Analysing discourse around COVID-19 in the Australian Twittersphere: A real-time corpus-based analysis

Schweinberger, Martin; Haugh, Michael; Hames, Sam

doi:10.1177/20539517211021437

Cited by 12 publications

(9 citation statements)

References 40 publications

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“…We used an extended database export to cover entries with at least 10,000 inhabitants (see Supplementary File for detailed information on matches/non-matches for each language). Relying on "location" fields to geolocate Twitter users is an established approach in social scientific research (for example, Bruns & Enli, 2018;Bruns et al, 2017;Rauchfleisch et al, 2021;Schweinberger et al, 2021;Vogler et al, 2019). However, some groups of users might be more inclined to disclose their location than others.…”

Section: Time Frame and Collection Of Tweetsmentioning

confidence: 99%

The multilingual Twitter-discourse on vaccination in Germany during the COVID-19 pandemic

Schmid-Petri

Bürger

Schlögl

et al. 2023

MaC

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There is evidence that specific segments of the population were hit particularly hard by the Covid-19 pandemic (e.g., people with a migration background). In this context, the impact and role played by online platforms in facilitating the integration or fragmentation of public debates and social groups is a recurring topic of discussion. This is where our study ties in, we ask: How is the topic of vaccination discussed and evaluated in different language communities in Germany on Twitter during the Covid-19 pandemic? We collected all tweets in German, Russian, Turkish, and Polish (i.e., the largest migrant groups in Germany) in March 2021 that included the most important keywords related to Covid-19 vaccination. All users were automatically geocoded. The data was limited to tweets from Germany. Our results show that the multilingual debate on Covid-19 vaccination in Germany does not have many structural connections. However, in terms of actors, arguments, and positions towards Covid-19 vaccination, the discussion in the different language communities is similar. This indicates that there is a parallelism of the debates but no social-discursive integration.

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Section: Time Frame and Collection Of Tweetsmentioning

confidence: 99%

The multilingual Twitter-discourse on vaccination in Germany during the COVID-19 pandemic

Schmid-Petri

Bürger

Schlögl

et al. 2023

MaC

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“…6,12–14 A growing body of researchers have shown that sentiment analysis and topic modeling can be used to successfully investigate emotions and sentiment using natural language processing. 13,15–17 Schweinberger et al 15 chose to model topics and sub-topics across different phases of the pandemic. Singh et al 18 demonstrated that Twitter conversations may be used to predict the spread and outbreak of COVID-19.…”

Section: Literature Reviewmentioning

confidence: 99%

What Remains Now That the Fear Has Passed? Developmental Trajectory Analysis of COVID-19 Pandemic for Co-occurrences of Twitter, Google Trends, and Public Health Data

Rathke¹,

Yu²,

Huang³

2023

Disaster med. public health prep.

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The rapid onset of COVID-19 created a complex virtual collective consciousness. Misinformation and polarization were hallmarks of the pandemic in the United States, highlighting the importance of studying public opinion online. Humans express their thoughts and feelings more openly than ever before on social media, co-occurrence of multiple data sources becomes valuable for monitoring and understanding public sentimental preparedness and response to an event within our society. In this study, Twitter and Google Trends data were used as the co-occurrence data for the understanding of the dynamics of sentiment and interest during the COVID-19 pandemic in the United States from January 2020 to September 2021. Developmental trajectory analysis of Twitter sentiment was conducted using corpus linguistic techniques and word cloud mapping to reveal eight positive and negative sentiments and emotions. Machine learning algorithms were employed to implement the opinion mining how Twitter sentiment was related to Google Trends interest with historical COVID-19 public health data. The sentiment analysis went beyond polarity to detect specific feelings and emotions during the pandemic. The discoveries on the behaviors of emotions at each stage of the pandemic were presented from the emotion detection when associated with the historical COVID-19 data and Google Trends data.

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“…A special influence on constructing the media discourse had one sector of the media plane, which is social media. The existing body of research on COVID-19 and social media discourse has been mainly oriented toward Twitter (Damiano & Catellier, 2020;Chen et al, 2020;Schweinberger et al, 2021). Since Twitter is considered to provide researchers the opportunity of studying the role of social media in global health crisis (Chen et al, 2020;Lecompte-Van Poucke, 2022), but also there are several research papers which focus on social media plane in general (Gupta et al, 2022;Han et al, 2020).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Critical Analysis of the Discursive Construction “Students of Covid-19 Era”

Rakipllari

2022

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The present paper aims to analyse the discursive construction students of COVID-19 (era), which has been broadly used as a label to refer to all the students, who were shifted to online education due to the COVID-19 pandemic situation. Following the critical discourse analysis theoretical approach, we examine the discursive planes that have built and reinforced this discursive construction. The paper focuses on three main planes that have reinforced this discourse construction: the science (s) plane, (online) media plane, and the everyday life plane. The scientific discourse on education has emphasized the detrimental effects the pandemic has had and is expected to have on learners. Moreover, traditional, online, and social media have continuously pointed to the negative impact of the pandemic on the academic performance of students. Especially social media has been using a very powerful tool to convey this kind of discursive stance, i.e., memes. The everyday life plane has also played a role in the discursive construction of students of the COVID-19 era by bringing up and building upon the real-life experiences of the learning loss that students have been facing. The paper analyses representative discursive fragments from all the above-mentioned discourse planes, to shed more light on the students of COVID-19 discursive construction, as a pathologizing label used for a whole generation of learners affected by the COVID-19 pandemic. Key words: COVID-19, discourse, discursive construction, learning loss, students.

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Analysing discourse around COVID-19 in the Australian Twittersphere: A real-time corpus-based analysis

Cited by 12 publications

References 40 publications

The multilingual Twitter-discourse on vaccination in Germany during the COVID-19 pandemic

The multilingual Twitter-discourse on vaccination in Germany during the COVID-19 pandemic

What Remains Now That the Fear Has Passed? Developmental Trajectory Analysis of COVID-19 Pandemic for Co-occurrences of Twitter, Google Trends, and Public Health Data

Critical Analysis of the Discursive Construction “Students of Covid-19 Era”

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