Dataset Information

COVID-19 news valence effects on emotion and its modulation by a relaxation: A randomized online experiment during COVID-19 pandemic.

ABSTRACT: This study evaluated how news with positive and negative content about COVID-19, as well as a relaxation pause, affect the emotional state. We also investigated the association between emotional state and practicing meditation/yoga, physical activity or having a mental disease. For that, a sample of 717 participants, recruited through social media, were randomly assigned to listen to negative or positive news about COVID-19. After that, both groups were guided through a short relaxation pause. Their emotional state was measured before they listened to the audios and after each audio. Mixed linear models were used to evaluate the effects of news group, relaxation pause, mental health and well-being practices. Negative news worsened their emotional state, whereas positive news improved it. A brief relaxation pause improved the effects of negative news content and may mitigate the effects of this valence of information. Practicing physical activity, meditation/yoga was associated with better emotional responses.

SUBMITTER: Bazan PR

PROVIDER: S-EPMC8529560 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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COVID-19 news valence effects on emotion and its modulation by a relaxation: A randomized online experiment during COVID-19 pandemic.

Bazán Paulo Rodrigo PR de Azevedo Neto Raymundo Machado RM Lacerda Shirley Silva SS Ribeiro Maurício Watanabe MW Balardin Joana Bisol JB Amaro Edson E Kozasa Elisa Harumi EH

Internet interventions 20211016

This study evaluated how news with positive and negative content about COVID-19, as well as a relaxation pause, affect the emotional state. We also investigated the association between emotional state and practicing meditation/yoga, physical activity or having a mental disease. For that, a sample of 717 participants, recruited through social media, were randomly assigned to listen to negative or positive news about COVID-19. After that, both groups were guided through a short relaxation pause. T ...[more]

PMID: 34697586

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Project description:BackgroundBefore the advent of an effective vaccine, nonpharmaceutical interventions, such as mask-wearing, social distancing, and lockdowns, have been the primary measures to combat the COVID-19 pandemic. Such measures are highly effective when there is high population-wide adherence, which requires information on current risks posed by the pandemic alongside a clear exposition of the rules and guidelines in place.ObjectiveHere we analyzed online news media coverage of COVID-19. We quantified the total volume of COVID-19 articles, their sentiment polarization, and leading subtopics to act as a reference to inform future communication strategies.MethodsWe collected 26 million news articles from the front pages of 172 major online news sources in 11 countries (available online at SciRide). Using topic detection, we identified COVID-19-related content to quantify the proportion of total coverage the pandemic received in 2020. The sentiment analysis tool Vader was employed to stratify the emotional polarity of COVID-19 reporting. Further topic detection and sentiment analysis was performed on COVID-19 coverage to reveal the leading themes in pandemic reporting and their respective emotional polarizations.ResultsWe found that COVID-19 coverage accounted for approximately 25.3% of all front-page online news articles between January and October 2020. Sentiment analysis of English-language sources revealed that overall COVID-19 coverage was not exclusively negatively polarized, suggesting wide heterogeneous reporting of the pandemic. Within this heterogenous coverage, 16% of COVID-19 news articles (or 4% of all English-language articles) can be classified as highly negatively polarized, citing issues such as death, fear, or crisis.ConclusionsThe goal of COVID-19 public health communication is to increase understanding of distancing rules and to maximize the impact of governmental policy. The extent to which the quantity and quality of information from different communication channels (eg, social media, government pages, and news) influence public understanding of public health measures remains to be established. Here we conclude that a quarter of all reporting in 2020 covered COVID-19, which is indicative of information overload. In this capacity, our data and analysis form a quantitative basis for informing health communication strategies along traditional news media channels to minimize the risks of COVID-19 while vaccination is rolled out.

Project description:Despite growing concerns that children (8-13 years old) tend to avoid the news, the reasons why have received little research attention. Therefore, the current study aims to develop and test a model conceptualizing the relations between children's news consumption, news avoidance, emotional responses (negative emotions and anxiety-related behaviors), and parent and child mitigation strategies. The model was tested using data collected during the first year of the COVID-19 pandemic. The current, preregistered, survey study was part of a longitudinal project and used data from the second wave. Data were collected in November/December 2020 among 510 children (M age = 10.40; 53.72% girls). Findings showed that children who consumed more news during the pandemic avoided pandemic news less often. Children who experienced more anxiety-related behaviors regarding pandemic news avoided pandemic news more often. The relation between news consumption and emotional responses was stronger for children who experienced restrictive parental mediation more often, indicating that this was not an effective parental mediation strategy for tempering their emotional responses. Children with higher levels of emotional responses used reactive coping strategies more often. However, this did not seem to be an effective strategy against pandemic news avoidance because none of the strategies had a negative relation with pandemic news avoidance. Distancing was even positively related to pandemic news avoidance. Although the current study was not able to fully unravel how news avoidance-related constructs relate to one another, we were able to get some important insights guiding future research. Specifically, it is of crucial importance to unravel the mechanisms that increase the chance of children's news avoidance and those that mitigate it, to build interventions to counteract news avoidance and to protect children from the negative emotional consequences by news consumption.

Project description:Emotion recognition is a crucial task in Natural Language Processing (NLP) that enables machines to comprehend the feelings conveyed in the text. The task involves detecting and recognizing various human emotions like anger, fear, joy, and sadness. The applications of emotion recognition are diverse, including mental health diagnosis, student support, and the detection of online suspicious behavior. Despite the substantial amount of literature available on emotion recognition in various languages, Arabic emotion recognition has received relatively little attention, leading to a scarcity of emotion-annotated corpora. This article presents the ArPanEmo dataset, a novel dataset for fine-grained emotion recognition of online posts in Arabic. The dataset comprises 11,128 online posts manually labeled for ten emotion categories or neutral, with Fleiss' kappa of 0.71. It is unique in that it focuses on the Saudi dialect and addresses topics related to the COVID-19 pandemic, making it the first and largest of its kind. Python's packages were utilized to collect online posts related to the COVID-19 pandemic from three sources: Twitter, YouTube, and online newspaper comments between March 2020 and March 2022. Upon collection of the online posts, each one underwent a semi-automatic classification process using a lexicon of emotion-related terms to determine whether it belonged to the neutral or emotion category. Subsequently, manual labeling was conducted to further categorize the emotional data into fine-grained emotion categories. We anticipate that the ArPanEmo dataset will enrich Arabic NLP resources and help in the development of machine learning and deep learning tools to identify emotions in a given text. It will also contribute to developing systems that monitor online suspicious behaviors or mental health disorders. The final dataset is formatted in CSV, consisting of three columns: the number of the post, the post's text, and the corresponding emotion label. This format facilitates incorporating and utilizing the dataset in any machine learning research.

Dataset Information

COVID-19 news valence effects on emotion and its modulation by a relaxation: A randomized online experiment during COVID-19 pandemic.

Publications

COVID-19 news valence effects on emotion and its modulation by a relaxation: A randomized online experiment during COVID-19 pandemic.

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