Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
Computational Biology Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
PLoS Biol. 2020 Sep 22;18(9):e3000860. doi: 10.1371/journal.pbio.3000860. eCollection 2020 Sep.
Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user's followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.
Twitter 和其他社交媒体平台经常为人们阅读科学文献提供便利。因此，论文社交媒体受众的人口统计学特征为了解学术研究是如何通过在线社区传播、消费和解释提供了丰富的信息。通过关注公众对其出版物的看法，科学家可以了解他们的研究是否激发了积极的学术和公众思想。他们还可以意识到，以有害的方式（故意或无意地）曲解他们工作的群体可能会对他们的研究产生潜在的负面兴趣模式，并制定策略来改变他们的信息传递方式，以减轻这些影响。在这项研究中，我们收集了 331696 条提及 1800 篇高 tweeted 预印本 bioRxiv 的推文，并利用主题建模推断出在 Twitter 上与每个预印本互动的各种社区的特征。我们从每个用户的 Twitter 个人资料中提供的关键字中，以无偏见的方式了解这些受众群体的特征。我们估计，在分析的预印本中，96%是由 Twitter 上的学术受众主导的，这表明社交媒体关注度并不总是与更大的公众曝光度相对应。我们进一步展示了我们的受众细分方法如何量化来自非专业受众群体（如心理健康倡导者、爱狗人士、视频游戏开发者、素食主义者、比特币投资者、阴谋论者、记者、宗教团体和政治选区）的兴趣水平。令人惊讶的是，我们还发现，在分析的预印本中，有 10%的预印本有相当大（>5%）的受众群体与右翼白人至上主义社区有关。尽管这些预印本中没有一个似乎有意宣扬任何右翼极端主义信息，但在某些情况下，极端主义的挪用占引用给定预印本的推文的 50%以上。这些结果为改善和使围绕科学研究的公众话语语境化提供了独特的机会。
