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The problem of understanding and predicting tourist behavior in choosing their destinations is a long-standing one. The first step in the process is to understand users' intention to visit a country, which may later translate into an actual visit. Would-be tourists may express their intention to visit a destination on social media. Being able to predict their intention may be useful for targeted promotion campaigns. In this paper, we propose an algorithm to predict visit (or revisit) intentions based on the texts in posts on social media. The algorithm relies on a neural network sentence-transformer architecture using optimized embedding and a logistic classifier. Employing two real labeled datasets from Twitter (now X) for training, the algorithm achieved 90% accuracy and balanced performances over the two classes (visit intention vs. no-visit intention). The algorithm was capable of predicting intentions to visit with high accuracy, even when fed with very imbalanced datasets, where the posts showing the intention to visit were an extremely small minority. [ABSTRACT FROM AUTHOR]

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Smart speakers equipped with intelligent voice processing functions, like Siri and Amazon Echo, have become widespread globally. However, their recording and networking capabilities spur concerns about users' privacy. In this research, we report the results of a text-mining analysis of customers' perception of privacy in smart speakers. The corpus comprises over 4,500 reviews of the Echo line of smart speakers collected from Amazon. Smart speakers owners appear to be mostly oblivious to privacy issues since the fraction of those mentioning privacy in their reviews is largely below 3%. The average sentiment towards privacy is positive, though the average hides a significant fraction exhibiting a negative sentiment. However, those negative perceptions do not affect the overall sentiment polarity towards the product, which stays positive. [ABSTRACT FROM AUTHOR]

Parallel to the increasing use of smart speakers (aka virtual assistants), privacy concerns emerge. In this article, we provide a systematic review of the literature on privacy issues in smart speakers. Both Scopus and Web of Science databases are examined, as well as the preprint repository arXiv, to take into account the most recent contributions. That literature exhibits a continuously growing trend in the last 4 years, with the USA being the single largest contributor to the field. The topics debated in the literature concern the perception of privacy issues, their contribution to adoption choices, the identification of vulnerabilities, the proposal of possible countermeasures, and (last and least) the analysis of the legal cases that are arising. [ABSTRACT FROM AUTHOR]