The Potential of Automated Text Analytics in Social Knowledge Building

There are still many sociologists who are skeptical of the findings of big data-based analysis of social-data, questioning the potential of this knowledge production and its contribution to the scientific discourse of sociology.The chapter shows that this tension can be addressed through the redefinition of the research methodological basis of sociology, by the organic incorporation of data science know-how into its methods; the combined application of qualitative and quantitative analysis; and, the use of knowledge-driven science instead of the data-driven approach.The theoretical, methodological, and topical pathways between traditional and computational sociology emerge gradually along the chapter, which also includes plenty of illustrative examples of research situated at the interplay between sociology and data science. As our overview shows, there are new possibilities for sociological research, which are, in some sense, just by-products of information science. We introduce recently developed methods, which can be applied to specific sociological problems outside the scope of business applications. We present sociological topics not yet studied in this area and show new insights the approach can offer to classical sociological questions. As our aim is to encourage sociologists to enter this field, we discuss the new methods on the base of the classic quantitative approach, using its concepts and terminology and addressing the question of how traditionally trained sociologists can acquire new skills.

From Durkheim to Machine Learning: Finding the Relevant Sociological Content in Depression and Suicide-Related Social Media Discourses

The phenomenon of suicide has been a focal point since Durkheim among social scientists. Internet and social media sites provide new ways for people to express their positive feelings, but they are also platforms to express suicide ideation or depressed thoughts. Most of these posts are not about real suicide, and some of them are a cry for help. Nevertheless, suicide- and depression-related content varies among platforms, and it is not evident how a researcher can find these materials in mass data of social media. Our paper uses the corpus of more than four million Instagram posts, related to mental health problems. After defining the initial corpus, we present two different strategies to find the relevant sociological content in the noisy environment of social media. The first approach starts with a topic modeling (Latent Dirichlet Allocation), the output of which serves as the basis of a supervised classification method based on advanced machine-learning techniques. The other strategy is built on an artificial neural network-based word embedding language model. Based on our results, the combination of topic modeling and neural network word embedding methods seems to be a promising way to find the research related content in a large digital corpus.Our research can provide added value in the detection of possible self-harm events. With the utilization of complex techniques (such as topic modeling and word embedding methods), it is possible to identify the most problematic posts and most vulnerable users.