{"title":"基于社交媒体的食品安全被动监测研究框架:用于监管用途的顾客评论识别和评估,以及30家餐馆的案例研究","authors":"A. Prabhune, N. Sethiya, Heemanshu Arora","doi":"10.18231/j.ijfcm.2022.031","DOIUrl":null,"url":null,"abstract":"The primary objective of this paper is to develop a framework for continuous monitoring of the safety of food business operators without overburdening established regulatory systems through social media for food safety. A phase-wise methodology was adopted, wherein Phase 1 was dedicated to identifying available literature on Adverse Drugs Reactions (ADR) reporting using Social Media data. Phase 2 used the data from google maps review of the restaurants to replicate a similar methodology for Food Safety Surveillance. We identified 5 themes for a complete Surveillance framework, theme 1 involves data collection from social media, theme 2 involves pre-processing of data for analysis, theme 3 involves data annotations, theme 4 involves Identifying the relationship between regulatory violation and event, and theme 5 involves evaluation of the model. We were able to demonstrate the ADR reporting methodology could be adopted till theme 3, whereas theme 4 requires the development of an algorithm to assess the causality of an event with the Food Safety Code. According to our research, it is possible to develop a passive surveillance system for food safety that adheres to the principle of ADR reporting; however, the main obstacle is the absence of a causality assessment algorithm that can link an event to the food safety code and help regulators take immediate action.","PeriodicalId":13276,"journal":{"name":"Indian journal of forensic and community medicine","volume":"112 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A research framework for passive surveillance for food safety from social media: Identification and evaluation of customer reviews for regulatory use and case study of 30 restaurants\",\"authors\":\"A. Prabhune, N. Sethiya, Heemanshu Arora\",\"doi\":\"10.18231/j.ijfcm.2022.031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The primary objective of this paper is to develop a framework for continuous monitoring of the safety of food business operators without overburdening established regulatory systems through social media for food safety. A phase-wise methodology was adopted, wherein Phase 1 was dedicated to identifying available literature on Adverse Drugs Reactions (ADR) reporting using Social Media data. Phase 2 used the data from google maps review of the restaurants to replicate a similar methodology for Food Safety Surveillance. We identified 5 themes for a complete Surveillance framework, theme 1 involves data collection from social media, theme 2 involves pre-processing of data for analysis, theme 3 involves data annotations, theme 4 involves Identifying the relationship between regulatory violation and event, and theme 5 involves evaluation of the model. We were able to demonstrate the ADR reporting methodology could be adopted till theme 3, whereas theme 4 requires the development of an algorithm to assess the causality of an event with the Food Safety Code. According to our research, it is possible to develop a passive surveillance system for food safety that adheres to the principle of ADR reporting; however, the main obstacle is the absence of a causality assessment algorithm that can link an event to the food safety code and help regulators take immediate action.\",\"PeriodicalId\":13276,\"journal\":{\"name\":\"Indian journal of forensic and community medicine\",\"volume\":\"112 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Indian journal of forensic and community medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18231/j.ijfcm.2022.031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indian journal of forensic and community medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18231/j.ijfcm.2022.031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A research framework for passive surveillance for food safety from social media: Identification and evaluation of customer reviews for regulatory use and case study of 30 restaurants
The primary objective of this paper is to develop a framework for continuous monitoring of the safety of food business operators without overburdening established regulatory systems through social media for food safety. A phase-wise methodology was adopted, wherein Phase 1 was dedicated to identifying available literature on Adverse Drugs Reactions (ADR) reporting using Social Media data. Phase 2 used the data from google maps review of the restaurants to replicate a similar methodology for Food Safety Surveillance. We identified 5 themes for a complete Surveillance framework, theme 1 involves data collection from social media, theme 2 involves pre-processing of data for analysis, theme 3 involves data annotations, theme 4 involves Identifying the relationship between regulatory violation and event, and theme 5 involves evaluation of the model. We were able to demonstrate the ADR reporting methodology could be adopted till theme 3, whereas theme 4 requires the development of an algorithm to assess the causality of an event with the Food Safety Code. According to our research, it is possible to develop a passive surveillance system for food safety that adheres to the principle of ADR reporting; however, the main obstacle is the absence of a causality assessment algorithm that can link an event to the food safety code and help regulators take immediate action.