Public health and social media: A study of Zika virus‐related posts on Yahoo! Answers

Jin, Zhang; Chen, Ye; Zhao, Yuehua; Wolfram, Dietmar; Ma, Feicheng

doi:10.1002/asi.24245

Cited by 31 publications

(27 citation statements)

References 38 publications

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“…Zhang et al [ 49 ] investigated the contents of posts about the Zika virus on Yahoo! Answers, identify and reveal subject patterns about the Zika virus, and analyze the temporal changes of the revealed subject topics over 4 defined periods of the Zika virus outbreak.…”

Section: Analysis Of Covid-19 Trendsmentioning

confidence: 99%

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Taieb

Aouicha

2021

Appl Intell

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Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benefits of social data analysis for the healthcare practices and curing domain. Similarly, these data are exploited now for tracking the COVID-19 pandemic but the majority of works exploited Twitter as source. In this paper, we choose to exploit Facebook, rarely used, for tracking the evolution of COVID-19 related trends. In fact, a multilingual dataset covering 7 languages (English (EN), Arabic (AR), Spanish (ES), Italian (IT), German (DE), French (FR) and Japanese (JP)) is extracted from Facebook public posts. The proposal is an analytics process including a data gathering step, pre-processing, LDA-based topic modeling and presentation module using graph structure. Data analysing covers the duration spanned from January 1st, 2020 to May 15, 2020 divided on three periods in cumulative way: first period January-February, second period March-April and the last one to 15 May. The results showed that the extracted topics correspond to the chronological development of what has been circulated around the pandemic and the measures that have been taken according to the various languages under discussion representing several countries.

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Section: Analysis Of Covid-19 Trendsmentioning

confidence: 99%

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Taieb

Aouicha

2021

Appl Intell

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“…For example, Yahoo! Answer includes more than 300 million questions and 90 million unique users worldwide as of 2012 since the service launched in 2005 [ 10 ]. Another popular social Q&A site launched in 2006, Wiki Answers, has 17 million answers posted.…”

Section: Related Literaturementioning

confidence: 99%

“…Answers in English, Baidu Knows in Chinese, and Naver Knowledge-IN in Korean, to exchange information to address their concerns about hearing problems. Prior research has demonstrated that a collection of questions that patients post on social Q&A provides not only interesting but also important information to health professionals [ 9 , 10 , 11 ]. Topic analysis of patients’ questions on the topic of hearing loss allows for achieving a thorough understanding of patients’ perspectives, thereby leading to better development of the patient-centered intervention.…”

Section: Introductionmentioning

confidence: 99%

Topic Modeling for Analyzing Patients’ Perceptions and Concerns of Hearing Loss on Social Q&A Sites: Incorporating Patients’ Perspective

Bahng

Lee

2020

IJERPH

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Hearing loss is the most common human sensory deficit, affecting normal communication. Recently, patients with hearing loss or at risk of hearing loss are increasingly turning to the online health community for health information and support. Information on health-related topics exchanged on the Internet is a useful resource to examine patients’ informational needs. The ability to understand the patients’ perspectives on hearing loss is critical for health professionals to develop a patient-centered intervention. In this paper, we apply Latent Dirichlet Allocation (LDA) on electronic patient-authored questions on social question-and-answer (Q&A) sites to identify patients’ perceptions, concerns, and needs on hearing loss. Our results reveal 21 topics, which are both representative and meaningful, and mostly correspond to sub-fields established in hearing science research. The latent topics are classified into five themes, which include “sudden hearing loss”, “tinnitus”, “noise-induced hearing loss”, “hearing aids”, “dizziness”, “curiosity about hearing loss”, “otitis media” and “complications of disease”. Our topic analysis of patients’ questions on the topic of hearing loss allows achieving a thorough understanding of patients’ perspectives, thereby leading to better development of the patient-centered intervention.

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“…Typically, artificial intelligence (AI) is mainly used to monitor online information propagation rules, [9][10][11] and analyze the influencing factors of online Web texts and infectious disease transmission. [12][13][14] The occurrence of a major public health emergency breaks the public's original psychological balance, which results in a psychological abnormality. As a result, identifying the emotions 15 of netizens can be of tremendous help in promoting public health prevention 16 and health education.…”

mentioning

confidence: 99%

Internet Public Opinion Evolution in the COVID-19 Event and Coping Strategies

Zhong

2020

Disaster med. public health prep.

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Objective: In this study, we carried out a text analysis on the information disseminated and discussed among netizens on the Baidu Post Bar (the world’s largest Chinese forum) during the COVID-19 epidemic, to create a policy basis for health administrative departments. Methods: We used Python tools to search for the relevant data on the Baidu Post Bar. Next, a text analysis was performed on the posts’ contents using a combination of LDA, sentiment analysis, and correlation analysis. Results: According to the LDA analysis, the public was highly interested in topics such as COVID-19 prevention, infection symptoms, infection and coping measures, sources of transmission and treatments, community management, and work resumption. The majority of the public had negative emotional values, yet a portion of the public held positive emotional values. We also performed a correlation analysis of the influencing factors was established. Conclusion: Netizens’ degree of concern shown in their posts was greatly associated with the spread of COVID-19. With the rise, diffusion, outbreak, and mitigation of COVID-19 in China, netizens have successively created a large number of posts, and the topics of discussion varied over time. Therefore, the media and the government have the responsibility to distribute positive information, to correctly guide the public’s emotions to bring some sort of reassurance to the public.

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Public health and social media: A study of Zika virus‐related posts on Yahoo! Answers

Cited by 31 publications

References 38 publications

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Topic Modeling for Analyzing Patients’ Perceptions and Concerns of Hearing Loss on Social Q&A Sites: Incorporating Patients’ Perspective

Internet Public Opinion Evolution in the COVID-19 Event and Coping Strategies

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