167 利用网络科学和自然语言处理评估 Altmetric 关注度

Alaguvalliappan Thiagarajan, Christopher McCarty, Edward Seh-Taylor
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引用次数: 0

摘要

目的/目标:我们的项目旨在评估在替代指标上得分较高的研究论文的构成或特征。这些替代指标包括报纸提及次数、社交媒体提及次数以及 Altmetric(一种用于记录特定研究论文社会关注度的工具)上的关注度得分。方法/研究对象:我们的研究旨在:1)利用主题建模来识别 Altmetric 上的流行主题;2)应用网络分析来阐明与高关注度论文相关的大学、资金来源、期刊和出版商之间的相互联系。3) 研究当关注度指标(如社交媒体提及率、报纸提及率或 Altmetric 分数)发生变化时,这些模式会如何变化。我们将首先对所有类型的论文进行分析,然后将网络局限于生物医学和临床科学以及公共和联合健康科学,以帮助了解哪些健康主题获得了关注。结果/预期结果:我们最初的 Altmetric 主题模型显示,COVID-19 和疫苗接种相关的出版物在大流行过后仍受到持续关注(具体来说,是 2023 年 1 月以后的论文)。癌症、痴呆症和肥胖症等健康主题也获得了很高的关注度。此外,政治类论文(选举、民主)、气候变化和电池研究的关注度也很高。我们需要做进一步的分析,以解释这些主题获得关注的原因和关注的类型。我们将构建网络,以了解关注度与大学、资金来源、期刊和出版商等实体之间的关系。这将确定这些实体的某些集群是否会产生高关注度的论文,或者关注度是否在它们之间均匀分布。讨论/意义:要衡量学术研究的广泛影响,除了引用之外,还需要其他指标。CTSA 广泛使用 Altmetric 来衡量社会对研究的兴趣。了解在 Altmetric 上获得关注的研究类型,有助于研究人员了解如何从社区中获得兴趣。
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167 An Evaluation of Altmetric Attention using Network Science and Natural Language Processing
OBJECTIVES/GOALS: Our project aims to assess the composition or characteristics of research papers that score high on alternative metrics. These alternative metrics including the number of newspaper mentions, social media mentions, and the attention score as catalogued on Altmetric, a tool used to document community attention for a given research paper. METHODS/STUDY POPULATION: Our study intends to 1) Utilize topic modeling to identify prevalent themes on Altmetric, and 2) Apply network analysis to elucidate the interconnectedness among universities, funding sources, journals, and publishers associated with high-attention papers. 3) Examine how these patterns vary when attention metrics shift, such as social media mentions, newspaper mentions, or the Altmetric score. We'll first perform this analysis on all types of papers and then limit the networks to Biomedical and Clinical Sciences, and Public and Allied Health Sciences to help inform what health topics garner attention. RESULTS/ANTICIPATED RESULTS: Our initial Altmetric topic models revealed sustained attention for COVID-19 and vaccination-related publications well beyond the pandemic (specifically, papers from January 2023). Health topics like cancer, dementia, and obesity also garnered high attention. Additionally, political papers (elections, democracy), climate change, and battery research had notable attention values. Further analysis needs to be done to explain why these topics gain attention and the type of attention they garner. We will construct networks to see the relationship between attention and entities like universities, funding sources, journals, and publishers. This will identify whether certain clusters of these entities produce papers with high attention or if attention is distributed evenly amoung them. DISCUSSION/SIGNIFICANCE: To gauge the broader impact of scholarly research alternative metrics beyond citations are needed. Altmetric is used widely by CTSA’s to measure the community interest in research. Understanding the types of research that gain traction on Altmetric can help researchers understand how to garner interest from the community.
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