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.
Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte
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