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Abstract：
Although computational linguistic methods-such as topic modelling, sentiment analysis and emotion detection-can provide social media researchers with insights into online public discourses, it is not inherent as to how these methods should be used, with a lack of transparent instructions on how to apply them in a critical way. There is a growing body of work focusing on the strengths and shortcomings of these methods. Through applying best practices for using these methods within the literature, we focus on setting expectations, presenting trajectories, examining with context and critically reflecting on the diachronic Twitter discourse of two case studies: the longitudinal discourse of the NHS Covid-19 digital contact-tracing app and the snapshot discourse of the Ofqual A Level grade calculation algorithm, both related to the UK. We identified difficulties in interpretation and potential application in all three of the approaches. Other shortcomings, such the detection of negation and sarcasm, were also found. We discuss the need for further transparency of these methods for diachronic social media researchers, including the potential for combining these approaches with qualitative ones-such as corpus linguistics and critical discourse analysis-in a more formal framework.
摘要：
尽管计算语言学方法——比如主题建模，情绪分析和情绪检测-可以为社交媒体研究人员提供对在线公共话语的见解，如何使用这些方法并不是固有的，缺乏关于如何以关键方式应用它们的透明说明。越来越多的工作集中在这些方法的优点和缺点上。通过在文献中应用使用这些方法的最佳实践，我们专注于设定期望，呈现轨迹，结合上下文进行审查，并批判性地反思两个案例研究的历时Twitter话语：NHSCovid-19数字联系人追踪应用程序的纵向话语和OfqualALevel等级计算算法的快照话语，两者都与英国有关。我们确定了所有三种方法在解释和潜在应用方面的困难。其他缺点,这种否定和讽刺的检测，也被发现了。我们讨论了对历时社交媒体研究人员进一步透明这些方法的必要性，包括将这些方法与定性方法（如语料库语言学和批判性语篇分析）结合在一个更正式的框架中的潜力。
