Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution
Food safety remains a crucial concern in both public health and societal stability. In the age of information technology, social media has emerged as a pivotal channel for shaping public opinion and disseminating information, exerting a substantial influence on how the public perceives incidents rel...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Food Protection |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0362028X24002023 |
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| author | Biao Ma Ruihan Zheng |
| author_facet | Biao Ma Ruihan Zheng |
| author_sort | Biao Ma |
| collection | DOAJ |
| description | Food safety remains a crucial concern in both public health and societal stability. In the age of information technology, social media has emerged as a pivotal channel for shaping public opinion and disseminating information, exerting a substantial influence on how the public perceives incidents related to food safety. This study specifically focuses on the “Rat-Headed Duck Neck” incident as a case study, conducting a comprehensive analysis of extensive social media data to investigate how online public discourse molds perceptions of such events. To accomplish this research, data were initially gathered using a custom web crawler technology. These data encompassed various aspects, including user interactions, emotional expressions, and the evolution of topics. Subsequently, the study employed an innovative approach by combining BERT-TextCNN and BERTopic models for a thorough analysis of sentiment and thematic aspects of the textual data. This analysis provided insights into the intricate emotions and primary concerns of the public regarding incidents related to food safety. Furthermore, the research harnessed Gephi, a network analysis tool, to scrutinize the dissemination of information within the network and to monitor dynamic shifts in public opinion. The findings from this study not only shed light on the role of online public sentiment in the propagation of food safety events but also provide fresh perspectives for policymakers and business leaders when responding to similar crises, taking into account the subtleties of online public sentiment. These innovative methodologies and findings significantly enhance our comprehension of public responses to food safety incidents within the realm of social media. |
| format | Article |
| id | doaj-art-48550b9fc45741d4a0b1708901c57c1d |
| institution | Kabale University |
| issn | 0362-028X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Food Protection |
| spelling | doaj-art-48550b9fc45741d4a0b1708901c57c1d2025-01-09T06:12:34ZengElsevierJournal of Food Protection0362-028X2025-01-01881100418Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment EvolutionBiao Ma0Ruihan Zheng1School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, PR China; School of Business, Wuxi Tai hu University, Wuxi 214122, PR China; Corresponding author at: School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, PR China.School of Business, Wuxi Tai hu University, Wuxi 214122, PR ChinaFood safety remains a crucial concern in both public health and societal stability. In the age of information technology, social media has emerged as a pivotal channel for shaping public opinion and disseminating information, exerting a substantial influence on how the public perceives incidents related to food safety. This study specifically focuses on the “Rat-Headed Duck Neck” incident as a case study, conducting a comprehensive analysis of extensive social media data to investigate how online public discourse molds perceptions of such events. To accomplish this research, data were initially gathered using a custom web crawler technology. These data encompassed various aspects, including user interactions, emotional expressions, and the evolution of topics. Subsequently, the study employed an innovative approach by combining BERT-TextCNN and BERTopic models for a thorough analysis of sentiment and thematic aspects of the textual data. This analysis provided insights into the intricate emotions and primary concerns of the public regarding incidents related to food safety. Furthermore, the research harnessed Gephi, a network analysis tool, to scrutinize the dissemination of information within the network and to monitor dynamic shifts in public opinion. The findings from this study not only shed light on the role of online public sentiment in the propagation of food safety events but also provide fresh perspectives for policymakers and business leaders when responding to similar crises, taking into account the subtleties of online public sentiment. These innovative methodologies and findings significantly enhance our comprehension of public responses to food safety incidents within the realm of social media.http://www.sciencedirect.com/science/article/pii/S0362028X24002023Deep learningFood safetySentiment analysisText mining |
| spellingShingle | Biao Ma Ruihan Zheng Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution Journal of Food Protection Deep learning Food safety Sentiment analysis Text mining |
| title | Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution |
| title_full | Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution |
| title_fullStr | Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution |
| title_full_unstemmed | Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution |
| title_short | Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution |
| title_sort | exploring food safety emergency incidents on sina weibo using text mining and sentiment evolution |
| topic | Deep learning Food safety Sentiment analysis Text mining |
| url | http://www.sciencedirect.com/science/article/pii/S0362028X24002023 |
| work_keys_str_mv | AT biaoma exploringfoodsafetyemergencyincidentsonsinaweibousingtextminingandsentimentevolution AT ruihanzheng exploringfoodsafetyemergencyincidentsonsinaweibousingtextminingandsentimentevolution |