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  1. Article ; Online: Knowledge Assessment of COVID-19 Symptoms

    Irene (Eirini) Kamenidou / Aikaterini Stavrianea / Spyridon Mamalis / Ifigeneia Mylona

    International Journal of Environmental Research and Public Health, Vol 17, Iss 6964, p

    Gender Differences and Communication Routes for the Generation Z Cohort

    2020  Volume 6964

    Abstract: This paper explores the generation Z (Gen Z) cohort’s self-assessed knowledge regarding the coronavirus disease 2019 (COVID-19) symptoms as well as their interest in acquiring information and learning more about the transmission and spread of the severe ... ...

    Abstract This paper explores the generation Z (Gen Z) cohort’s self-assessed knowledge regarding the coronavirus disease 2019 (COVID-19) symptoms as well as their interest in acquiring information and learning more about the transmission and spread of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2 virus) and the COVID-19 symptoms. Additionally, it investigates gender differences in self-assessed knowledge of COVID-19 symptoms. Field research employing a nonprobability sampling method with an online questionnaire resulted in collecting 762 valid questionnaires. Data analysis included descriptive statistics, factor and reliability analysis, and the independent sample t -test. Results reveal that overall symptom knowledge was assessed higher than the self-assessed knowledge of the 13 specific symptoms. No gender differences were detected regarding self-assessed knowledge of the following COVID-19 symptoms: cough, dyspnea, anorexia, productive cough with expectoration (phlegm), headache, and diarrhea. On the other hand, for self-assessed overall knowledge of COVID-19 symptoms, as well as self-assessed knowledge of COVID-19 symptoms related to fever and fatigue, myalgia (muscle pain), pharyngodynia, nausea–vomitus, hemoptysis, and abdominal pain, the t -tests conducted showed that there are statistical differences in knowledge assessment between male and female subjects. Based on the outcomes, the paper provides marketing communication practices targeting this young generation cohort to raise awareness so that Gen Z’ers may react effectively if these symptoms are observed and, thus, request medical assistance.
    Keywords generation Z cohort ; COVID-19 symptoms ; knowledge assessment ; gender differences ; marketing communication ; Medicine ; R
    Subject code 150 ; 310
    Language English
    Publishing date 2020-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic

    Dimitrios Amanatidis / Ifigeneia Mylona / Irene (Eirini) Kamenidou / Spyridon Mamalis / Aikaterini Stavrianea

    Applied Sciences, Vol 11, Iss 4281, p

    2021  Volume 4281

    Abstract: Instagram is perhaps the most rapidly gaining in popularity of photo and video sharing social networking applications. It has been widely adopted by both end-users and organizations, posting their personal experiences or expressing their opinion during ... ...

    Abstract Instagram is perhaps the most rapidly gaining in popularity of photo and video sharing social networking applications. It has been widely adopted by both end-users and organizations, posting their personal experiences or expressing their opinion during significant events and periods of crises, such as the ongoing COVID-19 pandemic and the search for effective vaccine treatment. We identify the three major companies involved in vaccine research and extract their Instagram posts, after vaccination has started, as well as users’ reception using respective hashtags, constructing the datasets. Statistical differences regarding the companies are initially presented, on textual, as well as visual features, i.e., image classification by transfer learning. Appropriate preprocessing of English language posts and content analysis is subsequently performed, by automatically annotating the posts as one of four intent classes, thus facilitating the training of nine classifiers for a potential application capable of predicting user’s intent. By designing and carrying out a controlled experiment we validate that the resulted algorithms’ accuracy ranking is significant, identifying the two best performing algorithms; this is further improved by ensemble techniques. Finally, polarity analysis on users’ posts, leveraging a convolutional neural network, reveals a rather neutral to negative sentiment, with highly polarized user posts’ distributions.
    Keywords Instagram ; COVID-19 ; communication ; social media ; machine learning ; intent classification ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 006
    Language English
    Publishing date 2021-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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