Users are usually required to share several types of data, including their personal data, as different providers strive to offer high-quality services that are often tailored to end-users’ preferences. However, when it comes to personalizing services, there are several challenges for meeting user’s needs and preferences. For content personalization and delivery of services to end users, services typically create user profiles. When user profiles are created, user data is collected and organized to meet the personalization requirements of the services. In this paper, we provide an overview of current research activities that focus on user profiling and ways to protect user data privacy. The paper presents different types of data that services collect from users on examples of commonly used Internet services. It proposes data categorization as a prerequisite for controlled data sharing between users and Internet services. Furthermore, it discusses how data generalization can be used for anonymization purposes on examples of the proposed data categories. Finally, it gives an overview of the privacy framework being developed and gives guidelines for future work focusing on data generalization methods in order to reduce user privacy risks.

The frequency with which fake news or misinformation is published on social networks is constantly increasing. Users of social networks are confronted with many different posts every day, often with sensationalist titles and content of dubious veracity. The problem is particularly common in times of sensitive social or political situations, such as epidemics of contagious diseases or elections. As such messages can have an impact on democratic processes or cause panic among the population, many countries and the European Commission itself have recently stepped up their activities to combat disinformation campaigns on social networks. Since previous research has shown that there are no tools available to combat disinformation in the Croatian language, we proposed a framework to detect potentially misinforming content in the comments on social media. The case study was conducted with real public comments published on Croatian Facebook pages. The initial results of this framework were encouraging as it can successfully classify and detect disinformation content.

In the field of natural language processing, language networks represent a method for observing linguistic units and their interactions in different linguistic contexts. This paper uses the previously presented Croatian language network for building a solution capable of generating spoken notifications in Croatian language. The novelty of this paper is that it proposes an approach for generating spoken notifications in smart environments by combining specialized services that enable interaction with the environment and human users. The process employed for generating spoken notifications is described in detail. Also, a novel contribution of this paper is the case-study evaluation of the proposed approach in a smart home environment.

Speech technologies have advanced significantly in the last decade, mostly due to rise in available computing power combined with novel approaches to natural language processing. As a result, speech-enabled systems have become popular commercial products, successfully integrated with various environments. However, this can be stated for English and a few other ?big? languages. From the perspective of a minority language, such as Croatian, there are many challenges ahead to achieve comparable results. In this paper, we propose a model for natural language generation and speech synthesis in a smart environment using Croatian language. The model is evaluated on 27 users to estimate the quality of user experience. The evaluation goal was to determine what users perceive to be more important ? generated speech quality or grammatical correctness of the spoken content. It is shown that most users perceived grammatically correct spoken texts as being of the highest quality.