ABSTRACT
Using user-generated content (UGC) on Twitter, the present study identifies the main themes that revolve around the concept of healthy diet and determine user feelings about various foods. Using a dataset of tweets with the hashtag "#Diet" or "#FoodDiet" (n = 10.591), we first use a Latent Dirichlet Allocation (LDA) model to identify the food categories most discussed on Twitter. Then, based on the results of the LDA model, we apply sentiment analysis to divide the identified tweets into three groups (negative, positive and neutral) based on the feelings expressed in corresponding tweets. Finally, the text mining approach is performed to identify foods according to the feelings expressed about those in corresponding tweets, as well as to derive key indicators that collectively present the UGC-based knowledge of healthy eating. The results of the present study show that among the foods most negatively perceived in the UGC are bacon, sugar, processed foods, red meat, and snacks. By contrast, water, apples, salads, broccoli and spinach are evaluated more positively. Furthermore, our findings suggest that the collective UGC knowledge is lacking on such healthy foods as fish, poultry, dry beans, nuts, as well as yogurt and cheese. The results of the present study can help the World Health Organization (WHO), as well as other institutions concerned with the study of healthy eating, to improve their communication policies on healthy products and preparation of balanced diets.
ABSTRACT
The #MeToo movement is among the most impressive social movements of recent years that have attracted stakeholders' attention and changed social mindsets. The present study seeks to provide a deeper understanding of the challenges involved in the #MeToo movement by identifying the main issues regarding business and marketing activities. To this end, the analysis of user-generated content (UGC) on Twitter was performed to extract the tweets with the hashtag "#MeToo" (31,305 tweets). Then, a Latent Dirichlet Allocation (LDA) model was applied to this database to identify topics. In the next step, using a Supervised Vector Machine (SVM) type analysis, we classified the tweets according to the sentiment they express (positive, negative, and neutral). Finally, we performed data text mining using the NVivo software. Our findings underscore the importance of (i) gender equality in communication campaigns, (ii) gender equality at work and (iii) social mobilizations in social networks, as well as suggest that (iv) marketing advertisers should become more inclusive and respectful in their advertising and marketing campaigns. The identified topics may be a starting point for future research on social movements, sociology, sexuality, or machismo in work environment, business and marketing strategies.
ABSTRACT
Due to technology development related to agricultural production, aircrafts such as the Unmanned Aerial Vehicle (UAV) and technologies such as Multispectral photogrammetry and Remote Sensing, have great potential in supporting some of the pressing problems faced by agricultural production in terms of analysis and testing of variables. This paper reports an experience related to the analysis of a vineyard with multispectral photogrammetry technology and UAVs and it demonstrates its great potential to analyze the Normalized Difference Vegetation Index (NDVI), the Near-Infrared Spectroscopy (NIRS) and the Digital Elevation Model (DEM) applied in the agriculture framework to collect information on the vegetative state of the crop, soil and plant moisture, and biomass density maps of. In addition, the collected information is analyzed with the PIX4D Cloud Computing technology software and its advantages over software that work with other data processing are highlighted. This research shows, therefore, the possibility that efficient plantations can be developed with the use of multispectral photogrammetry and the analysis of digital images from this process.
ABSTRACT
In the last several decades, electronic word of mouth (eWOM) has been widely used by consumers on different digital platforms to gather feedback about products and services from previous customer behavior. However, this useful information is getting blurred by fake reviews-i.e., reviews that were created artificially and are thus not representative of real customer opinions. The present study aims to thoroughly investigate the phenomenon of fake online reviews in the tourism sector on social networking and online reviews sites. To this end, we conducted a systematic review of the literature on fake reviews for tourism businesses. Our focus was on previous studies that addressed the following two main topics: (i) tourism (ii) fake reviews. Scientific databases were used to collect relevant literature. The search terms "tourism" and "fake reviews" were applied. The database of Web of Science produced a total of 124 articles and, after the application of different filters following the PRISMA 2009 Flow diagram, the process resulted in the selection of 17 studies. Our results demonstrate that (i) the analysis of fake reviews is interdisciplinary, ranging from Computer Science to Business and Management, (ii) the methods are based on algorithms and sentiment analysis, while other methodologies are rarely used; and (iii) the current and future state of fraudulent detection is based on emotional approaches, semantic analysis and new technologies such as Blockchain. This study also provides helpful strategies to counteract the ubiquity of fake reviews for tourism businesses.
ABSTRACT
The main objective of this exploratory study is to identify the social, economic, environmental and cultural factors related to the sustainable care of both environment and public health that most concern Twitter users. With 336 million active users as of 2018, Twitter is a social network that is increasingly used in research to get information and to understand public opinion as exemplified by Twitter users. In order to identify the factors related to the sustainable care of environment and public health, we have downloaded n = 5873 tweets that used the hashtag #WorldEnvironmentDay on the respective day. As the next step, sentiment analysis with an algorithm developed in Python and trained with data mining was applied to the sample of tweets to group them according to the expressed feelings. Thereafter, a textual analysis was used to group the tweets according to the Sustainable Development Goals (SDGs), identifying the key factors about environment and public health that most concern Twitter users. To this end, we used the qualitative analysis software NVivo Pro 12. The results of the analysis enabled us to establish the key factors that most concern users about the environment and public health such as climate change, global warming, extreme weather, water pollution, deforestation, climate risks, acid rain or massive industrialization. The conclusions of the present study can be useful to companies and institutions that have initiatives related to the environment and they also facilitate decision-making regarding the environment in non-profit organizations. Our findings will also serve the United Nations that will thoroughly review the 17 SDGs at the High-level Political Forum in 2019.
