Abstract Purpose Social media users share their ideas, thoughts, and emotions with other users. However, it is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications. Additionally, we investigate what features of the research articles help in such prediction. Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles. Design/methodology/approach Several tools are used for sentiment analysis, so we applied five sentiment analysis tools to check which are suitable for capturing a tweet's sentiment value and decided to use NLTK VADER and TextBlob. We segregated the sentiment value into negative, positive, and neutral. We measure the mean and median of tweets’ sentiment value for research articles with more than one tweet. We next built machine learning models to predict the sentiments of tweets related to scientific publications and investigated the essential features that controlled the prediction models. Findings We found that the most important feature in all the models was the sentiment of the research article title followed by the author count. We observed that the tree-based models performed better than other classification models, with Random Forest achieving 89% accuracy for binary classification and 73% accuracy for three-label classification. Research limitations In this research, we used state-of-the-art sentiment analysis libraries. However, these libraries might vary at times in their sentiment prediction behavior. Tweet sentiment may be influenced by a multitude of circumstances and is not always immediately tied to the paper's details. In the future, we intend to broaden the scope of our research by employing word2vec models. Practical implications Many studies have focused on understanding the impact of science on scientists or how science communicators can improve their outcomes. Research in this area has relied on fewer and more limited measures, such as citations and user studies with small datasets. There is currently a critical need to find novel methods to quantify and evaluate the broader impact of research. This study will help scientists better comprehend the emotional impact of their work. Additionally, the value of understanding the public's interest and reactions helps science communicators identify effective ways to engage with the public and build positive connections between scientific communities and the public. Originality/value This study will extend work on public engagement with science, sociology of science, and computational social science. It will enable researchers to identify areas in which there is a gap between public and expert understanding and provide strategies by which this gap can be bridged.

中文翻译：

---

通过 Twitter 情绪预测公众对科学研究的反应

摘要目的社交媒体用户与其他用户分享他们的想法、想法和情感。然而，尚不清楚在线用户将如何回应新的研究成果。这项研究旨在预测 Twitter 用户对科学出版物表达的情绪的性质。此外，我们调查研究文章的哪些特征有助于这种预测。识别社交媒体上研究文章的情绪将有助于科学家衡量其研究文章的新社会影响。设计/方法/方法 情绪分析使用了几种工具，因此我们应用了五种情绪分析工具来检查哪些工具适合捕获推文' s 情感值并决定使用 NLTK VADER 和 TextBlob。我们将情绪值分为负面、正面和中性。我们测量了多条推文的研究文章的推文情绪值的平均值和中位数。接下来，我们构建了机器学习模型来预测与科学出版物相关的推文的情绪，并研究了控制预测模型的基本特征。结果 我们发现，所有模型中最重要的特征是研究文章标题的情感，其次是作者数量。我们观察到基于树的模型比其他分类模型表现更好，随机森林的二分类准确率达到 89%，三标签分类准确率达到 73%。研究局限在这项研究中，我们使用了最先进的情感分析库。但是，这些库的情绪预测行为有时可能会有所不同。推文情绪可能会受到多种情况的影响，并不总是与论文的细节直接相关。未来，我们打算通过使用 word2vec 模型来扩大我们的研究范围。实际意义 许多研究都集中在了解科学对科学家的影响或科学传播者如何改善他们的成果。该领域的研究依赖于更少和更有限的措施，例如引用和使用小数据集的用户研究。目前迫切需要找到新的方法来量化和评估研究的更广泛影响。这项研究将帮助科学家更好地理解他们的工作对情感的影响。此外，了解公众的兴趣和反应的价值有助于科学传播者确定与公众互动的有效方式，并在科学界和公众之间建立积极的联系。原创性/价值 本研究将扩展公众参与科学、科学社会学和计算社会科学的工作。它将使研究人员能够确定公众和专家理解之间存在差距的领域，并提供可以弥合这种差距的策略。