Search

With the increasing use of the internet and social media, it is obvious that cyberbullying has become a major problem. The most basic way for protection against the dangerous consequences of cyberbullying is to actively detect and control the contents containing cyberbullying. When we look at today's internet and social media statistics, it is impossible to detect cyberbullying contents only by human power. Effective cyberbullying detection methods are necessary in order to make social media a safe communication space. Current research efforts focus on using machine learning for detecting and eliminating cyberbullying. Although most of the studies have been conducted on English texts for the detection of cyberbullying, there are few studies in Turkish. Limited methods and algorithms were also used in studies conducted on the Turkish language. In addition, the scope and performance of the algorithms used to classify the texts containing cyberbullying is different, and this reveals the importance of using an appropriate algorithm. The aim of this study is to compare the performance of different machine learning algorithms in detecting Turkish messages containing cyberbullying. In this study, nineteen different classification algorithms were used to identify texts containing cyberbullying using Turkish natural language processing techniques. Precision, recall, accuracy and F1 score values were used to evaluate the performance of classifiers. It was determined that the Light Gradient Boosting Model (LGBM) algorithm showed the best performance with 90.788% accuracy and 90.949% F1 Score value.

It is well known that social media has revolutionized communication. Nowadays, citizens, companies, and public institutions actively use social media in order to express themselves better to the population they address. This active use is also carried out by the gendarmerie and police organizations to communicate with the public with the purpose of improving social relations. However, it has been seen that the posts by the gendarmerie and police organizations did not attract much attention from their target audience from time to time, and it has been discovered that there was not enough research in the literature on this issue. In this study, it was aimed to investigate the use of social media by the gendarmerie and police organizations operating in Turkey (Jandarma - Polis), Italy (Carabinieri - Polizia), France (Gendarmerie - Police) and Spain (Guardia Civil - Polic\'ia), and the extent to which they can be effective on the followers, by comparatively examining their activity on twitter. According to the obtained results, it was found that Jandarma (Turkey) has the highest power of influence in the twitter sample, and the findings were comparatively presented in the study.

Applying machine learning (ML) on multivariate time series data has growing popularity in many application domains, including in computer system management. For example, recent high performance computing (HPC) research proposes a variety of ML frameworks that use system telemetry data in the form of multivariate time series so as to detect performance variations, perform intelligent scheduling or node allocation, and improve system security. Common barriers for adoption for these ML frameworks include the lack of user trust and the difficulty of debugging. These barriers need to be overcome to enable the widespread adoption of ML frameworks in production systems. To address this challenge, this paper proposes a novel explainability technique for providing counterfactual explanations for supervised ML frameworks that use multivariate time series data. The proposed method outperforms state-of-the-art explainability methods on several different ML frameworks and data sets in metrics such as faithfulness and robustness. The paper also demonstrates how the proposed method can be used to debug ML frameworks and gain a better understanding of HPC system telemetry data.