Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter

Jafarzadeh, Hamed; Pauleen, David J.; Abedin, Ehsan; Weerasinghe, Kasuni; Taşkın, Nazım; Coşkun, Mustafa

doi:10.1371/journal.pone.0259882

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Topic model views can be somewhat subjective, but model tuning can help to reduce subjectivity on views, providing a quasi-subjective framework with which to view the corpus. Model tuning acts like a pair of binoculars, focusing the corpus without under or over focusing word probabilities [44]. Fig 3 shows four model tuning estimators that provide windows into the topic model corpus [45][46][47][48][49].…”

Section: Plos Onementioning

confidence: 99%

What do people write about COVID-19 and teaching, publicly? Insulators and threats to newly habituated and institutionalized practices for instruction

Martinez

2022

PLoS ONE

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Covid represents major changes in teaching across the world. This study examined some of those changes through tweets that contained threats and insulators to habitualization of newer teaching practices. The investigator harvested tweets to determine sentiment differences between teaching and schools and teaching and online. Topic modeling explored the topics in two separate corpora. Omnibus Yuen’s robust bootstrapped t-tests tested for sentiment differences between the two corpora based on emotions such as fear, anger, disgust, etc. Qualitative responses voiced ideas of insulation and threats to teaching modalities institutionalized during the pandemic. The investigator found that ‘teaching and school’ was associated with higher anger, distrust, and negative emotions than ‘teaching and online’ corpus sets. Qualitative responses indicated support for online instruction, albeit complicated by topic modeling concerns with the modality. Some twitter responses criticized government actions as restrictive. The investigator concluded that insulation and threats towards habitualization and institutionalization of newer teaching modalities during covid are rich and sometimes at odds with each other, showing tension at times.

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Section: Plos Onementioning

confidence: 99%

What do people write about COVID-19 and teaching, publicly? Insulators and threats to newly habituated and institutionalized practices for instruction

Martinez

2022

PLoS ONE

View full text Add to dashboard Cite

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Bibliometric and visualized analysis of social network analysis research on Scopus databases and VOSviewer

Gandasari,

Tjahjana,

Dwidienawati

et al. 2024

Cogent Business & Management

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Tracking Long COVID Discourse on Twitter: A Comparative Study across Canada, the US, and English-Speaking Europe (including the UK), Using Topic Modeling and Sentiment Analysis (Preprint)

AbuRaed,

Prikryl,

Tang

et al. 2024

Preprint

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BACKGROUND Social media serves as a vast repository of data, offering insights into the perceptions and emotions surrounding significant societal issues. Amidst the COVID-19 pandemic, Long COVID has emerged as a chronic health condition, profoundly affecting numerous lives and livelihoods. Given the dynamic nature of Long COVID and our evolving understanding of it, effectively capturing people's sentiments and perceptions through social media becomes increasingly crucial. By harnessing the wealth of data available on social platforms, we can better track the evolving narrative surrounding Long COVID and the collective efforts to address this pressing issue. OBJECTIVE We aimed to investigate people’s perceptions and sentiments around Long COVID in Canada, the US, and English-speaking Europe, including the UK. METHODS We analyzed Long COVID–related tweets from 2021 using topic modeling and sentiment analysis, and interpreted the results with public health experts. We compared timelines of topics discussed across the three English-speaking regions, and also explored sentiments associated with the tweets. RESULTS Topic modeling identified five topics, and public health experts provided interpretations of the topics based on visualization, top-ranked salient terms, and representative tweets for each topic. The interpretation for each topic was not entirely distinct from one topic to the next, resulting in difficulty identifying clear patterns when analyzing the prominence of these topics over time. However, there appeared to be an overall increasing trend in the topic of duration and suffering associated with Long COVID across the regions, consistent with the increasing burden of and awareness around Long COVID as the pandemic progressed and the number of people living with Long COVID increased. CONCLUSIONS Analysis aided by natural language processing techniques and domain expert input has the potential to produce useful insights for public health monitoring and action. This study explored the use of topic modeling and sentiment analysis on Long COVID–related tweets in Canada, the US, and English-speaking Europe. This kind of information could serve as one input in public health monitoring and surveillance systems by providing a high-level understanding of the frequency and predominance of discussions and sentiments pertaining to public health issues such as Long COVID, as well as to future efforts to address such issues.

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Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter

Cited by 4 publications

References 45 publications

What do people write about COVID-19 and teaching, publicly? Insulators and threats to newly habituated and institutionalized practices for instruction

What do people write about COVID-19 and teaching, publicly? Insulators and threats to newly habituated and institutionalized practices for instruction

Bibliometric and visualized analysis of social network analysis research on Scopus databases and VOSviewer

Tracking Long COVID Discourse on Twitter: A Comparative Study across Canada, the US, and English-Speaking Europe (including the UK), Using Topic Modeling and Sentiment Analysis (Preprint)

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