In recent years, wineries have incorporated social media into their marketing strategies to promote their products and services. They offer wineries the opportunity to interact with their customers in real time, allowing them to share their experiences, preferences, and feelings, and create a sense of community. The objective of this paper is to investigate digital presence and consumer engagement and reactions in social media used by the Greek winery industry, using a three-step methodology. The study focuses specifically on Facebook and Instagram and compares data for the period between 2019 and 2022, including the COVID-19 outbreak, collected from the profiles of 311 Greek wineries using a social media analytics tool. The contribution of this work lies in demonstrating the change in consumers’ engagement and reactions witnessed on wineries’ profiles. The outcomes indicate consistently limited interactions on these profiles, reflecting low levels of consumer engagement and overall reactions across social media platforms. These findings underscore the necessity for additional research into wineries’ marketing strategies and the motivations driving user engagement. The proposed methodology can be used as a social media brand engagement approach that aids brands in attracting audience attention and fostering active participation in various business sectors.

The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis (SA) as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of this study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, the results highlight the necessity of developing specialized lexicons tailored not only to a specific language but also to a particular field, such as a specific type of restaurant or shop.

Nowadays, the exploitation of augmented reality in education is linked to a multitude of pedagogical subjects, including history, geography, natural sciences and arts. The extensive use of computers in the educational process in recent years, in addition to learners’ familiarity with the use of Internet and applications associated with its operation, have led to a search for creative methods, which will enhance interaction in the pedagogical approach. As a result, a digital learning environment has emerged in which trainees gain access to knowledge in an entertaining and interactive way by making use of innovations and gaining the ability to control their knowledge. The purpose of this paper is to feature the role of new technologies in the educational process by studying the use of augmented reality applications in teaching history. Designing such applications requires direct involvement of the teacher and, as established by the case study, the benefits are extremely important; students acquire a particular interest in the learning object and achieve higher performance, as compared to conventional forms of teaching.

Social Networking Websites are not only the most widely used tools to facilitate communication in today's digital age, but they are also one of the biggest sources of Big Data. There are of course many benefits of data applications, for both ordinary users and professionals alike, but also there are many risks, that users of Social Networking Sites can face. As users' interest in using Social Networking Websites grows, so does their concern about the risks they pose. The purpose of this thesis is to investigate the risks posed by the use of Social Networking Websites to adult users in Greece and examine the behaviors of users that may lead to risk exposure, (behaviors such as Risk Taking, Privacy Behavior, Trust in SNS companies, Privacy Concern, Perceived Control of Information, Information Identity Disclosure will be examined).It will also examine the correlation of user behaviors and exposure to risk and socio-demographic data of users. The results of the present study show that exposure to Risk in SNS is related to gender and also “Privacy Behavior” and “Information Identity Disclosure” are related to Age and Educational Level

The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of the study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, there is a significant need for the creation of industry-specific lexical resources that provide decision-makers with valuable insights.