results provide valuable insight into public opinion regarding this conflict. Among other things, we can mention the similar thoughts of users from the United States, Canada, the United Kingdom, and most Western European countries versus the shared views of Eastern European, Scandinavian, Asian, and South American nations toward the conflict.

Objectives This study aimed to investigate awareness of women living in the western region of Iran about warning signs and risk factors for ovarian cancer. Methods This cross-sectional, descriptive-analytical study was conducted in Sanandaj and Kermanshah, Iran, 2020 and 2021. In this study, 1081 women aged 18–70 years were selected as the sample to fill out the electronic version of the Ovarian Cancer Awareness Measure (OCAM) to identify the significant variables of ovarian cancer warning signs and risk factors. The obtained data were statistically analyzed by descriptive statistics and ordinal logistic regression in SPSS 19. Results In this study, 60.9% of the participants had medium awareness of the subject. The results indicated that participants with higher educational attainment (P Conclusion The study findings showed that women living in the western region of Iran have moderate awareness of ovarian cancer and insufficient knowledge of cancer warning signs; this suggests that it is necessary to train Iranian women to raise awareness of the signs and risk factors for ovarian cancer.

Additional file 1. W.H.O Psycho-Social Environment (PSE) Profile.

Additional file 1. Supplementary figures: The frequency distribution of side effects over time in the study groups.

With the emergence of the Internet of things (IoT), human life is now progressing towards smartification faster than ever before. Thus, smart cities become automated in different aspects such as business, education, economy, medicine, and urban areas. Since smartification requires a variety of dynamic information in different urban dimensions, mobile crowdsourcing has gained importance in smart cities. This chapter systematically reviews the related applications of smart cities that use mobile crowdsourcing for data acquisition. For this purpose, the applications are classified as environmental, urban life, and transportation categories and then investigated in detail. This survey helps in understanding the current situation of smart cities from the viewpoint of crowdsourcing and discusses the future research directions in this field.

One of the most important incidents in the world in 2020 is the outbreak of the Coronavirus. Users on social networks publish a large number of comments about this event. These comments contain important hidden information of public opinion regarding this pandemic. In this research, a large number of Coronavirus-related tweets are considered and analyzed using natural language processing and information retrieval science. Initially, the location of the tweets is determined using a dictionary prepared through the Geo-Names geographic database, which contains detailed and complete information of places such as city names, streets, and postal codes. Then, using a large dictionary prepared from the terms of economics, related tweets are extracted and sentiments corresponded to tweets are analyzed with the help of the RoBERTa language-based model, which has high accuracy and good performance. Finally, the frequency chart of tweets related to the economy and their sentiment scores (positive and negative tweets) is plotted over time for the entire world and the top 10 economies. From the analysis of the charts, we learn that the reason for publishing economic tweets is not only the increase in the number of people infected with the Coronavirus but also imposed restrictions and lockdowns in countries. The consequences of these restrictions include the loss of millions of jobs and the economic downturn.

Additional vignettes and associated images used in the survey (DOCX 490Â kb)