Due to the COVID-19 pandemic, vaccine development and community vaccination studies are carried out all over the world. At this stage, the opposition to the vaccine seen in the society or the lack of trust in the developed vaccine is an important factor hampering vaccination activities. In this study, aspect-base sentiment analysis was conducted for USA, UK, Canada, Turkey, France, Germany, Spain and Italy showing the approach of twitter users to vaccination and vaccine types during the COVID-19 period. Within the scope of this study, two datasets in English and Turkish were prepared with 928,402 different vaccine-focused tweets collected by country. In the classification of tweets, 4 different aspects (policy, health, media and other) and 4 different BERT models (mBERT-base, BioBERT, ClinicalBERT nad BERTurk) were used. 6 different COVID-19 vaccines with the highest frequency among the datasets were selected and sentiment analysis was made by using Twitter posts regarding these vaccines. To the best of our knowledge, this paper is the first attempt to understand people's views about vaccination and types of vaccines. With the experiments conducted, the results of the views of the people on vaccination and vaccine types were presented according to the countries. The success of the method proposed in this study in the F1 Score was between 84% and 88% in datasets divided by country, while the total accuracy value was 87%.

The studies dealing with the problem of predicting scientific impacts in the scientific world mostly focus on predicting citation count of papers (PCCP). However, in the literature, only a little bit of research has been conducted on estimating the future influence of scientists individually. Estimating the impact of scientists individually is a worthwhile task for the following scientific research and cooperatives. From this point of view, a new supervised link prediction method is proposed to predict the citation count of scientists (PCCS). Many PCCP studies employ document-based attributes, such as titles, abstracts, and keywords of papers; institutions of scientists; impact factors of publishers; etc. and they do not take advantage of any topological features of complex networks formed with citations among papers. However, citation networks include valuable features for PCCP and PCCS. Therefore, we formulate the problem of PCCS as a link prediction problem in directed, weighted, and temporal citation networks. The proposed approach predicts not only links but also its weights. Our supervised link prediction method is tested on two citation networks in Experiment 1. The results of Experiment 1 confirm that our method achieves promising performances when considering prediction links with its weights are addressed for the first time in terms of link prediction in directed, weighted, and temporal networks. In Experiment 2, the performance of the proposed link prediction metric and five well-known link prediction metrics are compared in terms of prediction new links in complex networks. The results of Experiment 2 demonstrate that the proposed link prediction metric outperforms all baseline link prediction metrics.