Catherine S W Albin, Tianwen Ma, Gabriela F Pucci, Aaron S Zelikovich, Eric C Lawson, Neil Dhruva, Simone Masiero, Aarti Sarwal, Neha S Dangayach, Aaron L Berkowitz, Nicholas A Morris, Lyell K Jones
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Tweetorial and author characteristics were collected from X and by viewing the author's academic profile. We created and validated a novel formula to determine the tweetorial's \"X Factor\" (XF), a measure of reader engagement and distribution, reflecting reposts and likes. Each tweetorial was analyzed for basic variables, the author's academic rank, and thematic content. Each first post underwent a language analysis using Linguistic Inquiry and Word Count (LIWC-22) and was hand-coded for style (such as \"statement\" or \"clinical case\"). We determined each covariate's impact on XF. The general estimating equation was applied to correct for the author effect.</p><p><strong>Results: </strong>We identified 392 neurology-themed tweetorials posted by 96 unique authors. XF strongly correlated with impressions (<i>R</i> <sup>2</sup> = 0.85) and was validated in a separate data set (<i>R</i> <sup>2</sup> = 0.74). The median XF of the tweetorials was 28.5K (interquartile range 12.7K-61.5K). Tweetorials about a \"General Neurology Topic\" and with a \"Clearly Stated Topic\" had 48% and 49% higher XF than those without (<i>p</i> = 0.001 and 0.006, 95% CI 17%-88%, 12%-97%, respectively). Having a \"creative\" first post, including a unique hashtag, and featuring an author-made graphic correlated with 60%, 49%, and 84% higher XF than posts without those elements (<i>p</i> = 0.01,95% CI 13%-125%, <i>p</i> < 0.001, 95% CI 16%-92%, <i>p</i> < 0.001, 95% CI 30%-164%, respectively). Continuing medical education (CME) accreditation and higher scores on \"positive tone\" negatively affected XF (-80%, <i>p</i> < 0.001, 95% CI 70%-86% and -7%/point of positivity, <i>p</i> < 0.001, 95% CI 2%-10%, respectively).</p><p><strong>Discussion: </strong>Tweetorial engagement and distribution are determined by multiple factors including authorship, clarity of the topic, and visual appeal of the post. CME accreditation was strongly negatively associated with sharing and may reflect a sharing preference for personal accounts over institutional ones, although further study is needed.</p>","PeriodicalId":520085,"journal":{"name":"Neurology. Education","volume":"3 4","pages":"00"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11744482/pdf/","citationCount":"0","resultStr":"{\"title\":\"Education Research: Making a Tweetorial Fly: Features of Educational Social Media Posts Associated With High Sharing and Engagement.\",\"authors\":\"Catherine S W Albin, Tianwen Ma, Gabriela F Pucci, Aaron S Zelikovich, Eric C Lawson, Neil Dhruva, Simone Masiero, Aarti Sarwal, Neha S Dangayach, Aaron L Berkowitz, Nicholas A Morris, Lyell K Jones\",\"doi\":\"10.1212/NE9.0000000000200160\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and objectives: </strong>Social media platforms such as X (formerly Twitter) are increasingly used in medical education. Characteristics of tweetorials (threaded teaching posts) associated with higher degrees of engagement are unknown. We sought to understand features of neurology-themed tweetorials associated with high sharing and engagement.</p><p><strong>Methods: </strong>We created a neurology-themed tweetorials data set by searching \\\"tweetorial\\\" AND \\\"neurology\\\" on X that were posted between November 2018 and December 2022. Tweetorial and author characteristics were collected from X and by viewing the author's academic profile. We created and validated a novel formula to determine the tweetorial's \\\"X Factor\\\" (XF), a measure of reader engagement and distribution, reflecting reposts and likes. Each tweetorial was analyzed for basic variables, the author's academic rank, and thematic content. Each first post underwent a language analysis using Linguistic Inquiry and Word Count (LIWC-22) and was hand-coded for style (such as \\\"statement\\\" or \\\"clinical case\\\"). We determined each covariate's impact on XF. The general estimating equation was applied to correct for the author effect.</p><p><strong>Results: </strong>We identified 392 neurology-themed tweetorials posted by 96 unique authors. XF strongly correlated with impressions (<i>R</i> <sup>2</sup> = 0.85) and was validated in a separate data set (<i>R</i> <sup>2</sup> = 0.74). The median XF of the tweetorials was 28.5K (interquartile range 12.7K-61.5K). Tweetorials about a \\\"General Neurology Topic\\\" and with a \\\"Clearly Stated Topic\\\" had 48% and 49% higher XF than those without (<i>p</i> = 0.001 and 0.006, 95% CI 17%-88%, 12%-97%, respectively). Having a \\\"creative\\\" first post, including a unique hashtag, and featuring an author-made graphic correlated with 60%, 49%, and 84% higher XF than posts without those elements (<i>p</i> = 0.01,95% CI 13%-125%, <i>p</i> < 0.001, 95% CI 16%-92%, <i>p</i> < 0.001, 95% CI 30%-164%, respectively). Continuing medical education (CME) accreditation and higher scores on \\\"positive tone\\\" negatively affected XF (-80%, <i>p</i> < 0.001, 95% CI 70%-86% and -7%/point of positivity, <i>p</i> < 0.001, 95% CI 2%-10%, respectively).</p><p><strong>Discussion: </strong>Tweetorial engagement and distribution are determined by multiple factors including authorship, clarity of the topic, and visual appeal of the post. CME accreditation was strongly negatively associated with sharing and may reflect a sharing preference for personal accounts over institutional ones, although further study is needed.</p>\",\"PeriodicalId\":520085,\"journal\":{\"name\":\"Neurology. 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引用次数: 0
摘要
背景和目的:X(以前的Twitter)等社交媒体平台越来越多地用于医学教育。与高参与度相关的推文(线程教学岗位)的特征尚不清楚。我们试图了解神经病学主题的推文与高分享和参与度相关的特征。方法:通过搜索2018年11月至2022年12月期间在X上发布的“tweeitor”和“neurology”,创建以神经病学为主题的推文数据集。通过查看作者的学术资料,从X收集了推文和作者的特征。我们创造并验证了一个新的公式来确定推文的“X因子”(XF),这是一个衡量读者参与度和分布的指标,反映了转发和点赞。对每条推文的基本变量、作者的学术排名和主题内容进行了分析。每篇帖子都经过了使用语言调查和字数统计(LIWC-22)的语言分析,并手工编码风格(如“陈述”或“临床病例”)。我们确定了每个协变量对XF的影响。应用一般估计方程对作者效应进行了修正。结果:我们确定了96位独特作者发布的392篇神经病学主题的推文。XF与印象强烈相关(r2 = 0.85),并在单独的数据集中得到验证(r2 = 0.74)。推文的XF中位数为28.5K(四分位数范围为12.7K-61.5K)。关于“普通神经病学主题”和带有“明确主题”的推文的XF分别比没有的高48%和49% (p = 0.001和0.006,95% CI分别为17%-88%,12%-97%)。与没有这些元素的帖子相比,拥有一个“创造性”的第一篇帖子,包括一个独特的标签,并以作者制作的图形为特色,与60%、49%和84%的XF相关(p = 0.01,95% CI 13%-125%, p < 0.001, 95% CI 16%-92%, p < 0.001, 95% CI 30%-164%)。继续医学教育(CME)认证和较高的“积极语气”分数对XF产生负面影响(分别为-80%,p < 0.001, 95% CI 70%-86%和-7%/点阳性,p < 0.001, 95% CI 2%-10%)。讨论:推文的参与和传播是由多种因素决定的,包括作者身份、主题的清晰度和帖子的视觉吸引力。CME认证与共享强烈负相关,可能反映了个人账户与机构账户的共享偏好,尽管需要进一步研究。
Education Research: Making a Tweetorial Fly: Features of Educational Social Media Posts Associated With High Sharing and Engagement.
Background and objectives: Social media platforms such as X (formerly Twitter) are increasingly used in medical education. Characteristics of tweetorials (threaded teaching posts) associated with higher degrees of engagement are unknown. We sought to understand features of neurology-themed tweetorials associated with high sharing and engagement.
Methods: We created a neurology-themed tweetorials data set by searching "tweetorial" AND "neurology" on X that were posted between November 2018 and December 2022. Tweetorial and author characteristics were collected from X and by viewing the author's academic profile. We created and validated a novel formula to determine the tweetorial's "X Factor" (XF), a measure of reader engagement and distribution, reflecting reposts and likes. Each tweetorial was analyzed for basic variables, the author's academic rank, and thematic content. Each first post underwent a language analysis using Linguistic Inquiry and Word Count (LIWC-22) and was hand-coded for style (such as "statement" or "clinical case"). We determined each covariate's impact on XF. The general estimating equation was applied to correct for the author effect.
Results: We identified 392 neurology-themed tweetorials posted by 96 unique authors. XF strongly correlated with impressions (R2 = 0.85) and was validated in a separate data set (R2 = 0.74). The median XF of the tweetorials was 28.5K (interquartile range 12.7K-61.5K). Tweetorials about a "General Neurology Topic" and with a "Clearly Stated Topic" had 48% and 49% higher XF than those without (p = 0.001 and 0.006, 95% CI 17%-88%, 12%-97%, respectively). Having a "creative" first post, including a unique hashtag, and featuring an author-made graphic correlated with 60%, 49%, and 84% higher XF than posts without those elements (p = 0.01,95% CI 13%-125%, p < 0.001, 95% CI 16%-92%, p < 0.001, 95% CI 30%-164%, respectively). Continuing medical education (CME) accreditation and higher scores on "positive tone" negatively affected XF (-80%, p < 0.001, 95% CI 70%-86% and -7%/point of positivity, p < 0.001, 95% CI 2%-10%, respectively).
Discussion: Tweetorial engagement and distribution are determined by multiple factors including authorship, clarity of the topic, and visual appeal of the post. CME accreditation was strongly negatively associated with sharing and may reflect a sharing preference for personal accounts over institutional ones, although further study is needed.