COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.

• Publikační typ
• časopisecké články MeSH

• MeSH
• celosvětové zdraví MeSH
• mezinárodní agentury MeSH
• mezinárodní spolupráce MeSH
• poskytování zdravotní péče MeSH
• referenční standardy MeSH
• řízení zdravotnictví MeSH
• zajištění kvality zdravotní péče MeSH

• Konspekt
• Veřejné zdraví a hygiena
• NLK Obory
• veřejné zdravotnictví
• management, organizace a řízení zdravotnictví
• NLK Publikační typ
• brožury

Sentiment extraction and analysis using spoken utterances or written corpora as well as collection and analysis of human heart rate data using sensors are commonly used techniques and methods. On the other hand, these have been not combined yet. The collected data can be used e.g. to investigate the mutual dependence of human physical and emotional activity. The paper describes the procedure of parallel acquisition of heart rate sensor data and tweets expressing sentiment and difficulties related to this procedure. The obtained datasets are described in detail and further discussed to provide as much information as possible for subsequent analyses and conclusions. Analyses and conclusions are not included in this paper. The presented experiment and provided datasets serve as the first basis for further studies where all four presented data sources can be used independently, combined in a reasonable way or used all together. For instance, when the data is used all together, performing studies comparing human sensor data, acquired noninvasively from the surface of the human body and considered as more objective, and human written data expressing the sentiment, which is at least partly cognitively interpreted and thus considered as more subjective, could be beneficial.

• Publikační typ
• časopisecké články MeSH

Online social networks have become an everyday aspect of many people's lives. Users spend more and more time on these platforms and, through their interactions on social media platforms, they create active and passive digital footprints. These data have a strong potential in many research areas; indeed, understanding people's communication on social media is essential for understanding their attitudes, experiences, behaviors and values. Researchers have found that the use of social networking sites impacts eating behavior; thus, analyzing social network data is important for understanding the meaning behind expressions used in the context of healthy food. This study performed a communication analysis of data from the social network Twitter, which included 666,178 messages posted by 168,134 individual users. These data comprised all tweets that used the #healthyfood hashtag between 2019 and 2020 on Twitter. The results revealed that users most commonly associate healthy food with a healthy lifestyle, diet, and fitness. Foods associated with this hashtag were vegan, homemade, and organic. Given that people change their behavior according to other people's behavior on social networks, these data could be used to identify current and future associations with current and future perceptions of healthy food characteristics.

• MeSH
• biopotraviny MeSH
• komunikace MeSH
• lidé MeSH
• sociální média * MeSH
• sociální sítě MeSH
• vegani MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Addressing the escalating prevalence of burnout syndrome, which affects individuals across various professions and domains, is becoming increasingly imperative due to its profound impact on personal and professional aspects of employees' lives. This paper explores the intersection of burnout syndrome and human resource management, recognizing employees as the primary assets of organizations. It emphasizes the growing importance of nurturing employee well-being, care, and work-life balance from a human resource management standpoint. Employing social media analysis, this study delves into Twitter-based discourse on burnout syndrome, categorizing communication into three distinct dimensions: individual, organizational, and environmental. This innovative approach provides fresh insights into interpreting burnout syndrome discourse through big data analysis within social network analysis. The methodology deployed in this study was predicated upon the enhanced Social Media Analysis based on Hashtag Research framework and frequency, topic and visual analysis were conducted. The investigation encompasses Twitter communication from January 1st, 2019, to July 31st, 2022, comprising a dataset of 190,770 tweets. Notably, the study identifies the most frequently used hashtags related to burnout syndrome, with #stress and #mentalhealth leading the discussion, followed closely by #selfcare, #wellbeing, and #healthcare. Moreover, a comprehensive analysis unveils seven predominant topics within the discourse on burnout syndrome: organization, healthcare, communication, stress and therapy, time, symptoms, and leadership. This study underscores the evolving landscape of burnout syndrome communication and its multifaceted implications for individuals, organizations, and the broader environment, shedding light on the pressing need for proactive interventions. In organizations at all levels of management, the concept of burnout should be included in the value philosophy of organizations and should focus on organizational aspects, working hours and work-life balance for a healthier working environment and well-being of employees at all levels of management.

• Publikační typ
• časopisecké články MeSH

This letter explores the potential of artificial intelligence models, specifically ChatGPT, for content analysis, namely for categorizing social media posts. The primary focus is on Twitter posts with the hashtag #plasticsurgery. Through integrating Python with the OpenAI API, the study provides a designed prompt to categorize tweet content. Looking forward, the utilization of AI in content analysis presents promising opportunities for advancing understanding of complex social phenomena.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine Ratings, please refer to Table of Contents or online Instructions to Authors http://www.springer.com/00266 .

• MeSH
• lidé MeSH
• sociální média * MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• dopisy MeSH