Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence DOI Creative Commons
Achini Adikari, Rashmika Nawaratne, Daswin De Silva

et al.

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 23(4), P. e27341 - e27341

Published: April 1, 2021

Background The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of life; ensuing social restrictions led to employment, lack interactions, and burgeoning psychological distress. As physical distancing regulations were introduced manage outbreaks, individuals, groups, communities engaged extensively on media express their thoughts emotions. internet-mediated communication self-reported information encapsulates emotional mental well-being all individuals impacted pandemic. Objective research aims investigate emotions related expressed over time, using an artificial intelligence (AI) framework. Methods Our study explores emotion classifications, intensities, transitions, profiles, as well alignment key themes topics, across four stages pandemic: declaration global crisis (ie, prepandemic), first lockdown, easing restrictions, second lockdown. employs AI framework comprised natural language processing, word embeddings, Markov models, growing self-organizing map algorithm, which are collectively used conversations. investigation carried out 73,000 Twitter conversations posted users in Australia from January September 2020. Results outcomes this enabled us analyze visualize different concerns that reflected during pandemic, could be gain insights into citizens’ health. First, topic analysis showed diverse common people had It noted personal-level escalated broader time. Second, intensity state transitions fear sadness more prominently at first; however, transitioned anger disgust Negative emotions, except for sadness, significantly higher (P<.05) showing increased frustration. Temporal conducted modeling changes demonstrated how emerged shifted Third, categorized where differences seen between lockdown profiles. Conclusions recorded general public. While established use discover informed time when impossible, also contribute toward postpandemic recovery understanding impact via changes, they potentially inform care decision making. exploited enhance our behaviors emergencies, lead improved planning policy making future crises.

Language: Английский

A deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine–Russia conflict DOI
Serpil Aslan

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 143, P. 110404 - 110404

Published: May 22, 2023

Language: Английский

Citations

37
User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis DOI Creative Commons
Hyojin Chin, Gabriel Lima, Mingi Shin

et al.

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e40922 - e40922

Published: Jan. 3, 2023

Chatbots have become a promising tool to support public health initiatives. Despite their potential, little research has examined how individuals interacted with chatbots during the COVID-19 pandemic. Understanding user-chatbot interactions is crucial for developing services that can respond people's needs global emergency.

Language: Английский

Citations

36
Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications DOI Creative Commons
Tala Mirzaei, Leila Amini, Pouyan Esmaeilzadeh

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: Sept. 9, 2024

Language: Английский

Citations

10
Exploring motivation, goals, facilitators, and barriers to adopt health behaviors at retirement age: a focus group study DOI Creative Commons
Paula Collazo‐Castiñeira, Rocío Rodríguez‐Rey, Gisela Isabel Delfino

et al.

BMC Public Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 28, 2025

This study qualitatively investigates retirement-age adults' perspectives on engaging in health behaviors such as physical activity or a healthy diet, distinguishing facilitators, barriers, goals, and motivations (the two later line with Self-Determination Theory). Two clinical psychologists conducted four focus groups Spanish adults around retirement age. We inductive deductive content analysis. The main facilitators barriers identified were the presence absence of social support/social network, mental health, willpower, time, motivation. Participants reported different types motivation (e.g., intrinsic enjoyment exercise cooking) goals (intrinsic extrinsic); except for goal management, which presented both motivation, participants regulated autonomously, extrinsic ones controlled A process internalizing source was inductively by participants. Facilitating networks addressing issues could aid engagement among this population. Additionally, management appeared significant goal, where autonomous can develop even if behavior initially arises from external triggers, medical advice.

Language: Английский

Citations

1
Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence DOI Creative Commons
Achini Adikari, Rashmika Nawaratne, Daswin De Silva

et al.

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 23(4), P. e27341 - e27341

Published: April 1, 2021

Background The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of life; ensuing social restrictions led to employment, lack interactions, and burgeoning psychological distress. As physical distancing regulations were introduced manage outbreaks, individuals, groups, communities engaged extensively on media express their thoughts emotions. internet-mediated communication self-reported information encapsulates emotional mental well-being all individuals impacted pandemic. Objective research aims investigate emotions related expressed over time, using an artificial intelligence (AI) framework. Methods Our study explores emotion classifications, intensities, transitions, profiles, as well alignment key themes topics, across four stages pandemic: declaration global crisis (ie, prepandemic), first lockdown, easing restrictions, second lockdown. employs AI framework comprised natural language processing, word embeddings, Markov models, growing self-organizing map algorithm, which are collectively used conversations. investigation carried out 73,000 Twitter conversations posted users in Australia from January September 2020. Results outcomes this enabled us analyze visualize different concerns that reflected during pandemic, could be gain insights into citizens’ health. First, topic analysis showed diverse common people had It noted personal-level escalated broader time. Second, intensity state transitions fear sadness more prominently at first; however, transitioned anger disgust Negative emotions, except for sadness, significantly higher (P<.05) showing increased frustration. Temporal conducted modeling changes demonstrated how emerged shifted Third, categorized where differences seen between lockdown profiles. Conclusions recorded general public. While established use discover informed time when impossible, also contribute toward postpandemic recovery understanding impact via changes, they potentially inform care decision making. exploited enhance our behaviors emergencies, lead improved planning policy making future crises.

Language: Английский

Citations

54