Machine Translation: Mining Text for Social Theory

Abstract

More of the social world lives within electronic text than ever before, from collective activity on the web, social media, and instant messaging to online transactions, government intelligence, and digitized libraries. This supply of text has elicited demand for natural language processing and machine learning tools to filter, search, and translate text into valuable data. We survey some of the most exciting computational approaches to text analysis, highlighting both supervised methods that extend old theories to new data and unsupervised techniques that discover hidden regularities worth theorizing. We then review recent research that uses these tools to develop social insight by exploring (a) collective attention and reasoning through content from communication; (b) social relationships through the process of communication; and (c) social states, roles, and moves identified through heterogeneous signals within communication. We highlight social questions for which these advances could offer powerful new insight. 18.1

Big Data in Sociology: New Data, New Sociology?

“…Из этого списка особенного внимания заслуживают инстру-менты из области вычислительной лингвистики. Появление тематического моде-лирования описывается как шаг революционного значения, который на данный момент пока не оценен социологами в должной мере (Evans, Aceves, 2016). Главные области применения -это социология науки и социология культуры, ведь имен-но в этих областях исследователи имеют дело с текстами.…”

Analytical sociology and computational social science

“…Instead of focusing on parameter estimation of models built from theory, machine learning models are typically evaluated on their ability to predict held-out samples of the data. Unlike the predictive use of machine learning common among computer scientists, social scientists start employing machine learning to measure latent characteristics in the social world and refine methods of causal inference from observational data [29][30][31][32].…”

“…During the last 20 years there has been a surge of methods developed for processing text under the label of natural language processing [33] including tools for the classification of content in large documents [34], semantic analysis [35], and opinion mining [36]. These tools, now readily available to researchers in the social sciences, make text originating from a wide range of sources such as books, newspapers, and online discussion groups accessible for large-scale quantitative analysis [29]. As becomes evident from a growing number of applications in sociology and the social sciences more generally, these powerful tools open new avenues for an ethnography on a systematic scale including, to name but a few, the study of culture [37,38], analyses of political expression and conflict [39,40] and, in combination with digitized archives, investigations of historical events and social change [41,42].…”

Using online data and network-based text analysis in HRM research

“…Recent advances in digital technology have resulted in the production and availability of large amounts of online information from social media posts to digitised libraries (Evans and Aceves, 2016;Light, 2014). This vast body of material provides an increasingly important source of data that complements traditional quantitative and qualitative data and allows researchers to ask novel research questions as well as unravel robust patterns about organisational and social phenomena.…”