Emotion Bubbles: Emotional Composition of Online Discourse Before and After the COVID-19 Outbreak

Zhunis, Assem; Lima, Gabriel; Song, Hyoung Woon; Han, Jiyoung; Cha, Meeyoung

doi:10.1145/3485447.3512132

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…Research has been conducted to identify patterns of COVID-19 discourse in diverse data sources, communities, and locations. While some studies analyzed academic papers [ 22 ] and news articles [ 13 , 23 ], most research focused on social media platforms like Reddit [ 14 , 23 ], Facebook [ 17 ], and Twitter [ 15 - 17 , 24 ]. Some studies investigated particular languages, such as Chinese [ 25 ], French [ 25 ], Portuguese [ 26 ], and German [ 27 ], as well as specific locations, such as North America [ 16 ] and Asia [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

et al. 2023

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Background 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 to people’s needs during a global health emergency. Objective This study examined the COVID-19 pandemic–related topics online users discussed with a commercially available social chatbot and compared the sentiment expressed by users from 5 culturally different countries. Methods We analyzed 19,782 conversation utterances related to COVID-19 covering 5 countries (the United States, the United Kingdom, Canada, Malaysia, and the Philippines) between 2020 and 2021, from SimSimi, one of the world’s largest open-domain social chatbots. We identified chat topics using natural language processing methods and analyzed their emotional sentiments. Additionally, we compared the topic and sentiment variations in the COVID-19–related chats across countries. Results Our analysis identified 18 emerging topics, which could be categorized into the following 5 overarching themes: “Questions on COVID-19 asked to the chatbot” (30.6%), “Preventive behaviors” (25.3%), “Outbreak of COVID-19” (16.4%), “Physical and psychological impact of COVID-19” (16.0%), and “People and life in the pandemic” (11.7%). Our data indicated that people considered chatbots as a source of information about the pandemic, for example, by asking health-related questions. Users turned to SimSimi for conversation and emotional messages when offline social interactions became limited during the lockdown period. Users were more likely to express negative sentiments when conversing about topics related to masks, lockdowns, case counts, and their worries about the pandemic. In contrast, small talk with the chatbot was largely accompanied by positive sentiment. We also found cultural differences, with negative words being used more often by users in the United States than by those in Asia when talking about COVID-19. Conclusions Based on the analysis of user-chatbot interactions on a live platform, this work provides insights into people’s informational and emotional needs during a global health crisis. Users sought health-related information and shared emotional messages with the chatbot, indicating the potential use of chatbots to provide accurate health information and emotional support. Future research can look into different support strategies that align with the direction of public health policy.

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Section: Introductionmentioning

confidence: 99%

User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

et al. 2023

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“…The advanced BERTopic algorithm has been applied in various fields such as user feedback, employee surveys, speech perception, social media, IT service management, electronic health records, and more. It is also increasingly utilized in academic research, such as in the assessment of cancer health disparities [65], mining citizen emotions under sudden public health events [66], and evaluating impressions of tourist destinations [67]. In previous studies, there has been little integration of the BERTopic algorithm into the field of government affairs.…”

Section: Plos Onementioning

confidence: 99%

Analyzing public demands on China’s online government inquiry platform: A BERTopic-Based topic modeling study

Tang,

Pan,

2024

PLoS ONE

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This study aims to enhance governmental decision-making by leveraging advanced topic modeling algorithms to analyze public letters on the "People Call Me" online government inquiry platform in Zhejiang Province, China. Employing advanced web scraping techniques, we collected publicly available letter data from Hangzhou City between June 2022 and May 2023. Initial descriptive statistical analyses and text mining were conducted, followed by topic modeling using the BERTopic algorithm. Our findings indicate that public demands are chiefly focused on livelihood security and rights protection, and these demands exhibit a diversity of characteristics. Furthermore, the public’s response to significant emergency events demonstrates both sensitivity and deep concern, underlining its pivotal role in government emergency management. This research not only provides a comprehensive landscape of public demands but also validates the efficacy of the BERTopic algorithm for extracting such demands, thereby offering valuable insights to bolster the government’s agility and resilience in emergency responses, enhance public services, and modernize social governance.

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“…During the COVID-19 pandemic, online communities, particularly on platforms such as Reddit, have displayed heightened emotionality in their user-generated content [15]. Research has shown that significant global events, such as a pandemic, can substantially influence the emotional content of social postings [16]. Reddit communities, for example, have experienced a range of emotions, including joy, trust, fear, surprise, sadness, disgust, anger, and anticipation [17].…”

Section: Introductionmentioning

confidence: 99%

Influence of Media Information Sources on Vaccine Uptake: The Full and Inconsistent Mediating Role of Vaccine Hesitancy

Recio-Román,

Recio-Menéndez,

Román-González

2023

Computation

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Vaccine hesitancy is a significant public health concern, with numerous studies demonstrating its negative impact on immunization rates. One factor that can influence vaccine hesitancy is media coverage of vaccination. The media is a significant source of immunization information and can significantly shape people’s attitudes and behaviors toward vaccine uptake. Media influences vaccination positively or negatively. Accurate coverage of the benefits and effectiveness of vaccination can encourage uptake, while coverage of safety concerns or misinformation may increase hesitancy. Our study investigated whether vaccine hesitancy acts as a mediator between information sources and vaccination uptake. We analyzed a cross-sectional online survey by the European Commission of 27,524 citizens from all EU member states between 15 and 29 March 2019. The study used structural equation modeling to conduct a mediation analysis, revealing that the influence of media on vaccine uptake is fully mediated by vaccine hesitancy, except for television, which depicted an inconsistent mediating role. In other words, the effect of different media on vaccine uptake is largely driven by the extent to which individuals are hesitant or resistant to vaccinating. Therefore, media outlets, governments, and public health organizations must work together to promote accurate and reliable information about vaccination and address vaccine hesitancy.

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Emotion Bubbles: Emotional Composition of Online Discourse Before and After the COVID-19 Outbreak

Cited by 8 publications

References 40 publications

User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

Analyzing public demands on China’s online government inquiry platform: A BERTopic-Based topic modeling study

Influence of Media Information Sources on Vaccine Uptake: The Full and Inconsistent Mediating Role of Vaccine Hesitancy

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