Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

Zhang, Wei; Wang, Meng; Zhu, Yan

doi:10.1016/j.ijinfomgt.2019.04.001

Cited by 74 publications

(42 citation statements)

References 20 publications

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“…From the perspective of the communication object, the users’ emotion types regarding food safety issues varied according to the emotion object. When the object of emotion was a government agency, it was more likely to cause negative comments, which is consistent with previous studies [ 82 , 83 ]. When the emotion object was an opinion leader, positive comments were more easily produced, with some opinion leaders being regarded by users as heroic figures who dare to think and act.…”

Section: Discussionsupporting

confidence: 92%

Factors Affecting Social Media Users’ Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security

Xiong

2021

Healthcare

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Social media is gradually building an online information environment regarding health. This environment is filled with many types of users’ emotions regarding food safety, especially negative emotions that can easily cause panic or anger among the population. However, the mechanisms of how it affects users’ emotions have not been fully studied. Therefore, from the perspective of communication and social psychology, this study uses the content analysis method to analyze factors affecting social media users’ emotions regarding food safety issues. In total, 371 tweet samples of genetically modified food security in Sina Weibo (similar to Twitter) were encoded, measured, and analyzed. The major findings are as follows: (1) Tweet account type, tweet topic, and emotion object were all significantly related to emotion type. Tweet depth and objectivity were both positively affected by emotion type, and objectivity had a greater impact. (2) Account type, tweet topic, and emotion object were all significantly related to emotion intensity. When the depths were the same, emotion intensity became stronger with the decrease in objectivity. (3) Account type, tweet topic, emotion object, and emotion type were all significantly related to a user’s emotion communication capacity. Tweet depth, objectivity, and user’s emotion intensity were positively correlated with emotion communication capacity. Positive emotions had stronger communication capacities than negative ones, which is not consistent with previous studies. These findings help us to understand both theoretically and practically the changes and dissemination of user’s emotions in a food safety and health information environment.

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Section: Discussionsupporting

confidence: 92%

Factors Affecting Social Media Users’ Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security

Xiong

2021

Healthcare

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“…In this way, we now enter a new stage where large companies that collect large volumes of data acquire access to global crowd control and information management ( Zhang et al., 2020 ). Therefore, in the fight against COVID-19, users should be aware of the limits and available options concerning the use of their data in new mass control approaches proposed by governments and private entities ( LaBrie et al., 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Ribeiro‐Navarrete

Saura

Palacios‐Marqués

2021

Technological Forecasting and Social Change

158

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Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users’ mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users’ mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.

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“…In the era of big data, how to quickly analyze massive network data and then establish a public opinion monitoring and guidance mechanism to provide decision support for managers is the current hot research topic. Compared with traditional social public opinion analysis, social public opinion analysis in the era of big data is more focused on the collection, storage, cleaning, and text mining techniques of large amounts of network data to obtain relevant public opinion research information from a large number of low-value density data [ 16 ]. “Data” and “technology” are the key production factors of big data public opinion research.…”

Section: Literature Reviewmentioning

confidence: 99%

Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data

Xia

et al. 2022

Socio-Economic Planning Sciences

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Based on complex adaptive system theory and information theory for investigating heterogeneous situations, this paper develops an outlier knowledge management framework based on three aspects—dimension, object, and situation—for dealing with extreme public health events. In the context of the COVID-19 pandemic, we apply advanced natural language processing (NLP) technology to conduct data mining and feature extraction on the microblog data from the Wuhan area and the imported case province (Henan Province) during the high and median operating periods of the epidemic. Our experiment indicates that the semantic and sentiment vocabulary of words, the sentiment curve, and the portrait of patients seeking help were all heterogeneous in the context of COVID-19. We extract and acquire the outlier knowledge of COVID-19 and incorporate it into the outlier knowledge base of extreme public health events for knowledge sharing and transformation. The knowledge base serves as a think tank for public opinion guidance and platform suggestions for dealing with extreme public health events. This paper provides novel ideas and methods for outlier knowledge management in healthcare contexts.

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Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

Cited by 74 publications

References 20 publications

Factors Affecting Social Media Users’ Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security

Factors Affecting Social Media Users’ Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data

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