As the number of academic publications increase, preparing a literature review becomes more challenging. This paper introduces an automated literature review support system to ease the literature review process for academia with reference graphs, abstract and full document summaries, paper clusters by keywords, abstracts, and abstract summaries combined. The output of the proposed system may ease exploring the state-of-the-art research.

In the 21st century, the world has experienced devastating wars that have caused people to migrate, creating problems in host countries. Among these migration routes, maritime migration routes are more desirable compared to other routes because coasts cannot be controlled as strictly as the alternative passages. However, maritime migration poses life-threatening risks due to unsafe boats, transportation between undesignated areas, lack of life-saving equipment, and dangerous weather conditions chosen for covert operations. Refugees and migrants may die or go missing at sea during these migrations. Most refugees are unaware of high risks they are facing as they hopelessly set out in search of better living conditions. In this study, we propose that such suicide-like maritime migration activities can be detected to some extent through cell phone activities and there may be a way to track early signs of migrants coming together from other regions and migrating through sea routes. By collecting media reports of failed attempts by immigrants and linking them to the D4R cell phone data, we were able to gain some indications of the possibility of early warning systems through analysis of cell phone calls.

The development of e-commerce has created new shopping trends of customers. In online shopping environments, product reviews play a critical role in the choice of customers. Online reviews are additionally valuable for the manufacturers and the vendors by providing easily accessible feedback to them. In this study, a text analysis method is proposed to find the defective features of the products by detecting features with negative opinion tendency in the clustered customer reviews. The output of the proposed model, the extracted defects, may provide a strong source of guidance both for consumers in purchase decisions and for producers in product improvement.

Social media platforms are places where people post their feelings and thoughts about a topic. The institutions, organizations, individuals, or companies that are the subject of these ideas are affected by these posts. As discussed in different studies, companies in stock exchange markets are affected by the posts made on these social media platforms. At the same time, individuals who are aware of this fact, namely speculators and influencers, may make profit by manipulating the truth. In this study, possible speculators or influencers using the Twitter social media platform are investigated. As the target companies, Google, Amazon, Apple, Tesla, and Microsoft were chosen, which are among the largest companies on the NASDAQ stock exchange market. In the study, asentiment analysis using the Loughran and McDonald sentiment analysis dictionary was utilized. The sentiment analysis results were used to model different machine learning algorithms. With the models, individuals who had too many positive or negative effects as possible speculators or influencers were identified. The study was performed for 5 years of data. The results indicates that (1) without noise reduction, it is not possible to establish a correlation on individual tweets and their effects on the stock market; (2) it is not possible to establish a correlation between the number of tweets and the volume of companies; (3) the effect of threshold on the accuracy, which has been done and proven in different studies, has also been proven in this study; (4) RBF Kernel SVM method gives better result than other machine learning methods.

We present the project SpEnD, a complete SPARQL endpoint discovery and analysis portal. In a previous study, the SPARQL endpoint discovery and analysis steps of the SpEnD system were explained in detail. In the SpEnD portal, the SPARQL endpoints are extracted from the web by using web crawling techniques, monitored and analyzed by live querying the endpoints systematically. After many sustainability improvements in the SpEnD project, the SpEnD system is now online as a portal. SpEnD portal currently serves 1487 SPARQL endpoints, out of which 911 endpoints are uniquely found by SpEnD only when compared to the other existing SPARQL endpoint repositories. In this portal, the analytic results and the content information are shared for every SPARQL endpoint. The endpoints stored in the repository are monitored and updated continuously.