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1.
During the coronavirus pandemic, policy makers need to interpret available public health data to make decisions affecting public health. However, the United States’ coronavirus response faced data gaps, inadequate and inconsistent definitions of data across different governmental jurisdictions, ambiguous timing in reporting, problems in accessing data, and changing interpretations from scientific institutions. These present numerous problems for the decision makers relying on this information. This paper documents some of the data pitfalls in coronavirus public health data reporting, as identified by the authors in the course of supporting data management for New England’s coronavirus response. We provide recommendations for individuals to collect data more effectively during emergency situations such as a COVID-19 surge, as well as recommendations for institutions to provide more meaningful data for various users to access. Through this, we hope to motivate action to avoid data pitfalls during public health responses in the future. 相似文献
2.
《Information processing & management》2022,59(4):102990
Documenting the emergent social representations of COVID-19 in public communication is necessary for critically reflecting on pandemic responses and providing guidance for global pandemic recovery policies and practices. This study documents the dynamics of changing social representations of the COVID-19 pandemic on one of the largest Chinese social media, Weibo, from December 2019 to April 2020. We draw on the social representation theory (SRT) and conceptualize topics and topic networks as a form of social representation. We analyzed a dataset of 40 million COVID-19 related posts from 9.7 million users (including the general public, opinion leaders, and organizations) using machine learning methods. We identified 12 topics and found an expansion in social representations of COVID-19 from a clinical and epidemiological perspective to a broader perspective that integrated personal illness experiences with economic and sociopolitical discourses. Discussions about COVID-19 science did not take a prominent position in the representations, suggesting a lack of effective science and risk communication. Further, we found the strongest association of social representations existed between the public and opinion leaders and the organizations’ representations did not align much with the other two groups, suggesting a lack of organizations’ influence in public representations of COVID-19 on social media in China. 相似文献
3.
The implementation of digital contact tracing applications around the world to help reduce the spread of the COVID-19 pandemic represents one of the most ambitious uses of massive-scale citizen data ever attempted. There is major divergence among nations, however, between a “privacy-first” approach which protects citizens’ data at the cost of extremely limited access for public health authorities and researchers, and a “data-first” approach which stores large amounts of data which, while of immeasurable value to epidemiologists and other researchers, may significantly intrude upon citizens’ privacy. The lack of a consensus on privacy protection in the contact tracing process creates risks of non-compliance or deliberate obfuscation from citizens who fear revealing private aspects of their lives – a factor greatly exacerbated by recent major scandals over online privacy and the illicit use of citizens’ digital information, which have heightened public consciousness of these issues and created significant new challenges for any collection of large-scale public data. While digital contact tracing for COVID-19 remains in its infancy, the lack of consensus around best practices for its implementation and for reassuring citizens of the protection of their privacy may already have impeded its capacity to contribute to the pandemic response. 相似文献
4.
《Information processing & management》2023,60(3):103299
Understanding the effects of gender-specific emotional responses on information sharing behaviors are of great importance for swift, clear, and accurate public health crisis communication, but remains underexplored. This study fills this gap by investigating gender-specific anxiety- and anger-related emotional responses and their effects on the virality of crisis information by creatively drawing on social role theory, integrated crisis communication modeling, and text mining. The theoretical model is tested using two datasets (Changsheng vaccine crisis with 2,423,074 textual data and COVID-19 pandemic with 893,930 textual data) collected from Weibo, a leading social media platform in China. Females express significantly high anxiety and anger levels (p value<0.001) during the Changsheng fake vaccine crisis, while express significantly higher levels of anxiety during COVID-19 than males (p value<0.001), but not anger (p value=0.13). Regression analysis suggests that the virality of crisis information is significantly strengthened when the level of anger in posts of males is high or the level of anxiety in posts of females is high for both crises. However, such gender-specific virality differences of anger/anxiety expressions are violated once females have large numbers of followers (influencers). Furthermore, the gender-specific emotional effects on crisis information are more significantly enhanced for male influencers than female influencers. This study contributes to the literature on gender-specific emotional characteristics of crisis communication on social media and provides implications for practice. 相似文献
5.
Political polarization remains perhaps the “greatest barrier” to effective COVID-19 pandemic mitigation measures in the United States. Social media has been implicated in fueling this polarization. In this paper, we uncover the network of COVID-19 related news sources shared to 30 politically biased and 2 neutral subcommunities on Reddit. We find, using exponential random graph modeling, that news sources associated with highly toxic – “rude, disrespectful” – content are more likely to be shared across political subreddits. We also find homophily according to toxicity levels in the network of online news sources. Our findings suggest that news sources associated with high toxicity are rewarded with prominent positions in the resultant network. The toxicity in COVID-19 discussions may fuel political polarization by denigrating ideological opponents and politicizing responses to the COVID-19 pandemic, all to the detriment of mitigation measures. Public health practitioners should monitor toxicity in public online discussions to familiarize themselves with emerging political arguments that threaten adherence to public health crises management. We also recommend, based on our findings, that social media platforms algorithmically promote neutral and scientific news sources to reduce toxic discussion in subcommunities and encourage compliance with public health recommendations in the fight against COVID-19. 相似文献
6.
《Information processing & management》2022,59(2):102820
As COVID-19 swept over the world, people discussed facts, expressed opinions, and shared sentiments about the pandemic on social media. Since policies such as travel restriction and lockdown in reaction to COVID-19 were made at different levels of the society (e.g., schools and employers) and the government, we build a large geo-tagged Twitter dataset titled UsaGeoCov19 and perform an exploratory analysis by geographic location. Specifically, we collect 650,563 unique geo-tagged tweets across the United States covering the date range from January 25 to May 10, 2020. Tweet locations enable us to conduct region-specific studies such as tweeting volumes and sentiment, sometimes in response to local regulations and reported COVID-19 cases. During this period, many people started working from home. The gap between workdays and weekends in hourly tweet volumes inspire us to propose algorithms to estimate work engagement during the COVID-19 crisis. This paper also summarizes themes and topics of tweets in our dataset using both social media exclusive tools (i.e., #hashtags, @mentions) and the latent Dirichlet allocation model. We welcome requests for data sharing and conversations for more insights.UsaGeoCov19 link: http://yunhefeng.me/geo-tagged_twitter_datasets/. 相似文献
7.
《Information processing & management》2023,60(4):103325
Climate change has become one of the most significant crises of our time. Public opinion on climate change is influenced by social media platforms such as Twitter, often divided into believers and deniers. In this paper, we propose a framework to classify a tweet’s stance on climate change (denier/believer). Existing approaches to stance detection and classification of climate change tweets either have paid little attention to the characteristics of deniers’ tweets or often lack an appropriate architecture. However, the relevant literature reveals that the sentimental aspects and time perspective of climate change conversations on Twitter have a major impact on public attitudes and environmental orientation. Therefore, in our study, we focus on exploring the role of temporal orientation and sentiment analysis (auxiliary tasks) in detecting the attitude of tweets on climate change (main task). Our proposed framework STASY integrates word- and sentence-based feature encoders with the intra-task and shared-private attention frameworks to better encode the interactions between task-specific and shared features. We conducted our experiments on our novel curated climate change CLiCS dataset (2465 denier and 7235 believer tweets), two publicly available climate change datasets (ClimateICWSM-2022 and ClimateStance-2022), and two benchmark stance detection datasets (SemEval-2016 and COVID-19-Stance). Experiments show that our proposed approach improves stance detection performance (with an average improvement of 12.14% on our climate change dataset, 15.18% on ClimateICWSM-2022, 12.94% on ClimateStance-2022, 19.38% on SemEval-2016, and 35.01% on COVID-19-Stance in terms of average F1 scores) by benefiting from the auxiliary tasks compared to the baseline methods. 相似文献
8.
[目的/意义]新型冠状病毒肺炎疫情(简称新冠肺炎疫情)的全球蔓延引发了各领域学者对于突发公共卫生事件科学应对的思考。文章以新冠肺炎疫情为例,以微博为研究对象,旨在探讨突发公共卫生事件中公众的信息需求对于危机治理的影响机制。[方法/过程]首先,对新冠肺炎疫情及微博舆情做出阶段划分,进而利用质性分析结合层次聚类法从微博文本数据中抽取公众信息需求并跟踪其演变,最终结合相关理论探索性地建立了突发公共卫生事件公众信息需求模型。[结果/结论]突发公共卫生事件中公众的信息需求主要围绕风险认知、行为规范、情感、行为四个方面,通过社交媒体可以准确追踪公众信息需求并向公众提供所需信息,信息需求的满足最终促使公众自发参与危机治理。 相似文献
9.
《Information processing & management》2022,59(6):103073
With the onset of COVID-19, the pandemic has aroused huge discussions on social media like Twitter, followed by many social media analyses concerning it. Despite such an abundance of studies, however, little work has been done on reactions from the public and officials on social networks and their associations, especially during the early outbreak stage. In this paper, a total of 9,259,861 COVID-19-related English tweets published from 31 December 2019 to 11 March 2020 are accumulated for exploring the participatory dynamics of public attention and news coverage during the early stage of the pandemic. An easy numeric data augmentation (ENDA) technique is proposed for generating new samples while preserving label validity. It attains superior performance on text classification tasks with deep models (BERT) than an easier data augmentation method. To demonstrate the efficacy of ENDA further, experiments and ablation studies have also been implemented on other benchmark datasets. The classification results of COVID-19 tweets show tweets peaks trigged by momentous events and a strong positive correlation between the daily number of personal narratives and news reports. We argue that there were three periods divided by the turning points on January 20 and February 23 and the low level of news coverage suggests the missed windows for government response in early January and February. Our study not only contributes to a deeper understanding of the dynamic patterns and relationships of public attention and news coverage on social media during the pandemic but also sheds light on early emergency management and government response on social media during global health crises. 相似文献
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11.
《Information processing & management》2023,60(1):103116
Research on automated social media rumour verification, the task of identifying the veracity of questionable information circulating on social media, has yielded neural models achieving high performance, with accuracy scores that often exceed 90%. However, none of these studies focus on the real-world generalisability of the proposed approaches, that is whether the models perform well on datasets other than those on which they were initially trained and tested. In this work we aim to fill this gap by assessing the generalisability of top performing neural rumour verification models covering a range of different architectures from the perspectives of both topic and temporal robustness. For a more complete evaluation of generalisability, we collect and release COVID-RV, a novel dataset of Twitter conversations revolving around COVID-19 rumours. Unlike other existing COVID-19 datasets, our COVID-RV contains conversations around rumours that follow the format of prominent rumour verification benchmarks, while being different from them in terms of topic and time scale, thus allowing better assessment of the temporal robustness of the models. We evaluate model performance on COVID-RV and three popular rumour verification datasets to understand limitations and advantages of different model architectures, training datasets and evaluation scenarios. We find a dramatic drop in performance when testing models on a different dataset from that used for training. Further, we evaluate the ability of models to generalise in a few-shot learning setup, as well as when word embeddings are updated with the vocabulary of a new, unseen rumour. Drawing upon our experiments we discuss challenges and make recommendations for future research directions in addressing this important problem. 相似文献
12.
此次新冠肺炎疫情传播速度快、影响范围广、防控难度大,在没有特异性治疗药物和疫苗的情况下,我国通过最全面彻底的、以"非医疗干预"手段为主的防控举措,全社会动员,全民参与,科学防治、精准防治,有效遏制了本土疫情,取得了阶段性重要成效。在此次疫情防控中,我国各级公共卫生与疾病预防控制(以下简称"疾控")体系起到了重要作用,但也暴露出了诸多短板和不足。文章梳理了现阶段我国疾控体系、公共卫生法律法规建设、卫生应急管理政策保障3个方面存在的突出问题,提出了新时代疾控体系的职能任务发展、关键科研技术建设和专业人才资源储备3个方面的具体建议。 相似文献
13.
《Information processing & management》2022,59(1):102796
In the period of Corona Virus Disease 2019 (COVID-19), millions of people participate in the discussion of COVID-19 on the Internet, which can easily trigger public opinion and threaten social stability. This paper creatively proposes a multi-stage risk grading model of Internet public opinion for public health emergencies. On the basis of general public opinion risk grading analysis, the model continuously pays attention to the risk level of Internet public opinion based on the time scale of regular or major information updates. This model combines Analytic Hierarchy Process Sort II (AHPSort II) and Swing Weighting (SW) methods and proposes a new Multi-Criteria Decision Making (MCDM) method – AHPSort II-SW. Intuitionistic fuzzy number and linguistic fuzzy number are introduced into the model to evaluate the criteria that cannot be quantified. The multi-stage model is tested using more than 2,000 textual data about COVID-19 collected from Microblog, a leading social media platform in China. Seven public opinion risk assessments were conducted from January 23 to April 8, 2020. The empirical results show that in the early COVID-19 outbreak, the risk of public opinion is more serious on macroscopic view. In details, the risk of public opinion decreases slowly with time, but the emergence of important events may still increase the risk of public opinion. The analysis results are in line with the actual situation and verify the effectiveness of the method. Comparative analysis indicates the improved method is proved to be superior and effective, sensitivity analysis confirms its stability. Finally, management suggestions was provided, this study contributes to the literature on public opinion risk assessment and provides implications for practice. 相似文献
14.
Ethics and Information Technology - The COVID-19 pandemic has brought the long-standing public health practice of contact tracing into the public spotlight. While contact tracing and case... 相似文献
15.
《Information processing & management》2023,60(3):103287
In the context of the COVID-19 epidemic, a “double-hazard scenario” consisting of a natural disaster and a public health event occurring simultaneously is likely to arise. Focusing on this double-hazard scenario, this study developed a new opinion dynamics model that verifies the effect of opinion dynamic in practical applications and extends the realistic meaning of the logic matrix. The new model can be used to quickly identify changing trends in public opinion about two co-occurring public safety events in China, helping the government to better anticipate and respond to these real double-hazard scenarios. The new model was tested with three real double-hazard scenarios involving natural disasters and public health events in China and the simulation results were analyzed. Using visualization and Pearson correlation coefficients to analyze more than a million items of network-wide public opinion data, the new model was found to show a good fit with reality. The study finally found that in China, public attention to both natural hazards and public health events was greater when these public safety events co-occurred (double-hazard scenario) than when they occurred separately (single-hazard scenarios). These results verify the coupling phenomenon of different disasters in a multi-hazard scenario at the information level for the first time, which is greatly meaningful for multi-hazard research. 相似文献
16.
【目的/意义】突发公共卫生事件是公众关注的重要话题,极易引发网络信息泛滥和社会公众恐慌;了解公
共卫生舆情地区的差异,为舆情调控提出建议。【方法/过程】利用网络爬虫技术爬取新浪微博自2020年1月17日至
5月29日的COVID-19每日疫情通报博文下共计10余万条评论,运用情感分析和词频统计探讨地区舆情演变特征
及其原因,利用面板数据模型估计方法对网络舆情情感得分进行预测。【结果/结论】模型预测我国7个地区,14个
影响变量,样本记录 938条;整体的情感得分区间为(0.1,0.6);其中华北、华中、东北地区情感得分均值区间为(0.25,
0.35),而华东、华南、西南、西北地区的情感得分均值区间为(0.35,0.45);相关分析表明预测模型拟合具有统计学意义
（P<0.05,R2=0.65）。【创新/局限】基于COVID-19的网络舆情呈现出地理区域特性和时间特性,通过建模测度手段
对舆情进行监测,从而采取应对措施,但是还需考虑潜在因素的影响。 相似文献
17.
[目的/意义]政府开放数据质量是影响人们获取利用开放数据的重要因素，研究政府开放数据质量控制机制，对优化政府开放数据质量，促进公众更广泛地参与，实现政府开放数据宗旨具有重要意义。[方法/过程]本文运用演化博弈理论，构建了有限理性的政府开放数据提供者和使用者的复制动态模型，分析在不同的数据质量监管状态下博弈双方的进化稳定策略以及实现政府开放数据质量控制的均衡条件。[结果/结论]研究结果表明：高于一定阈值的监管激励能够有效控制政府开放数据质量，建立科学的数据质量评估标准和数据质量过滤机制，降低提供优质数据的额外成本以及完善数据发布机制有利于提高数据质量控制效率。 相似文献
18.
新冠肺炎疫情短期内严重冲击了我国生产供应,加大了国内产业链加速外移的风险。文章基于当前疫情导致我国短期生产与出口供给缺口的研究,结合我国在全球生产网络和亚洲生产链的枢纽位置,就疫情对全球产业链的冲击及其引发的我国产业链加速外移风险进行了剖析。研究结果表明,疫情引起的产能缺口将对全球生产体系产生冲击,跨国企业将加快生产链布局调整,需密切关注疫情造成部分产业链加速外移的风险。最后,就企业复工保障、疫情防控和产业链布局等方面提出相关政策建议。 相似文献
19.
2019年底暴发的新冠肺炎疫情是一次重大的公共卫生事件,它对我国乃至世界的社会经济都将产生巨大而深远的影响。遗憾的是,这样的事件已不再是百年一遇,而是十年一遇或更短的时间,因此如何有效地应对大规模传染性疾病的暴发是人类社会面临的十分重要而迫切的问题。这次新冠肺炎疫情既是一个不幸,也是一个难得的学习机会。通过对疫情防控过程的回顾,以及对在该过程中所遇到的一些困难的梳理,发现很多问题最终都可以归结为供需矛盾的问题,而供给侧的挑战通常又都源于供应链的无序和混乱。文章结合现代供应链管理理论的一些基本原理,对疫情防控工作进行了分析并给出了建议,如建立全国联网传染病监测中心、国家防控战略物资安全库存、国家突发事件应急管理指挥中心等。另外,疫后经济重建也至关重要,因此还从供应链思维的角度,分析了新冠肺炎疫情的行业影响,以及企业复工复产过程中应该注意的事项。 相似文献
20.