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Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification. 使用转换语言模型和食品和药物管理局的警告信检测含有大麻二酚相关COVID-19错误信息的推文:内容分析和识别。
Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2023-01-01 DOI: 10.2196/38390
Jason Turner, Mehmed Kantardzic, Rachel Vickers-Smith, Andrew G Brown

Background: COVID-19 has introduced yet another opportunity to web-based sellers of loosely regulated substances, such as cannabidiol (CBD), to promote sales under false pretenses of curing the disease. Therefore, it has become necessary to innovate ways to identify such instances of misinformation.

Objective: We sought to identify COVID-19 misinformation as it relates to the sales or promotion of CBD and used transformer-based language models to identify tweets semantically similar to quotes taken from known instances of misinformation. In this case, the known misinformation was the publicly available Warning Letters from Food and Drug Administration (FDA).

Methods: We collected tweets using CBD- and COVID-19-related terms. Using a previously trained model, we extracted the tweets indicating commercialization and sales of CBD and annotated those containing COVID-19 misinformation according to the FDA definitions. We encoded the collection of tweets and misinformation quotes into sentence vectors and then calculated the cosine similarity between each quote and each tweet. This allowed us to establish a threshold to identify tweets that were making false claims regarding CBD and COVID-19 while minimizing the instances of false positives.

Results: We demonstrated that by using quotes taken from Warning Letters issued by FDA to perpetrators of similar misinformation, we can identify semantically similar tweets that also contain misinformation. This was accomplished by identifying a cosine distance threshold between the sentence vectors of the Warning Letters and tweets.

Conclusions: This research shows that commercial CBD or COVID-19 misinformation can potentially be identified and curbed using transformer-based language models and known prior instances of misinformation. Our approach functions without the need for labeled data, potentially reducing the time at which misinformation can be identified. Our approach shows promise in that it is easily adapted to identify other forms of misinformation related to loosely regulated substances.

背景:COVID-19为大麻二酚(CBD)等监管宽松物质的网络卖家提供了另一个机会,以治疗疾病为幌子促进销售。因此,有必要创新识别此类错误信息的方法。目的:我们试图识别与CBD销售或推广相关的COVID-19错误信息,并使用基于转换器的语言模型来识别语义上类似于已知错误信息实例引用的推文。在这种情况下,已知的错误信息是食品和药物管理局(FDA)公开发布的警告信。方法:我们收集使用CBD和covid -19相关术语的推文。使用先前训练过的模型,我们提取了表明CBD商业化和销售的推文,并根据FDA的定义注释了那些包含COVID-19错误信息的推文。我们将推文和错误信息引用的集合编码成句子向量,然后计算每条引用和每条推文之间的余弦相似度。这使我们能够建立一个阈值,以识别关于CBD和COVID-19的虚假声明的推文,同时最大限度地减少误报的情况。结果:我们证明,通过使用FDA向类似错误信息的肇事者发出的警告信中的引用,我们可以识别语义上相似的推文,也包含错误信息。这是通过识别警告信和推文的句子向量之间的余弦距离阈值来实现的。结论:本研究表明,使用基于转换器的语言模型和已知的先前错误信息实例,可以识别和遏制商业CBD或COVID-19错误信息。我们的方法在不需要标记数据的情况下发挥作用,潜在地减少了识别错误信息的时间。我们的方法显示出希望,因为它很容易适应于识别与松散管制物质相关的其他形式的错误信息。
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引用次数: 0
Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study. 用词汇嵌入挖掘推特上COVID-19疫苗信念的趋势:纵向观察研究
Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2023-01-01 DOI: 10.2196/34315
Harshita Chopra, Aniket Vashishtha, Ridam Pal, Ananya Tyagi, Tavpritesh Sethi

Background: Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online.

Objective: This study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia.

Methods: We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories-emotions and influencing factors. Using cosine distance from selected seed words' embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks.

Results: Our findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41% to 39% in India. We also observed a significant change (P<.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42% of tweets coming from India and 45% of tweets from the United States represented the "vaccine_rollout" category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases.

Conclusions: By extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions.

背景:社交媒体在全球新闻传播中起着举足轻重的作用,它是人们就各种话题表达意见的平台。全球各地的COVID-19疫苗接种活动伴随着各种各样的观点,这些观点往往受到情绪的影响,随着病例的增加、疫苗的批准以及在线讨论的多种因素而变化。目的:本研究旨在分析印度、美国、巴西、英国和澳大利亚5个重要疫苗推广国家推文中不同情绪的时间演变及其影响因素。方法:提取近180万条与COVID-19疫苗接种相关的Twitter帖子语料库,创建2类词汇类别-情绪和影响因素。利用与选定种子词嵌入的余弦距离,我们扩展了每个类别的词汇量,并跟踪了每个国家从2020年6月到2021年4月的词汇强度的纵向变化。社区检测算法用于寻找正相关网络中的模块。结果:我们的研究结果表明,不同国家的情绪和影响因素之间存在不同的关系。在所有国家中,对疫苗表示犹豫的推文提到的与健康有关的影响最多,在印度从41%降至39%。我们还观察到一个显著的变化(p结论:通过提取和可视化这些推文,我们提出这样一个框架可能有助于指导有效疫苗运动的设计,并被政策制定者用来模拟疫苗摄取和有针对性的干预措施。
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引用次数: 8
Potential Impact of the COVID-19 Pandemic on Public Perception of Water Pipes on Reddit: Observational Study. COVID-19大流行对Reddit上公众对水管认知的潜在影响:观察性研究。
Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2023-01-01 DOI: 10.2196/40913
Zihe Zheng, Zidian Xie, Maciej Goniewicz, Irfan Rahman, Dongmei Li

Background: Socializing is one of the main motivations for water pipe smoking. Restrictions on social gatherings during the COVID-19 pandemic might have influenced water pipe smokers' behaviors. As one of the most popular social media platforms, Reddit has been used to study public opinions and user experiences.

Objective: In this study, we aimed to examine the influence of the COVID-19 pandemic on public perception and discussion of water pipe tobacco smoking using Reddit data.

Methods: We collected Reddit posts between December 1, 2018, and June 30, 2021, from a Reddit archive (PushShift) using keywords such as "waterpipe," "hookah," and "shisha." We examined the temporal trend in Reddit posts mentioning water pipes and different locations (such as homes and lounges or bars). The temporal trend was further tested using interrupted time series analysis. Sentiment analysis was performed to study the change in sentiment of water pipe-related posts before and during the pandemic. Topic modeling using latent Dirichlet allocation (LDA) was used to examine major topics discussed in water pipe-related posts before and during the pandemic.

Results: A total of 45,765 nonpromotion water pipe-related Reddit posts were collected and used for data analysis. We found that the weekly number of Reddit posts mentioning water pipes significantly increased at the beginning of the COVID-19 pandemic (P<.001), and gradually decreased afterward (P<.001). In contrast, Reddit posts mentioning water pipes and lounges or bars showed an opposite trend. Compared to the period before the COVID-19 pandemic, the average number of Reddit posts mentioning lounges or bars was lower at the beginning of the pandemic but gradually increased afterward, while the average number of Reddit posts mentioning the word "home" remained similar during the COVID-19 pandemic (P=.29). While water pipe-related posts with a positive sentiment were dominant (12,526/21,182, 59.14% before the pandemic; 14,686/24,583, 59.74% after the pandemic), there was no change in the proportion of water pipe-related posts with different sentiments before and during the pandemic (P=.19, P=.26, and P=.65 for positive, negative, and neutral posts, respectively). Most topics related to water pipes on Reddit were similar before and during the pandemic. There were more discussions about the opening and closing of hookah lounges or bars during the pandemic.

Conclusions: This study provides a first evaluation of the possible impact of the COVID-19 pandemic on public perceptions of and discussions about water pipes on Reddit.

背景:社交是吸烟的主要动机之一。新冠肺炎疫情期间对社交聚会的限制可能对水烟吸烟者的行为产生了影响。作为最受欢迎的社交媒体平台之一,Reddit一直被用来研究民意和用户体验。目的:在本研究中,我们旨在利用Reddit数据研究COVID-19大流行对公众对水烟吸烟的认知和讨论的影响。方法:我们从Reddit存档(PushShift)中收集了2018年12月1日至2021年6月30日期间的Reddit帖子,使用关键词如“水管”、“水烟”和“水烟”。我们研究了Reddit上提到水管和不同地点(比如家里、休息室或酒吧)的帖子的时间趋势。使用中断时间序列分析进一步检验时间趋势。进行情绪分析,研究大流行前和大流行期间水管相关帖子情绪的变化。使用潜在狄利克雷分配(LDA)的主题建模来检查大流行之前和期间在水管相关帖子中讨论的主要主题。结果:共收集了45,765条与水管相关的非推广Reddit帖子并用于数据分析。我们发现,在COVID-19大流行开始时,Reddit上每周提到水管的帖子数量显著增加(PPP= 0.29)。而积极情绪的水管相关帖子占主导地位(12,526/21,182,大流行前为59.14%;(14,686/24,583,大流行后59.74%),在大流行前和大流行期间,不同情绪的水管相关岗位所占比例没有变化(P=。19日,P =。26, P=。正面、负面和中性职位分别为65)。在大流行之前和期间,Reddit上与水管相关的大多数话题都是相似的。在大流行期间,关于水烟休息室或酒吧的开放和关闭有更多的讨论。结论:本研究首次评估了COVID-19大流行对Reddit上公众对水管的看法和讨论可能产生的影响。
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引用次数: 2
Analyzing Discussions Around Rural Health on Twitter During the COVID-19 Pandemic: Social Network Analysis of Twitter Data. COVID-19大流行期间Twitter上关于农村卫生的讨论分析:Twitter数据的社会网络分析
Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2023-01-01 DOI: 10.2196/39209
Wasim Ahmed, Josep Vidal-Alaball, Josep Maria Vilaseca Llobet

Background: Individuals from rural areas are increasingly using social media as a means of communication, receiving information, or actively complaining of inequalities and injustices.

Objective: The aim of our study is to analyze conversations about rural health taking place on Twitter during a particular phase of the COVID-19 pandemic.

Methods: This study captured 57 days' worth of Twitter data related to rural health from June to August 2021, using English-language keywords. The study used social network analysis and natural language processing to analyze the data.

Results: It was found that Twitter served as a fruitful platform to raise awareness of problems faced by users living in rural areas. Overall, Twitter was used in rural areas to express complaints, debate, and share information.

Conclusions: Twitter could be leveraged as a powerful social listening tool for individuals and organizations that want to gain insight into popular narratives around rural health.

背景:来自农村地区的个人越来越多地使用社交媒体作为沟通、接收信息或积极抱怨不平等和不公正的手段。目的:我们研究的目的是分析在COVID-19大流行的特定阶段在Twitter上发生的关于农村卫生的对话。方法:本研究使用英语关键词捕获了2021年6月至8月期间与农村健康相关的57天Twitter数据。该研究使用社会网络分析和自然语言处理来分析数据。结果:发现Twitter是一个富有成效的平台,可以提高农村地区用户对所面临问题的认识。总的来说,Twitter在农村地区被用来表达抱怨、辩论和分享信息。结论:Twitter可以作为一个强大的社会倾听工具,帮助个人和组织深入了解农村健康的流行叙事。
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引用次数: 0
The Quality, Readability, and Accuracy of the Information on Google About Cannabis and Driving: Quantitative Content Analysis 关于大麻和驾驶的谷歌信息的质量、可读性和准确性:定量内容分析
Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2022-09-27 DOI: 10.2196/43001
Maria Josey, Dina Gaid, Lisa D. Bishop, Michael Blackwood, M. Najafizada, Jennifer R. Donnan
Background The public perception of driving under the influence of cannabis (DUIC) is not consistent with current evidence. The internet is an influential source of information available for people to find information about cannabis. Objective The purpose of this study was to assess the quality, readability, and accuracy of the information about DUIC found on the internet using the Google Canada search engine. Methods A quantitative content analysis of the top Google search web pages was conducted to analyze the information available to the public about DUIC. Google searches were performed using keywords, and the first 20 pages were selected. Web pages or web-based resources were eligible if they had text on cannabis and driving in English. We assessed (1) the quality of information using the Quality Evaluation Scoring Tool (QUEST) and the presence of the Health on the Net (HON) code; (2) the readability of information using the Gunning Fox Index (GFI), Flesch Reading Ease Scale (FRES), Flesch-Kincaid Grade Level (FKGL), and Simple Measure of Gobbledygook (SMOG) scores; and (3) the accuracy of information pertaining to the effects of cannabis consumption, prevalence of DUIC, DUIC effects on driving ability, risk of collision, and detection by law enforcement using an adapted version of the 5Cs website evaluation tool. Results A total of 82 web pages were included in the data analysis. The average QUEST score was 17.4 (SD 5.6) out of 28. The average readability scores were 9.7 (SD 2.3) for FKGL, 11.4 (SD 2.9) for GFI, 12.2 (SD 1.9) for SMOG index, and 49.9 (SD 12.3) for FRES. The readability scores demonstrated that 8 (9.8%) to 16 (19.5%) web pages were considered readable by the public. The accuracy results showed that of the web pages that presented information on each key topic, 96% (22/23) of them were accurate about the effects of cannabis consumption; 97% (30/31) were accurate about the prevalence of DUIC; 92% (49/53) were accurate about the DUIC effects on driving ability; 80% (41/51) were accurate about the risk of collision; and 71% (35/49) were accurate about detection by law enforcement. Conclusions Health organizations should consider health literacy of the public when creating content to help prevent misinterpretation and perpetuate prevailing misperceptions surrounding DUIC. Delivering high quality, readable, and accurate information in a way that is comprehensible to the public is needed to support informed decision-making.
背景公众对大麻影响下驾驶的看法与目前的证据不一致。互联网是人们寻找大麻信息的一个有影响力的信息来源。目的本研究旨在评估使用谷歌加拿大搜索引擎在互联网上发现的DUIC信息的质量、可读性和准确性。方法对谷歌热门搜索网页进行定量内容分析,分析公众可获得的DUIC信息。谷歌使用关键词进行搜索,并选择了前20个页面。如果网页或网络资源中有关于大麻和驾驶的英文文本,则符合条件。我们评估了(1)使用质量评估评分工具(QUEST)的信息质量和网络健康(HON)代码的存在;(2) 使用Gunning Fox指数(GFI)、Flesch阅读能力量表(FRES)、Fletch-Kincaid等级水平(FKGL)和Gobbledygouk简单测量(SMOG)分数的信息可读性;以及(3)与大麻消费的影响、酒后驾车的流行率、酒后驾车对驾驶能力的影响、碰撞风险以及执法部门使用5Cs网站评估工具的检测有关的信息的准确性。结果共有82个网页被纳入数据分析。QUEST平均得分为17.4(标准差5.6)(满分28分)。FKGL的平均可读性得分为9.7(SD 2.3),GFI为11.4(SD 2.9),SMOG指数为12.2(SD 1.9),FRES为49.9(SD 12.3)。可读性得分表明,公众认为8个(9.8%)至16个(19.5%)网页可读。准确性结果显示,在提供每个关键主题信息的网页中,96%(22/23)的网页对大麻消费的影响是准确的;97%(30/31)的DUIC患病率准确;酒后驾车对驾驶能力的影响准确率为92%(49/53);80%(41/51)对碰撞风险准确;71%(35/49)的人对执法部门的检测准确。结论卫生组织在创作内容时应考虑公众的健康素养,以防止误解和延续围绕酒后驾车的普遍误解。需要以公众能够理解的方式提供高质量、可读和准确的信息,以支持知情决策。
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引用次数: 0
Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights. 疫苗强制接种言论中的情绪和不礼貌行为:自然语言处理的启示。
IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2022-09-13 eCollection Date: 2022-07-01 DOI: 10.2196/37635
Hannah Stevens, Muhammad Ehab Rasul, Yoo Jung Oh
<p><strong>Background: </strong>Despite vaccine availability, vaccine hesitancy has inhibited public health officials' efforts to mitigate the COVID-19 pandemic in the United States. Although some US elected officials have responded by issuing vaccine mandates, others have amplified vaccine hesitancy by broadcasting messages that minimize vaccine efficacy. The politically polarized nature of COVID-19 information on social media has given rise to incivility, wherein health attitudes often hinge more on political ideology than science.</p><p><strong>Objective: </strong>To the best of our knowledge, incivility has not been studied in the context of discourse regarding COVID-19 vaccines and mandates. Specifically, there is little focus on the psychological processes that elicit uncivil vaccine discourse and behaviors. Thus, we investigated 3 psychological processes theorized to predict discourse incivility-namely, anxiety, anger, and sadness.</p><p><strong>Methods: </strong>We used 2 different natural language processing approaches: (1) the Linguistic Inquiry and Word Count computational tool and (2) the Google Perspective application programming interface (API) to analyze a data set of 8014 tweets containing terms related to COVID-19 vaccine mandates from September 14, 2021, to October 1, 2021. To collect the tweets, we used the Twitter API Tweet Downloader Tool (version 2). Subsequently, we filtered through a data set of 375,000 vaccine-related tweets using keywords to extract tweets explicitly focused on vaccine mandates. We relied on the Linguistic Inquiry and Word Count computational tool to measure the valence of linguistic anger, sadness, and anxiety in the tweets. To measure dimensions of post incivility, we used the Google Perspective API.</p><p><strong>Results: </strong>This study resolved discrepant operationalizations of incivility by introducing incivility as a multifaceted construct and explored the distinct emotional processes underlying 5 dimensions of discourse incivility. The findings revealed that 3 types of emotions-anxiety, anger, and sadness-were uniquely associated with dimensions of incivility (eg, toxicity, severe toxicity, insult, profanity, threat, and identity attacks). Specifically, the results showed that anger was significantly positively associated with all dimensions of incivility (all <i>P</i><.001), whereas sadness was significantly positively related to threat (<i>P</i>=.04). Conversely, anxiety was significantly negatively associated with identity attack (<i>P</i>=.03) and profanity (<i>P</i>=.02).</p><p><strong>Conclusions: </strong>The results suggest that our multidimensional approach to incivility is a promising alternative to understanding and intervening in the psychological processes underlying uncivil vaccine discourse. Understanding specific emotions that can increase or decrease incivility such as anxiety, anger, and sadness can enable researchers and public health professionals to develop effective inte
背景:尽管有疫苗可用,但对疫苗的犹豫不决阻碍了公共卫生官员缓解 COVID-19 在美国大流行的努力。尽管一些美国民选官员已通过发布疫苗强制令作出回应,但其他一些官员则通过广播最大限度地降低疫苗功效的信息来放大疫苗犹豫不决的情绪。社交媒体上 COVID-19 信息的政治两极化性质引发了不文明现象,人们对健康的态度往往更多地取决于政治意识形态而非科学:据我们所知,在有关 COVID-19 疫苗和任务的讨论中,尚未对不文明行为进行研究。具体而言,人们很少关注引发不文明疫苗言论和行为的心理过程。因此,我们研究了理论上可预测不文明言论的 3 个心理过程,即焦虑、愤怒和悲伤:我们使用了两种不同的自然语言处理方法:(1)语言调查和字数统计计算工具;(2)Google Perspective 应用程序编程接口 (API),对 2021 年 9 月 14 日至 2021 年 10 月 1 日期间包含 COVID-19 疫苗规定相关术语的 8014 条推文数据集进行了分析。为了收集推文,我们使用了 Twitter API 推文下载工具(第 2 版)。随后,我们使用关键字过滤了 375,000 条与疫苗相关的推文数据集,提取出明确关注疫苗接种规定的推文。我们利用 "语言调查和字数统计"(Linguistic Inquiry and Word Count)计算工具来测量推文中愤怒、悲伤和焦虑的语言情绪。为了测量帖子中的不文明行为,我们使用了 Google Perspective API:本研究通过将不文明行为作为一个多层面的概念引入,解决了不文明行为在操作上的差异,并探索了话语不文明行为 5 个维度背后的不同情绪过程。研究结果显示,焦虑、愤怒和悲伤这三种情绪与不文明行为的维度(如毒性、严重毒性、侮辱、亵渎、威胁和身份攻击)有着独特的关联。具体来说,研究结果表明,愤怒与不文明行为的所有维度都有显著的正相关(PP=0.04)。相反,焦虑与身份攻击(P=.03)和亵渎(P=.02)明显负相关:结果表明,我们的多维不文明行为研究方法是了解和干预不文明疫苗言论背后的心理过程的一种很有前途的替代方法。了解焦虑、愤怒和悲伤等会增加或减少不文明行为的特定情绪,可以帮助研究人员和公共卫生专业人员针对不文明疫苗言论制定有效的干预措施。鉴于需要对网络上传播的健康信息和错误信息进行实时监控和自动响应,社交媒体平台可以利用谷歌视角应用程序接口(Google Perspective API),在检测到不文明评论时向用户提供即时的自动反馈。
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引用次数: 0
COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic. COVID-19 误报检测:机器学习信息解决方案。
IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2022-08-25 eCollection Date: 2022-07-01 DOI: 10.2196/38756
Nikhil Kolluri, Yunong Liu, Dhiraj Murthy
<p><strong>Background: </strong>The volume of COVID-19-related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning-based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19-related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed.</p><p><strong>Objective: </strong>The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response.</p><p><strong>Methods: </strong>We evaluated three strategies for training a machine-learning model to determine the highest model performance: (1) COVID-19-related fact-checked data only, (2) general fact-checked data only, and (3) combined COVID-19 and general fact-checked data. We created two COVID-19-related misinformation data sets from fact-checked "false" content combined with programmatically retrieved "true" content. The first set contained ~7000 entries from July to August 2020, and the second contained ~31,000 entries from January 2020 to June 2022. We crowdsourced 31,441 votes to human label the first data set.</p><p><strong>Results: </strong>The models achieved an accuracy of 96.55% and 94.56% on the first and second external validation data set, respectively. Our best-performing model was developed using COVID-19-specific content. We were able to successfully develop combined models that outperformed human votes of misinformation. Specifically, when we blended our model predictions with human votes, the highest accuracy we achieved on the first external validation data set was 99.1%. When we considered outputs where the machine-learning model agreed with human votes, we achieved accuracies up to 98.59% on the first validation data set. This outperformed human votes alone with an accuracy of only 73%.</p><p><strong>Conclusions: </strong>External validation accuracies of 96.55% and 94.56% are evidence that machine learning can produce superior results for the difficult task of classifying the veracity of COVID-19 content. Pretrained language models performed best when fine-tuned on a topic-specific data set, while other models achieved their best accuracy when fine-tuned on a combination of topic-specific and general-topic data sets. Crucially, our study found that blended models, trained/fine-tuned on general-topic content with crowdsourced data, improved our models' accuracies up to 99.7%. The successful use of crowdsourced data can increase the accuracy of models in situations when expert-labeled data are scarce. The 98.59% accuracy on a "high-confidence" subsection comprised of machine-learned and human labels sugges
背景:与 COVID-19 相关的虚假信息数量之大,早已超出了事实核查人员可用的资源,无法有效减轻其不良影响。自动化和基于网络的方法可以有效遏制网络误报。基于机器学习的方法已经在文本分类任务中取得了优异的成绩,包括潜在的低质量新闻可信度评估。尽管最初的快速干预措施取得了进展,但与 COVID-19 相关的大量错误信息仍然让事实核查人员不堪重负。因此,迫切需要改进自动和机器学习方法,以应对信息瘟疫:本研究的目的是改进自动和机器学习方法,以应对信息瘟疫:我们评估了训练机器学习模型的三种策略,以确定最高的模型性能:(1) 仅使用 COVID-19 相关事实校验数据,(2) 仅使用一般事实校验数据,(3) 结合 COVID-19 和一般事实校验数据。我们创建了两个与 COVID-19 相关的错误信息数据集,这些数据集由经过事实核查的 "虚假 "内容和通过程序检索的 "真实 "内容组成。第一个数据集包含 2020 年 7 月至 8 月的约 7000 个条目,第二个数据集包含 2020 年 1 月至 2022 年 6 月的约 31000 个条目。我们通过众包获得了 31,441 张选票,对第一组数据进行了人工标注:在第一个和第二个外部验证数据集上,模型的准确率分别达到 96.55% 和 94.56%。我们使用 COVID-19 的特定内容开发了表现最佳的模型。我们成功地开发出了组合模型,其表现优于对错误信息的人工投票。具体来说,当我们将模型预测与人工投票相结合时,我们在第一个外部验证数据集上达到的最高准确率为 99.1%。当我们考虑机器学习模型与人工投票一致的输出时,我们在第一个验证数据集上的准确率高达 98.59%。这超过了仅有 73% 的人工投票准确率:96.55%和94.56%的外部验证准确率证明,机器学习可以在对COVID-19内容的真实性进行分类这一艰巨任务中取得优异成绩。预训练的语言模型在特定主题数据集上进行微调时表现最佳,而其他模型在特定主题数据集和一般主题数据集的组合上进行微调时则达到最佳准确率。重要的是,我们的研究发现,在一般主题内容与众包数据上训练/微调的混合模型可将我们模型的准确率提高到 99.7%。在缺乏专家标签数据的情况下,成功使用众包数据可以提高模型的准确性。由机器学习标签和人工标签组成的 "高置信度 "分节的准确率为 98.59%,这表明众包投票可以优化机器学习标签,从而将准确率提高到纯人工水平之上。这些结果支持了监督机器学习在阻止和打击未来健康相关虚假信息方面的实用性。
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引用次数: 0
Media Data and Vaccine Hesitancy: Scoping Review. 媒体数据和疫苗犹豫:范围审查。
IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2022-08-10 eCollection Date: 2022-07-01 DOI: 10.2196/37300
Jason Dean-Chen Yin
<p><strong>Background: </strong>Media studies are important for vaccine hesitancy research, as they analyze how the media shapes risk perceptions and vaccine uptake. Despite the growth in studies in this field owing to advances in computing and language processing and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology.</p><p><strong>Objective: </strong>This review aimed to identify and illustrate the media platforms and methods used to study vaccine hesitancy and how they build or contribute to the study of the media's influence on vaccine hesitancy and public health.</p><p><strong>Methods: </strong>This study followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A search was conducted on PubMed and Scopus for any studies that used media data (social media or traditional media), had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, or stance), were written in English, and were published after 2010. Studies were screened by only 1 reviewer and extracted for media platform, analysis method, the theoretical models used, and outcomes.</p><p><strong>Results: </strong>In total, 125 studies were included, of which 71 (56.8%) used traditional research methods and 54 (43.2%) used computational methods. Of the traditional methods, most used content analysis (43/71, 61%) and sentiment analysis (21/71, 30%) to analyze the texts. The most common platforms were newspapers, print media, and web-based news. The computational methods mostly used sentiment analysis (31/54, 57%), topic modeling (18/54, 33%), and network analysis (17/54, 31%). Fewer studies used projections (2/54, 4%) and feature extraction (1/54, 2%). The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. The following five major categories of studies arose: antivaccination themes centered on the distrust of institutions, civil liberties, misinformation, conspiracy theories, and vaccine-specific concerns; provaccination themes centered on ensuring vaccine safety using scientific literature; framing being important and health professionals and personal stories having the largest impact on shaping vaccine opinion; the coverage of vaccination-related data mostly identifying negative vaccine content and revealing deeply fractured vaccine communities and echo chambers; and the public reacting to and focusing on certain signals-in particular cases, deaths, and scandals-which suggests a more volatile period for the spread of information.</p><p><strong>Conclusions: </strong>The heterogeneity in the use of media to study vaccines can be better consolidated through theoretical grounding. Areas of suggested research include understanding how trust in institutions is asso
背景:媒体研究对疫苗犹豫研究很重要,因为它们分析媒体如何塑造风险认知和疫苗摄取。尽管由于计算和语言处理的进步以及社交媒体的不断扩大,这一领域的研究有所增加,但没有一项研究巩固了用于研究疫苗犹豫的方法学方法。综合这些信息可以更好地构建并为数字流行病学这一不断发展的分支领域树立先例。目的:本综述旨在确定和说明用于研究疫苗犹豫的媒体平台和方法,以及它们如何构建或促进媒体对疫苗犹豫和公共卫生的影响的研究。方法:本研究遵循PRISMA-ScR(首选报告项目的系统评价和荟萃分析扩展范围评价)指南。我们在PubMed和Scopus上检索了所有使用媒体数据(社交媒体或传统媒体)、结果与疫苗情绪(意见、接受、犹豫、接受或立场)相关、以英文撰写并在2010年之后发表的研究。研究仅由1位审稿人筛选,并根据媒体平台、分析方法、使用的理论模型和结果进行提取。结果:共纳入125篇研究,其中传统研究方法71篇(56.8%),计算方法54篇(43.2%)。在传统的文本分析方法中,主要采用内容分析(43/ 71,61 %)和情感分析(21/ 71,30 %)。最常见的平台是报纸、印刷媒体和网络新闻。计算方法主要采用情感分析(31/ 54,57 %)、主题建模(18/ 54,33 %)和网络分析(17/ 54,31 %)。较少的研究使用投影(2/ 54,4%)和特征提取(1/ 54,2%)。最常见的平台是Twitter和Facebook。从理论上讲,大多数研究都很薄弱。出现了以下五个主要类别的研究:反疫苗主题集中在对机构、公民自由、错误信息、阴谋论和疫苗特定问题的不信任;以利用科学文献确保疫苗安全为中心的预防接种主题;框架很重要,卫生专业人员和个人故事对形成疫苗意见影响最大;疫苗接种相关数据的覆盖范围主要是确定阴性疫苗内容并揭示严重断裂的疫苗社区和回声室;公众对某些信号的反应和关注——在特殊情况下,死亡和丑闻——表明信息传播的更不稳定时期。结论:通过理论铺垫,可以更好地巩固疫苗使用介质的异质性。建议的研究领域包括了解对机构的信任如何与疫苗接种相关联,错误信息和信息信号如何影响疫苗接种,以及评估政府关于疫苗推广和疫苗相关事件的信息通报。该综述以一项声明结束,即媒体数据分析虽然在方法上具有开创性,但应该补充——而不是取代——公共卫生研究中的现行做法。
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引用次数: 0
Web-Based Perspectives of Deemed Consent Organ Donation Legislation in Nova Scotia: Thematic Analysis of Commentary in Facebook Groups. 新斯科舍省器官捐赠立法的网络视角:Facebook群组评论的专题分析。
Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2022-07-01 DOI: 10.2196/38242
Alessandro R Marcon, Darren N Wagner, Carly Giles, Cynthia Isenor

Background: The Canadian province of Nova Scotia recently became the first jurisdiction in North America to implement deemed consent organ donation legislation. Changing the consent models constituted one aspect of a larger provincial program to increase organ and tissue donation and transplantation rates. Deemed consent legislation can be controversial among the public, and public participation is integral to the successful implementation of the program.

Objective: Social media constitutes key spaces where people express opinions and discuss topics, and social media discourse can influence public perceptions. This project aimed to examine how the public in Nova Scotia responded to legislative changes in Facebook groups.

Methods: Using Facebook's search engine, we searched for posts in public Facebook groups using the terms "deemed consent," "presumed consent," "opt out," or "organ donation" and "Nova Scotia," appearing from January 1, 2020, to May 1, 2021. The finalized data set included 2337 comments on 26 relevant posts in 12 different public Nova Scotia-based Facebook groups. We conducted thematic and content analyses of the comments to determine how the public responded to the legislative changes and how the participants interacted with one another in the discussions.

Results: Our thematic analysis revealed principal themes that supported and critiqued the legislation, raised specific issues, and reflected on the topic from a neutral perspective. Subthemes showed individuals presenting perspectives through a variety of themes, including compassion, anger, frustration, mistrust, and a range of argumentative tactics. The comments included personal narratives, beliefs about the government, altruism, autonomy, misinformation, and reflections on religion and death. Content analysis revealed that Facebook users reacted to popular comments with "likes" more than other reactions. Comments with the most reactions included both negative and positive perspectives about the legislation. Personal donation and transplantation success stories, as well as attempts to correct misinformation, were some of the most "liked" positive comments.

Conclusions: The findings provide key insights into perspectives of individuals from Nova Scotia on deemed consent legislation, as well as organ donation and transplantation broadly. The insights derived from this analysis can contribute to public understanding, policy creation, and public outreach efforts that might occur in other jurisdictions considering the enactment of similar legislation.

背景:加拿大新斯科舍省最近成为北美第一个实施视为同意器官捐赠立法的司法管辖区。改变同意模式是一个更大的省级计划的一个方面,以提高器官和组织捐赠和移植率。被视为同意的立法可能在公众中引起争议,公众参与是该计划成功实施的组成部分。目的:社交媒体是人们表达意见和讨论话题的关键空间,社交媒体话语可以影响公众的看法。这个项目旨在研究新斯科舍省的公众如何回应Facebook群组的立法变化。方法:使用Facebook的搜索引擎,我们搜索了2020年1月1日至2021年5月1日期间出现在Facebook公共群组中使用“视为同意”、“推定同意”、“选择退出”或“器官捐赠”和“新斯科舍省”等术语的帖子。最终确定的数据集包括对新斯科舍省12个不同公共Facebook群组中26个相关帖子的2337条评论。我们对意见进行主题和内容分析,以确定公众对立法改革的反应,以及与会者在讨论中如何相互作用。结果:我们的专题分析揭示了支持和批评立法的主要主题,提出了具体问题,并从中立的角度对主题进行了反思。副主题展示了个人通过各种主题表达观点,包括同情、愤怒、沮丧、不信任和一系列争论策略。这些评论包括个人叙述、对政府的信念、利他主义、自治、错误信息以及对宗教和死亡的反思。内容分析显示,Facebook用户对热门评论的“喜欢”反应多于其他反应。反应最多的评论包括对立法的负面和正面看法。个人捐赠和移植的成功故事,以及纠正错误信息的努力,是最受“喜欢”的积极评论。结论:研究结果为新斯科舍省个人对视为同意立法以及广泛的器官捐赠和移植的看法提供了关键见解。从这一分析中得出的见解可以有助于其他考虑制定类似立法的司法管辖区的公众理解、政策制定和公众宣传工作。
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引用次数: 1
Confounding Effect of Undergraduate Semester-Driven "Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration. 本科学期驱动的“学术”互联网搜索对在Google趋势数据中检测真实疾病季节性的能力的混淆效应:傅立叶滤波方法的开发和演示。
Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2022-07-01 DOI: 10.2196/34464
Timber Gillis, Scott Garrison
<p><strong>Background: </strong>Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year-driven internet search patterns of health care students.</p><p><strong>Objective: </strong>This study aimed to (1) demonstrate that artificial "academic cycling" of Google Trends' search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples.</p><p><strong>Methods: </strong>We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook.</p><p><strong>Results: </strong>Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term ["Staphylococcus"], for which academic cycling explained 73.8% of the variability in search volume (using the squared Spearman rank correlation coefficient, <i>P</i><.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) ["Aeromonas" + "Plesiomonas"] (nosocomial infections that were searched for more frequently during the summer), (2) ["Ehrlichia"] (a tick-borne pathogen that was searched for more frequently during late spring), (3) ["Moraxella"] and ["Haemophilus"] (respiratory infections that were searched for more frequently during late winter), (4) ["Legionella"] (searched for more frequently during midsummer), and (5) ["Vibrio"] (which spiked for 2 months during midsummer). The terms ["myocardial infarction"] and ["hypertension"] lacked any obvious seasonal cycling after filtering, whereas ["depression"] maintained an annual cycling pattern.</p><p><strong>Conclusions: </strong>Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends' internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to f
背景:谷歌趋势(Google Trends)追踪的互联网医疗信息搜索量已被用于证明各种医疗状况的症状负担中意想不到的季节性。然而,当使用更多的专业医学语言(例如诊断)时,我们认为这种技术被循环的、学年驱动的医疗保健学生的互联网搜索模式所混淆。目的:本研究旨在(1)证明谷歌趋势搜索量的人为“学术循环”存在于许多医疗保健术语中,(2)证明如何使用信号处理技术从谷歌趋势数据中过滤学术循环,以及(3)将这种过滤技术应用于一些临床相关的例子。方法:我们获得了各种学术术语的Google Trends搜索量数据,显示出很强的学术循环,并使用傅里叶分析技术(1)在一个特别强的例子中识别出这种调制模式的频域指纹,(2)从原始数据中过滤出该模式。在这个说述性的例子之后,我们将同样的过滤技术应用于互联网搜索有关3种被认为具有真正季节性调节的医学病症(心肌梗死、高血压和抑郁症)的信息,以及一本普通医学微生物学教科书中的所有细菌属术语。结果:学术循环解释了许多以技术为导向的搜索词的互联网搜索量的季节性变化,包括细菌属术语[“葡萄球菌”],其中学术循环解释了搜索量变化的73.8%(使用平方Spearman秩相关系数,p)。虽然使用谷歌趋势的互联网搜索量和适合的搜索词来搜索医疗状况的季节性变化是合理的,但更多技术搜索词的变化可能是由医疗保健专业的学生驱动的,他们的搜索频率随着学年的变化而变化。在这种情况下,使用傅立叶分析来过滤掉学术周期是确定是否存在额外季节性的潜在手段。
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引用次数: 1
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JMIR infodemiology
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