Hence, the results of the research revealed the basic characteristics of the traffic accidents that took place in Sakarya. The accidents in Adapazarı, Erenler, Serdivan ve Arifiye that is sub-province match up with the clusters to determine the characteristics of accident. Sub-provinces correspond with Clusters such as Adapazarı-Cluster 1, Serdivan-Cluster 3, Erenler-Cluster 4 and Arifiye-Cluster 2. Thus, it is expected that this research will help decision makers to prevent and reduce traffic accidents.

Crime, terrorism, and other illegal activities are increasingly taking place in cyberspace. Crime in the dark web is one of the most critical challenges confronting governments around the world. Dark web makes it difficult to detect criminals and track activities, as it provides anonymity due to special tools such as TOR. Therefore, it has evolved into a platform that includes many illegal activities such as pornography, weapon trafficking, drug trafficking, fake documents, and more specially terrorism as in the context of this paper. Dark web studies are critical for designing successful counter-terrorism strategies. The aim of this research is to conduct a critical analysis of the literature and to demonstrate research efforts in dark web studies related to terrorism. According to result of the study, the scientific studies related to terrorism activities have been minimally conducted and the scientific methods used in detecting and combating them in dark web should be varied. Advanced artificial intelligence, image processing and classification by using machine learning, natural language processing methods, hash value analysis, and sock puppet techniques can be used to detect and predict terrorist incidents on the dark web.

Social networks are social systems composed of individuals who interact each other directly or indirectly. An individual has various kinds of relationships in social life and she/he participates in different types of social network structures depending on these relationships. Each social interaction which is a component of the social life has its own internal dynamics and structures with different characteristics. It is necessary to know the characteristics of social networks in order to analyze the structural features of the relationships in the network. In this study, the variabilities in social network structures is examined by local centrality measures that are related to the immediate environment of the individual and by nonlocal centrality measures which define the individual's position in the network, depending on his/her general position among individuals with whom he/she has relationships. In this study, the measures of centralization of the actors in different types of networks, such as friendship network, politic discussion network and Facebook network, each of which is formed by the same members, are examined. Moreover, the specific features of networks have been determined to vary according to network types. Additionally, the claim which states that "Each network differs from other types of networks in terms of their characteristics" is supported by empirical evidence. In summary, individuals on a social network acts independently from their position on other networks, and therefore each type of network should be evaluated within its own internal dynamics.

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