Understand research hotspots surrounding COVID-19 and other coronavirus infections using topic modeling

Dong, Mengying; Cao, Xiaojun; Liang, Mingbiao; Li, Lijuan; Liu, Guangjian; Liang, Huiying

doi:10.1101/2020.03.26.20044164

Cited by 24 publications

(29 citation statements)

References 19 publications

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“…Analyses were conducted to identify patterns of health communications in diverse kinds of data sources, communities, and locations. Although some works investigated news articles [ 10 ] or research papers [ 11 ], most research focused on social media such as Reddit posts [ 12 ] and tweets [ 13 - 19 ]. Conversations in particular communities were examined, such as tweets posted by US governors and presidential cabinet members [ 13 ], and African American twitter communities [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Jang¹,

Rempel²,

Roth³

et al. 2021

J Med Internet Res

139

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Background Social media is a rich source where we can learn about people’s reactions to social issues. As COVID-19 has impacted people’s lives, it is essential to capture how people react to public health interventions and understand their concerns. Objective We aim to investigate people’s reactions and concerns about COVID-19 in North America, especially in Canada. Methods We analyzed COVID-19–related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people’s sentiment about COVID-19–related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. Results Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, “vaccines,” “economy,” and “masks”) and 60 opinion terms such as “infectious” (negative) and “professional” (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. Conclusions Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19–related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.

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Section: Introductionmentioning

confidence: 99%

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Jang¹,

Rempel²,

Roth³

et al. 2021

J Med Internet Res

139

View full text Add to dashboard Cite

show abstract

“…It is assumed that the issue relates to many regional and global strategic dimensions. Perhaps, it is essential to understand the evolution of scientific knowledge on coronaviruses including COVID-19 to assist in need-based and better-informed decision making [ 30 , 32 ]. More importantly, despite coronaviruses' significance and its magnitude of impact from epidemics to a pandemic, in the last many decades, literature is sparse on comprehensive coronaviruses picture over time in terms of relevant research performance and trends [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

A scientometric analysis on coronaviruses research (1900–2020): Time for a continuous, cooperative and global approach

Malik¹,

Butt²,

Bashir³

et al. 2021

Journal of Infection and Public Health

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“…The rapid rate of COVID-19 spread alerts the global community to respond promptly to medical and mental concerns to accelerate the action of disease control [ 39 ]. In this way, Dong et al [ 54 ], Stokes et al [ 55 ], and Liu et al [ 40 ] employed topic modeling techniques to detect public concerns and health communications.…”

Section: Methodsmentioning

confidence: 99%

Track Iran's national COVID-19 response committee’s major concerns using two-stage unsupervised topic modeling

Kaveh-Yazdy

Zarifzadeh

2021

International Journal of Medical Informatics

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Background Since the World Health Organization (WHO) declared the COVID-19 as a Public Health Emergency of International Concern (PHEIC) on January 31, 2020, governments have been enfaced with crisis for timely responses. The efficacy of these responses directly depends on the social behaviors of the target society. People react to these actions with respect to the information they received from different channels, such as news and social networks. Thus, analyzing news demonstrates a brief view of the information users received during the outbreak. Methods The raw data used in this study is collected from official news channels of news wires and agencies in Telegram messenger, which exceeds 2,400,000 posts. The posts that are quoted by NCRC’s members are collected, cleaned, and divided into sentences. The topic modeling and tracking are utilized in a two-stage framework, which is customized for this problem to separate miscellaneous sentences from those presenting concerns. The first stage is fed with embedding vectors of sentences where they are grouped by the Mapper algorithm. Sentences belonging to singleton nodes are labeled as miscellaneous sentences. The remained sentences are vectorized, adopting Tf-IDF weighting schema in the second stage and topically modeled by the LDA method. Finally, relevant topics are aligned to the list of policies and actions, named topic themes, that are set up by the NCRC. Results Our results show that major concerns presented in about half of the sentences are (1) PCR lab. test, diagnosis, and screening, (2) Closure of the education system, and (3) awareness actions about washing hands and facial mask usage. Among the eight themes, intra-provincial travel and traffic restrictions, as well as briefing the national and provincial status, are under-presented. The timeline of concerns annotated by the preventive actions illustrates the changes in concerns addressed by NCRC. This timeline shows that although the announcements and public responses are not lagged behind the events, but cannot be considered as timely. Furthermore, the fluctuating series of concerns reveal that the NCRC has not a long-time response map, and members react to the closest announced policy/act. Conclusion The results of our study can be used as a quantitative indicator for evaluating the availability of an on-time public response of Iran’s NCRC during the first three months of the outbreak. Moreover, it can be used in comparative studies to investigate the differences between awareness acts in various countries. Results of our customized-design framework showed that about one-third of the discussions of the NCRC’s members cover miscellaneous topics that must be removed from the data.

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Understand research hotspots surrounding COVID-19 and other coronavirus infections using topic modeling

Cited by 24 publications

References 19 publications

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

A scientometric analysis on coronaviruses research (1900–2020): Time for a continuous, cooperative and global approach

Track Iran's national COVID-19 response committee’s major concerns using two-stage unsupervised topic modeling

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