Social Media Insights During the COVID-19 Pandemic: Infodemiology Study Using Big Data

Tran, Huyen Thi Thanh; Lu, Shih‐Hao; Tran, Ha Thi Thu; Nguyen, Bien Van

doi:10.2196/27116

Cited by 18 publications

(15 citation statements)

References 33 publications

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“…The big data method has created new stimulation for the research of public sentiment ( 7 – 10 ). Hu et al ( 11 ) accrued over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021 and examined the spatiotemporal patterns of public sentiment and emotion over time at the national and state levels.…”

Section: Application Of the Big Data Methodsmentioning

confidence: 99%

Investigating the Public Sentiment in Major Public Emergencies Through the Complex Networks Method: A Case Study of COVID-19 Epidemic

Yang

Wang

Lin

2022

Front. Public Health

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The main purpose of this study is to investigate what topic indicators correlate with public sentiment during “coronavirus disease 2019 (COVID-19) epidemic” and which indicators control the complex networks of the topic indicators. We obtained 68,098 Weibo, categorized them into 11 topic indicators, and grouped these indicators into three dimensions. Then, we constructed the complex networks model of Weibo's topics and examined the key indicators affecting the public's sentiment during the major public emergency. The results showed that “positive emotion” is positively correlated with “recordings of epidemic” and “foreign comparisons,” while “negative emotion” is negatively correlated with “government image,” “recordings of epidemic,” and “asking for help online.” In addition, the two vertexes of “recordings of epidemic” and “foreign comparisons” are the most important “bridges” which connect the government and the public. The “recordings of epidemic” is the main connection “hub” between the government and the media. In other words, the “recordings of epidemic” is the central topic indicator that controls the entire topic network. In conclusion, the government should publish the advance of the events through official media on time and transparent way and create a platform where everyone can speak directly to the government for advice and assistance during a major public emergency in the future.

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Section: Application Of the Big Data Methodsmentioning

confidence: 99%

Investigating the Public Sentiment in Major Public Emergencies Through the Complex Networks Method: A Case Study of COVID-19 Epidemic

Yang

Wang

Lin

2022

Front. Public Health

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“…Some of these service providers make data available only to authorized users, whereas others provide it publicly. Previous studies used APIs to extract large amounts of behavioral data (Sohail et al 2021); (Tran et al 2021). This article contributed to how social media is defined by research from different scientific and non-English backgrounds, also elaborating on the use of social media in different cultural contexts (Aichner et al 2021).…”

Section: Total Area 12409417 Hamentioning

confidence: 98%

Performing Toraja rice ritual, communicating biodiversity on YouTube: A study of conservation on rice landrace

Ranteallo

Palinggi

Alam

et al. 2021

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The proliferating public interest in conservation can be explored by monitoring what people say and what people do on social media platforms. YouTube is one of the resources for digital conservation studies as the users are allowed to archive and share content. Meanwhile, networks visualization provided by computer software is powerful to preserve vernacular languages since text(s) tagging is one of the main processes in gaining data. The present study aims to answer research questions using a transdisciplinary approach to sociology, ethnobotany, and communication science. Thus, this research enquires; (a) how might conservation of Toraja rice landraces be practiced continuously? (b) what are the current forms of conservation that are developed and accepted as Toraja socio-cultural activities? (c) how do social media platforms, YouTube, for instance, contribute to the conservation efforts in rice landraces? (d) what are the social implications of conservation education through social media? In order to answer the above questions, six different research methods, namely, observations, in-depth interview, mapping distribution of rice landrace varieties in six fields of studies, calculating Index of Cultural Significance on rice accessions for practicing conservation rice landrace. Digital conservation and conservation culturomics were also measured using NodeXL and API for visualizing the graph of YouTube videos network and calculating the frequency of words occurrence according to search string Toraja rice ritual. The outcome from the study indicates the current forms of conservation by cultivating and consuming rice landrace varieties on everyday life and ritual, as well as preserving rice and agriculture images on wood carving motifs, folk song, and folk dance. The digital conservation efforts are demonstrated by archiving and broadcasting rice rituals on YouTube videos. With an increase of frequency on word occurrences by Toraja language on hashtags on YouTube videos, the users were contributing to participatory culture for preserving vernacular language and promoting agricultural innovations in digital conservation ecosystems and environmental communication.

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“…Regarding recent police statistics, nearly 300,000 news reports were uploaded to the internet, including websites, blogs, and forums, and approximately 600,000 news stories, articles, films, and clips about the illness were shared on social media sites [52]. Additionally, social media is effective in promoting health [53].…”

Section: Hypothesis 3(h3) Perception Covid-19 Vaccine (Pcv) Has a Positive Impact On An Individual's Behavioral Covid-19 Vaccination Intementioning

confidence: 99%

A Hybrid Model with Spherical Fuzzy-AHP, PLS-SEM and ANN to Predict Vaccination Intention against COVID-19

et al. 2021

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This study aims to identify the key factors affecting individuals’ behavioral vaccination intention against COVID-19 in Vietnam through an online questionnaire survey. Differing from previous studies, a novel three-staged approach combining Spherical Fuzzy Analytic Hierarchy Process (SF-AHP), Partial Least Squares-Structural Equation Model (PLS-SEM), and Artificial Neural Network (ANN) is proposed. Five factors associated with individuals’ behavioral vaccination intention (INT) based on 15 experts’ opinions are considered in SF-AHP analysis, including Perceived Severity of COVID-19 (PSC), Perceived COVID-19 vaccines (PVC), Trust in government intervention strategies (TRS), Social Influence (SOI), and Social media (SOM). First, the results of SF-AHP indicated that all proposed factors correlate with INT. Second, the data of 474 valid respondents were collected and analyzed using PLS-SEM. The PLS-SEM results reported that INT was directly influenced by PVC and TRS. In contrast, SOI had no direct effect on INT. Further, PSC and SOM moderated the relationship between PVC, TRS and INT, respectively. The ANN was deployed to validate the previous stages and found that the best predictors of COVID-19 vaccination intention were PVC, TRS, and SOM. These results were consistent with the SF-AHP and PLS-SEM models. This research provides an innovative new approach employing quantitative and qualitative techniques to understand individuals’ vaccination intention during the global pandemic. Furthermore, the proposed method can be used and expanded to assess the perceived efficacy of COVID-19 measures in other nations currently battling the COVID-19 outbreak.

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Social Media Insights During the COVID-19 Pandemic: Infodemiology Study Using Big Data

Cited by 18 publications

References 33 publications

Investigating the Public Sentiment in Major Public Emergencies Through the Complex Networks Method: A Case Study of COVID-19 Epidemic

Investigating the Public Sentiment in Major Public Emergencies Through the Complex Networks Method: A Case Study of COVID-19 Epidemic

Performing Toraja rice ritual, communicating biodiversity on YouTube: A study of conservation on rice landrace

A Hybrid Model with Spherical Fuzzy-AHP, PLS-SEM and ANN to Predict Vaccination Intention against COVID-19

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