This study aims to establish actionable guidelines and provide strategic insights as a means of increasing the social media effectiveness of consumer brands. Post-related factors in addition to the contextual and temporal factors influencing consumer engagement (i.e., reposting, commenting on or liking posts), as an indicator of social media effectiveness, are considered in detail in the research model. Moreover, the model considers differences between industries as well as social media platforms. A total of 1130 posts made by four brands, two each from the durable goods and fast-moving consumer goods sectors, were collected from Facebook and Twitter in Turkey. Through predictive analysis, four different machine learning algorithms were utilized to develop easy-to-apply plans of action and strategies. The findings highlight the significant impact of videos, images, post frequency and interactivity on engagement. Furthermore, social media platforms and the brands themselves were found to be instrumental in influencing engagement levels, indicating that more than one formula is needed for effective social media management. The range and depth of the post-related factors (e.g., image type, video length, kind of interactivity) considered go far beyond those found in the significant majority of similar studies. Moreover, the unique setting and the novel data analysis algorithms applied set this study apart from similar ones.

The emergence of big data made it possible to make better predictions and discover hidden patterns which contain a load of useful information. Like other domains, health discipline is also enjoying this new data science era. In this study, we suggest some big data sources for health sector, draw a big data framework in health, and we present a machine learning pre-diagnosis model for emergency departments. The system predicts the diagnosis with a minimum accuracy of 75.5%. Patients’ verbal complaints about their own situations are used for machine learning. Two different models, Probabilistic Neural Network based on the Dynamic Decay Adjustment and Random Forest Decision Tree, have been used for machine learning after a series of text mining processes. Although there are other studies to predict diagnosis, this study is probably the first one using patients’ natural verbal complaints as user generated data. Both models’ accuracy and precision statistics suggest that they can be used as a decision support system to direct emergency department patients to appropriate healthcare centers. The system may also be developed into a triage prediction model at emergency departments.

In a regular retail shop the behavior of customers may yield a lot to the shop assistant. However, when it comes to online shopping it is not possible to see and analyze customer behavior such as facial mimics, products they check or touch etc. In this case, clickstreams or the mouse movements of e-customers may provide some hints about their buying behavior. In this study, we have presented a model to analyze clickstreams of e-customers and extract information and make predictions about their shopping behavior on a digital market place. After collecting data from an e-commerce market in Turkey, we performed a data mining application and extracted online customers' behavior patterns about buying or not. The model we present predicts whether customers will or will not buy their items added to shopping baskets on a digital market place. For the analysis, decision tree and multi-layer neural network prediction data mining models have been used. Findings have been discussed in the conclusion.