Symposium on Public Health Law Surveillance: The Nexus of Information Technology and Public Health Law

McGowan, Angela; Schooley, Michael; Narvasa, Helen; Rankin, J.; Sosin, Daniel M.

doi:10.1111/j.1748-720x.2003.tb00744.x

Cited by 5 publications

(3 citation statements)

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“…Although a wide array of data sets on health risk exposures, health behaviors, and health outcomes are available, data on health laws are much harder to find (Hadfield 2006; Heise 1999; Mello and Zeiler 2008). Unlike many other areas of public health research, research on public health law and policy has developed few surveillance systems (Brownson et al 2006; McGowan et al 2003). Gathering information about the patterns of public health law adoption and implementation across states and local governments over time generally is done de novo in each research project.…”

Section: Challenges Facing Phlrmentioning

confidence: 99%

Making the Case for Laws That Improve Health: A Framework for Public Health Law Research

et al. 2010

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Section: Challenges Facing Phlrmentioning

confidence: 99%

Making the Case for Laws That Improve Health: A Framework for Public Health Law Research

et al. 2010

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“…Data collection is required to fit the time frame of the relevant and related policies [ 12 , 13 ]. Additionally, policy surveillance systems need to monitor patterns and trends of the related policy influence [ 14 , 15 ]. These systems require the data to be collected not only efficiently but also feasibly.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19

Tsai

Wang

2021

IJERPH

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Policymakers and relevant public health authorities can analyze people’s attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people’s attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people’s attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people’s attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.

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“…The evidence data preparation is required to fit the policy design window [12,13]. Also, policy surveillance systems need to monitor patterns and trends of the related policy influence [14,15]. These systems require the data collection is not only efficient but also cheap.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Twitter Data to Evaluate the People’s Attitudes to Public Health Policies and Events in the Era of COVID-19

Tsai

Wang

2020

Preprint

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Background: In the era of a pandemic like COVID-19, monitoring the sentimental changes of the population is an urgent need, especially for the policy makers of the public health. A possible solution is to build a fast and low-cost surveillance system by using the sentiment analysis of Twitter data. Unfortunately, choosing a suitable sentiment classification model is still challenging. The general pre-trained model may be insensitive to the new specific terms of the pandemic. The early-trained model may have a bias issue due to the incomplete specific corpus. Although it is reasonable to assume the late-trained model is relatively reliable, it is usually available months after a pandemic begins. Methods: This paper conducts the sentiment analysis of Twitter data and compares different models. Furthermore, we propose a strategy for using the pre-trained, early-trained, and latetrained models in a surveillance system based on Twitter data. The first two models can be used together in the early stage, while the last model can be used in the late stage. This study also analyzes the relationship between the sentimental changes of COVID-19-related Twitter data and the public health policies and events. Results: Our results indicate that applying the pre-trained model to preprocessing early training samples may improve the early-trained model. Both models can work together by making up each other in the surveillance system in the early stage. Conclusions: A fast and low-cost surveillance system is critical to the policy makers of the public health in a pandemic. This work uses the sentiment analysis of Twitter data to evaluate people’s attitudes to public health policies and events. We propose a strategy to make the surveillance system effective since the early stage. This study also connects the sentimental changes of COVID19-related Twitter data to the public health policies and events.

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Symposium on Public Health Law Surveillance: The Nexus of Information Technology and Public Health Law

Cited by 5 publications

References 0 publications

Making the Case for Laws That Improve Health: A Framework for Public Health Law Research

Making the Case for Laws That Improve Health: A Framework for Public Health Law Research

Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19

Analyzing Twitter Data to Evaluate the People’s Attitudes to Public Health Policies and Events in the Era of COVID-19

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