The study aimes to analyze the discourse of Arab tweeters regarding the normalization of Arab-Israeli relations on the social media site (Twitter).
Study Methodology: The study relay on the survey method, using the comprehensive survey method, and this research is one of the descriptive types that aim to describe the phenomenon in a qualitative way, and discuss it through the method of in-depth analysis, in addition to using the tools of (discourse analysis).
Study sample: A sample of (8686) tweets for (7425) tweeters, which was posted on Twitter during the period from 15/9 to 30/9/2020, on the issue of normalizing Arab-Israeli relations.
Study tools: The data study was collected using the data mining tool from social networks (NodeXL), along with a discourse analysis sheet.
The most prominent results of the study:
The thesis (popular rejection of normalization with Israel) rank first for the theses addressed in the tweets. The negative features attributed to the active forces in the Arab countries focused on their official position in support and defender of normalization. As for the positive features, the role and popular position rejecting normalization were shared. The frameworks of the case focused on the framework of Palestinian reconciliation, the framework of the US-Iranian conflict in the Middle East, and the framework of the conflict within the ruling regimes regarding normalization. While the personality framework focused on Palestinian martyrs, Arab presidents and rulers who support normalization, as well as clerics. The causes and consequences were varied in their content.
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Breast cancer has got much attention in the recent years as it is a one of the complex diseases that can threaten people lives. It can be determined from the levels of secreted proteins in the blood. In this project, we developed a method of finding a threshold to classify the probability of being affected by it in a population based on the levels of the related proteins in relatively small case-control samples. We applied our method to simulated and real data. The results showed that the method we used was accurate in estimating the probability of being diseased in both simulation and real data. Moreover, we were able to calculate the sensitivity and specificity under the null hypothesis of our research question of being diseased o
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