In recent years, in all fields of knowledge, a data-driven approach has spread according to the new scenario defined by the Big Data era. The so-called data deluge has started a season where an impressive amount of data constitutes a valuable research material for scholars. In this new context, the data-driven approach enables academics and scientists to examine and organize data with the goal of increasing knowledge in many research areas. The deluge of data today allows us to plan new analyses on a variety of unstructured data that are produced in major part by web navigation. Recent estimates maintain that 80% of all data is textual data. Furthermore, information that comes from social networks and social media, like Facebook, Twitter, and Instagram, produces unstructured data in real time. Unstructured data is not organized according to a predefined scheme, and information resulting from these sets of data is typically text-heavy. Anyway, it may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand their meaning using traditional programmes as compared to data stored in structured databases. This flow of data has allowed the development of new methods and models, i.e. sentiment analysis to measure the mood of individuals and capture gender differences in language. Sentiment analysis, which is also called opinion mining, has been one of the most active research areas in natural language processing since early 2000 (Liu, 2015), and the constant refinement of analytical tools is offering a richer array of opportunities to analyse these data for many different purposes. These broad assets of data are nowadays available and largely accessible. They represent both a strategic opportunity and a powerful challenge for researchers enabling them to find out new paths for social life exploration, and, more specifically, for the comprehension of the complex relationships intersecting some key concepts in gender, feminist and women/ men sexual identity studies. Data is mainly textual and produced by fertile human communication and exchange activities that take place on an increasing number of social platforms with a powerful viral potential. Human relationships are shaped at different levels of abstraction, fluctuating in the virtual space of the web-net. In these virtual spaces people are actively engaged, performing their agency, and managing their voice contributing to the ‘reproduction of or the resistance to gender arrangements’ in a community (Holmes & Meyerhoff, 1999:180). For this reason, it is worth studying how the concept of gender is built in the social practice of everyday life through multiple interactions, on the one side – and how it intersects other concepts and dimensions of the same research field, on the other. The Community of Practice approach, proposed by Holmes and Meyerhoff (1999), is actually compatible with the social-constructionist theory. This new kind of data has also changed qualitative research in gender studies. Kumsal Bayazit, Chief Executive Officer of Elsevier (2019) said ‘To make progress in gender inclusion, we need to be able to measure where we are and where we want to be’. An increasing number of scholars states the importance of carrying out research based on multiple-approaches methodologies, combining the strength points of micro–macro level analysis techniques (Bergvall, 1999). In fact, there are two main needs: on the one hand, the need to locate the speech in

中文翻译：

---

国际社会学评论 (1/2020)

近年来，在所有知识领域，数据驱动的方法都根据大数据时代定义的新场景展开。所谓的数据泛滥已经开始了一个季节，大量的数据构成了学者们宝贵的研究材料。在这种新的背景下，数据驱动的方法使学者和科学家能够检查和组织数据，以增加许多研究领域的知识。今天的数据泛滥使我们能够对主要由网络导航产生的各种非结构化数据进行新的分析。最近的估计认为所有数据的 80% 是文本数据。此外，来自社交网络和社交媒体（如 Facebook、Twitter 和 Instagram）的信息会实时生成非结构化数据。非结构化数据不是根据预定义的方案组织的，这些数据集产生的信息通常是文本密集型的​​。无论如何，它也可能包含日期、数字和事实等数据。与存储在结构化数据库中的数据相比，这会导致使用传统程序难以理解其含义的不规则性和歧义性。这种数据流允许开发新的方法和模型，即情绪分析来衡量个人的情绪并捕捉语言中的性别差异。情感分析，也称为意见挖掘，自 2000 年初以来一直是自然语言处理中最活跃的研究领域之一 (Liu, 2015)，分析工具的不断改进提供了更丰富的机会，可以为许多不同的目的分析这些数据。这些广泛的数据资产现在可用并且在很大程度上可以访问。它们对研究人员来说既是一个战略机遇，也是一个强大的挑战，使他们能够找到探索社会生活的新途径，更具体地说，是为了理解与性别、女权主义和女性/男性性别认同中的一些关键概念交叉的复杂关系学习。数据主要是文本形式，由在越来越多具有强大病毒潜力的社交平台上进行的丰富的人类交流和交流活动产生。人际关系在不同的抽象层次上形成，在网络的虚拟空间中波动。在这些虚拟空间中，人们积极参与、发挥他们的作用并管理他们的声音，为社区中的“性别安排的再现或抵抗”做出贡献（Holmes & Meyerhoff，1999：180）。出于这个原因，值得研究性别概念如何通过多重互动在日常生活的社会实践中建立起来——另一方面，它如何与同一研究领域的其他概念和维度相交。Holmes 和 Meyerhoff (1999) 提出的实践社区方法实际上与社会建构主义理论兼容。这种新数据也改变了性别研究的定性研究。爱思唯尔首席执行官 Kumsal Bayazit（2019 年）说：“为了在性别包容方面取得进展，我们需要能够衡量我们所处的位置以及我们想要的位置”。越来越多的学者指出基于多种方法进行研究的重要性，结合微观-宏观水平分析技术的优势（Bergvall，1999）。其实主要有两个需求：一方面，需要将语音定位在