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Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation

机译:通过自动化社交媒体观众分割来量化和上下文化生物XIV预印的影响

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摘要

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.
机译:Twitter和其他社交媒体平台往往促进与科学手稿的参与。因此,纸张的社交媒体观众的人口统计数据提供了有关如何通过在线社区传播,消费和解释学术研究的丰富信息。通过注意公众对其出版物的看法,科学家可以了解他们的研究是否正在激发积极的学术和公众思想。它们也可以了解来自群体的潜在负面影响,这些群体是故意或无意的方式误解他们的有害方式,并制定改变他们的消息传递的策略来减轻这些影响。在这项研究中,我们收集了331,696个Twitter员额,参考了1,800名高发推文的BiorXiv预印刷品,并利用主题建模,以推断与Twitter上的每个预印刷品接合的各种社区的特征。我们从每个用户的追随者提供了从关键字中获取了这些受众扇区的特征。我们估计,分析的96%的预印刷品由Twitter上的学术受众主导,这表明社交媒体的关注并不总是对应于更大的公众曝光。我们进一步展示了我们的观众分割方法如何量化非特刊主义者的感兴趣程度,如心理健康倡导者,狗恋人,视频游戏开发商,素食主义者,比特币投资者,阴谋理论家,记者,宗教团体和政治选区。令人惊讶的是,我们还发现,分析的10%的预印特征具有大量的(& 5%)与右翼白人国民社区相关的受众部门。虽然这些预印迹似乎没有故意支持任何右翼极端主义消息,但是存在极端主义拨款的案例包括超过50%的推文引用给定的预印刷品。这些结果为改善和上下文化围绕科学研究的公众话语提供了独特的机会。

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