ABSTRACT
The spread of rumors has often been linked to major social and political impacts with consequences that oftentimes may prove to be severe. While there are multiple factors that could make a rumor more believable, this paper focuses on investigating the effects of personality traits on believing or disbelieving rumors. Participants were given a survey which included rumors relating to a single topic, COVID-19, to avoid topic-bias. Participants were also given a personality test which assessed the participants’ traits based on the Big 5 Model and categorized them as high or low. The effect of valence (pleasure) and arousal (excitement) on believing or disbelieving rumors was also explored, along with how this effect differs from one trait to another. The results showed that people with high agreeableness tend to believe rumors more than people with low agreeableness and that there was a correlation between valence and believing rumors for people with high neuroticism and people with low agreeableness. No correlation was found between arousal and believing rumors for any of the personality traits. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Rumors about the COVID-19 vaccines are spreading rapidly on social media platforms, questioning their intentions and efficiency. Currently, chatbots are used to combat the risk of misinformation amplification during the pandemic. They provide users with information from trusted and reliable sources. However, most of the current COVID-19 chatbots are non-personalized and do not focus on the vaccination process, rather they focus on answering general questions and performing symptom checking. In this paper, an empathetic chatbot named “Vaxera” was developed to provide users with accurate and up-to-date information about COVID-19 and its vaccines specifically. Vaxera provides users with information regarding COVID-19 frequently asked questions, advice and precautions, available vaccines, rumors and myths, and travel regulations. Additionally, it clears the circulating misconceptions about the vaccines and motivates the users on social media platforms to get vaccinated in a friendly manner. It tries to build a bond with the users through empathy and humor, so users will not feel forced. The results showed positive feedback from the participants who found the chatbot friendly and informative, as it corrected multiple rumors they believed. Moreover, a significant increase in the participants’ intentions to get vaccinated was observed after interacting with the chatbot. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
BACKGROUND: Especially in the current crisis of the COVID-19 pandemic and the lockdown it entailed, technology became crucial. Machines need to be able to interpret and represent human behavior, to improve human interaction with technology. This holds for all domains but even more so for the domain of student behavior in relation to education and psychological well-being. METHODS: This work presents the theoretical framework of a psychologically driven computing ontology, CCOnto, describing situation-based human behavior in relation to psychological states and traits. In this manuscript, we use and apply CCOnto as a theoretical and formal description system to categorize psychological factors that influence student behavior during the COVID-19 situation. By doing so, we show the added value of ontologies, i.e., their ability to automatically organize information from unstructured human data by identifying and categorizing relevant psychological concepts. RESULTS: The already existing CCOnto was modified to automatically categorize university students' state and trait markers related to different aspects of student behavior, including learning, worrying, health, and socially based on psychological theorizing and psychological data conceptualization. DISCUSSION: The paper discusses the potential advantages of using ontologies for describing and modeling psychological research questions. The handling of dataset completion, unification, and its explanation by means of Artificial Intelligence and Machine Learning models is also discussed.
ABSTRACT
BACKGROUND: The WHO has raised concerns about the psychological consequences of the current COVID-19 pandemic, negatively affecting health across societies, cultures and age-groups. METHODS: This online survey study investigated mental health, subjective experience, and behaviour (health, learning/teaching) among university students studying in Egypt or Germany shortly after the first pandemic lockdown in May 2020. Psychological assessment included stable personality traits, self-concept and state-like psychological variables related to (a) mental health (depression, anxiety), (b) pandemic threat perception (feelings during the pandemic, perceived difficulties in describing, identifying, expressing emotions), (c) health (e.g., worries about health, bodily symptoms) and behaviour including perceived difficulties in learning. Assessment methods comprised self-report questions, standardized psychological scales, psychological questionnaires, and linguistic self-report measures. Data analysis comprised descriptive analysis of mental health, linguistic analysis of self-concept, personality and feelings, as well as correlational analysis and machine learning. N = 220 (107 women, 112 men, 1 = other) studying in Egypt or Germany provided answers to all psychological questionnaires and survey items. RESULTS: Mean state and trait anxiety scores were significantly above the cut off scores that distinguish between high versus low anxious subjects. Depressive symptoms were reported by 51.82% of the student sample, the mean score was significantly above the screening cut off score for risk of depression. Worries about health (mental and physical health) and perceived difficulties in identifying feelings, and difficulties in learning behaviour relative to before the pandemic were also significant. No negative self-concept was found in the linguistic descriptions of the participants, whereas linguistic descriptions of feelings during the pandemic revealed a negativity bias in emotion perception. Machine learning (exploratory) predicted personality from the self-report data suggesting relations between personality and subjective experience that were not captured by descriptive or correlative data analytics alone. CONCLUSION: Despite small sample sizes, this multimethod survey provides important insight into mental health of university students studying in Egypt or Germany and how they perceived the first COVID-19 pandemic lockdown in May 2020. The results should be continued with larger samples to help develop psychological interventions that support university students across countries and cultures to stay psychologically resilient during the pandemic.