An analysis of COVID-19 economic measures and attitudes: evidence from social media mining

Domalewska, Dorota

doi:10.1186/s40537-021-00431-z

Cited by 16 publications

(10 citation statements)

References 44 publications

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“…A smaller-scale analysis, which tracks the public reception of specific scientific news stories, may yield useful granular detail on communicative dynamics within and between media sources [ 91 ]. Alternatively, a ‘big data’ approach could identify disparities between different media platforms; for example, news media may depart from social media’s documented tendency to focus on short-term over long-term implications of the pandemic [ 92 ].…”

Section: Discussionmentioning

confidence: 99%

Media Representations of Science during the First Wave of the COVID-19 Pandemic: A Qualitative Analysis of News and Social Media on the Island of Ireland

O’Connor

O’Connell

Burke

et al. 2021

IJERPH

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COVID-19 is arguably the most critical science communication challenge of a generation, yet comes in the wake of a purported populist turn against scientific expertise in western societies. This study advances understanding of science–society relations during the COVID-19 pandemic by analysing how science was represented in news and social media coverage of COVID-19 on the island of Ireland. Thematic analysis was performed on a dataset comprising 952 news articles and 603 tweets published between 1 January and 31 May 2020. Three themes characterised the range of meanings attached to science: ‘Defining science: Its subjects, practice and process’, ‘Relating to science: Between veneration and suspicion’ and ‘Using science: As solution, policy and rhetoric’. The analysis suggested that the COVID-19 pandemic represented a platform to highlight the value, philosophy, process and day-to-day activity of scientific research. However, the study also identified risks the pandemic might pose to science communication, including feeding public alienation by disparaging lay understandings, reinforcing stereotypical images of scientists, and amplifying the politicisation of scientific statements.

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Section: Discussionmentioning

confidence: 99%

Media Representations of Science during the First Wave of the COVID-19 Pandemic: A Qualitative Analysis of News and Social Media on the Island of Ireland

O’Connor

O’Connell

Burke

et al. 2021

IJERPH

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“…This system allows working with texts in the Kazakh and Russian languages. It also has built-in modules for connecting to the application programming interfaces (APIs) of social networks: Vkontakte [52], Facebook [53,54], Twitter [22,55], Instagram [56,57], YouTube [58], Telegram [59], and Odnoklassniki [60]. The OMSystem automatically determines the language of the text (Russian, Kazakh) and the sentiment of the topic, as negative, positive, or neutral, using a sentiment dictionary and ML algorithms.…”

Section: The Omsystem Information System Design Methodologymentioning

confidence: 99%

On the development of an information system for monitoring user opinion and its role for the public

et al. 2022

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Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users’ opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem’s social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system’s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people’s moods and attitudes to the Government’s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.

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“…This system allows working with texts in the Kazakh and Russian languages. It also has built-in modules for connecting to the application programming interfaces (APIs) of social networks: Vkontakte [40], Facebook [41,42], Twitter [43,44], Instagram [45,46], YouTube [47], Telegram [48], and Odnoklassniki [49]. The OMSystem automatically determines the language of the text (Russian, Kazakh, smiles/characters) and the sentiment of the topic, as negative, positive, or neutral, using a sentiment dictionary and ML algorithms.…”

Section: The Omsystem Information System Design Methodologymentioning

confidence: 99%

On the Development of an Information System for Monitoring User Opinion and its Role for the Public

Karyukin¹,

Mutanov²,

Mamykova³

et al. 2021

Preprint

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Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages such as the Russian and Kazakh languages in social media are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users’ opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. In the experimental part, the system’s monitoring of the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease are thoroughly observed and analyzed. The analysis allowed discovering the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system’s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people’s mood and attitude to the Government’s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust to vaccination, etc.) and assessment of the social mood.

show abstract

An analysis of COVID-19 economic measures and attitudes: evidence from social media mining

Cited by 16 publications

References 44 publications

Media Representations of Science during the First Wave of the COVID-19 Pandemic: A Qualitative Analysis of News and Social Media on the Island of Ireland

Media Representations of Science during the First Wave of the COVID-19 Pandemic: A Qualitative Analysis of News and Social Media on the Island of Ireland

On the development of an information system for monitoring user opinion and its role for the public

On the Development of an Information System for Monitoring User Opinion and its Role for the Public

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