Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media

Yin, Hui; Yang, Shuiqiao; Li, Jianxin

doi:10.1007/978-3-030-65390-3_46

Cited by 88 publications

(55 citation statements)

References 26 publications

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“…Emotional toll can be investigated quantitatively by sentiment analysis, which calculates emotional polarization of text from negative, through neutral, to positive. Three previous studies have attempted to quantify emotional toll using sentiment analysis of social media conversations on COVID-19 [22][23][24]. Counterintuitively, given pandemic subject matter, all three studies of sentiments from COVID-19 conversations on social media showed a higher proportion of positive rather than negative emotions.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource

Krawczyk¹,

Chełkowski²,

Laydon³

et al. 2021

J Med Internet Res

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Background Before 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. Objective Here 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. Methods We 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. Results We 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. Conclusions The 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.

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Section: Introductionmentioning

confidence: 99%

Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource

Krawczyk¹,

Chełkowski²,

Laydon³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Chen, et al [ 32 ] also applied topic modeling and opinion analysis to verify whether “Chinese Virus” and “COVID-19” are substitutable. Apart from these studies, several other studies also applied topic modeling to identify different trends in public opinion toward COVID-19 [ 6 , 18 , 22 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Related Workmentioning

confidence: 99%

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

Shah

Jeong

2021

IJERPH

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(1) Background: Physician rating websites (PRWs) are a rich resource of information where individuals learn other people response to various health problems. The current study aims to investigate and analyze the people top concerns and sentiment dynamics expressed in physician online reviews (PORs). (2) Methods: Text data were collected from four U.S.-based PRWs during the three time periods of 2018, 2019 and 2020. Based on the dynamic topic modeling, hot topics related to different aspects of healthcare were identified. Following the hybrid approach of aspect-based sentiment analysis, the social network of prevailing topics was also analyzed whether people expressed positive, neutral or negative sentiments in PORs. (3) Results: The study identified 30 dominant topics across three different stages which lead toward four key findings. First, topics discussed in Stage III were quite different from the earlier two stages due to the COVID-19 outbreak. Second, based on the keyword co-occurrence analysis, the most prevalent keywords in all three stages were related to the treatment, questions asked by patients, communication problem, patients’ feelings toward the hospital environment, disease symptoms, time spend with patients and different issues related to the COVID-19 (i.e., pneumonia, death, spread and cases). Third, topics related to the provider service quality, hospital servicescape and treatment cost were the most dominant topics in Stages I and II, while the quality of online information regarding COVID-19 and government countermeasures were the most dominant topics in Stage III. Fourth, when zooming into the topic-based sentiments analysis, hot topics in Stage I were mostly positive (joy be the dominant emotion), then negative (disgust be the dominant emotion) in Stage II. Furthermore, sentiments in the initial period of Stage III (COVID-19) were negative (anger be the dominant emotion), then transformed into positive (trust be the dominant emotion) later. The findings also revealed that the proposed method outperformed the conventional machine learning models in analyzing topic and sentiment dynamics expressed in PRWs. (4) Conclusions: Methodologically, this research demonstrates the ability and importance of computational techniques for analyzing large corpora of text and complementing conventional social science approaches.

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“…The field of research in crises began to focus on all the information produced in times of crisis, not only determining the type of crisis, but also the results related to it. Therefore, the large amount of social media posts are considered a useful data source to recognize people's mental health during a period of the crisis such as COVID-19 by analyzing the popular topics and their associated sentiments due to the crisis [5]. All research in the field of crises has the main goal of knowing the type of crisis, reaching the injured, and knowing all the required needs.…”

Section: Related Workmentioning

confidence: 99%

Question Answering techniques for detecting needs of people in crisis

Karam

Hussein

Gharib

2021

IJICIS

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The use of social networks has become one of the basics of daily life to follow the news, and also in the case of inquiries and requesting assistance. In case of a crisis, information about the event begins to spread on social networks with the aim of raising awareness and knowing all the important instructions and requesting assistance. This information can be used to respond to people with the required needs depending on the type of assistance or inquiry requested. In this paper, an approach was proposed that uses Question Answering techniques based on natural language techniques and neural networks to extract the needs of affected people during a crisis and provide the proper response. The approach was tested on twitter data from different types of crises. The results showed that the suggested approach can answer with suitable guidelines with a precision of 0.81, a recall of 0.76 and an f-score of 0.78.

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Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media

Cited by 88 publications

References 26 publications

Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource

Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

Question Answering techniques for detecting needs of people in crisis

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