Sentiment Analysis of Users’ Reactions on Social Media during the Pandemic

Abdukhamidov, Eldor; Juraev, Firuz; Abuhamad, Mohammed; El–Sappagh, Shaker; Abuhmed, Tamer

doi:10.3390/electronics11101648

Cited by 12 publications

(8 citation statements)

References 36 publications

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“…Text sentiment trends can influence user behavior in Q&A communities [ 30 ]. Sentiment analysis is a common text-processing operation that often uses the TextBlob library to return the sentiment property.…”

Section: Methodsmentioning

confidence: 99%

Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter

Yang,

Liu,

Wang

et al. 2023

J Med Internet Res

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Background The web-based health question-and-answer (Q&A) community has become the primary and handy way for people to access health information and knowledge directly. Objective The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. Methods Full-scale data on health consultations were acquired from the Metafilter Q&A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. Results A total of 18,099 post records were collected from a well-known Q&A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. Conclusions The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q&A communities.

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Section: Methodsmentioning

confidence: 99%

Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter

Yang,

Liu,

Wang

et al. 2023

J Med Internet Res

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“…Different platforms attract different demographics and have varying levels of user engagement. Researchers must choose platforms that are widely used by post-COVID patients in India to ensure a representative sample of data [5]. Popular platforms such as Twitter, Facebook, and Instagram may be considered for data collection due to their widespread usage and the ability to access public posts [4].…”

Section: IImentioning

confidence: 99%

Sentiment Analysis of Post-Covid Patients on Social Media by Predictive Modeling

S. Roja

2023

IJRITCC

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Social media has become a powerful platform for individuals to express their thoughts, experiences, and emotions. With the advent of the COVID-19 pandemic, social media has witnessed a surge in posts related to the virus and its aftermath. In India, a country severely hit by the pandemic, there is a prevalence of post-COVID patients sharing their experiences on social media. These posts provide valuable insights into the physical and emotional challenges faced by individuals after recovering from COVID-19. This paper aims to explore the prevalence of post-COVID patients on social media in India, the signs and symptoms experienced by these individuals, the correlations between post-COVID sentiment and patient characteristics, and the development of a predictive model for sentiment analysis. The prevalence of post-COVID patients on social media in India is evident through the increased number of posts related to COVID-19 and its aftermath. The surge in COVID-19 tweets was followed by an increased number of posts expressing personal experiences with the virus, indicating a significant presence of post-COVID patients on social media. These posts provide a valuable resource for understanding the challenges faced by individuals after recovering from COVID-19. Post-COVID patients often experience a range of signs and symptoms that can have a significant impact on their physical and mental well-being. Fatigue, shortness of breath, brain fog, chest pain, headache, and other symptoms have been reported as potential long-term effects of COVID-19. Additionally, patients with post-COVID-19 depressive symptoms share psychopathological symptoms, indicating the presence of mental health challenges. These signs and symptoms highlight the need for a comprehensive understanding of the post-COVID experience and the emotional toll it can take on individuals. Predictive modeling plays a crucial role in analyzing sentiment expressed by post-COVID patients on social media. Predictive modeling is a mathematical process that aims to predict future events or outcomes by analyzing relevant historical data. By developing a predictive model for sentiment analysis, researchers can gain valuable insights into the emotional experiences of post-COVID patients and potentially identify patterns or trends. This modeling approach allows for a systematic analysis of the vast amount of data available on social media platforms.

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“…Since its identification by the WHO in late 2019, the USA alone has since seen more than 87 million confirmed cases and 1 million virus-related deaths 1. With millions of people forced out of public spaces, conversations surrounding the pandemic have taken place online, with a vast majority via social media networks 2 3. People in the Public Eye (PIPE) such as news anchors, politicians, athletes and entertainers have taken this opportunity to discuss diverse topics with their platform followers, including their own experiences and opinion about health issues3 such as COVID-19, COVID-19 vaccines and other public health measures.…”

Section: Introductionmentioning

confidence: 99%

“…With millions of people forced out of public spaces, conversations surrounding the pandemic have taken place online, with a vast majority via social media networks 2 3. People in the Public Eye (PIPE) such as news anchors, politicians, athletes and entertainers have taken this opportunity to discuss diverse topics with their platform followers, including their own experiences and opinion about health issues3 such as COVID-19, COVID-19 vaccines and other public health measures. These shared experiences and opinions by PIPE often give way to public discourse, with many reacting to polarising statements by liking, commenting and resharing.…”

Section: Introductionmentioning

confidence: 99%

Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis

White

Melton

Zareie

et al. 2023

BMJ Health Care Inform

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Objectives The COVID-19 pandemic has introduced new opportunities for health communication, including an increase in the public’s use of online outlets for health-related emotions. People have turned to social media networks to share sentiments related to the impacts of the COVID-19 pandemic. In this paper, we examine the role of social messaging shared by Persons in the Public Eye (ie, athletes, politicians, news personnel, etc) in determining overall public discourse direction. Methods We harvested approximately 13 million tweets ranging from 1 January 2020 to 1 March 2022. The sentiment was calculated for each tweet using a fine-tuned DistilRoBERTa model, which was used to compare COVID-19 vaccine-related Twitter posts (tweets) that co-occurred with mentions of People in the Public Eye. Results Our findings suggest the presence of consistent patterns of emotional content co-occurring with messaging shared by Persons in the Public Eye for the first 2 years of the COVID-19 pandemic influenced public opinion and largely stimulated online public discourse. Discussion We demonstrate that as the pandemic progressed, public sentiment shared on social networks was shaped by risk perceptions, political ideologies and health-protective behaviours shared by Persons in the Public Eye, often in a negative light. Conclusion We argue that further analysis of public response to various emotions shared by Persons in the Public Eye could provide insight into the role of social media shared sentiment in disease prevention, control and containment for COVID-19 and in response to future disease outbreaks.

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Sentiment Analysis of Users’ Reactions on Social Media during the Pandemic

Cited by 12 publications

References 36 publications

Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter

Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter

Sentiment Analysis of Post-Covid Patients on Social Media by Predictive Modeling

Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis

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