Annual Review of Sociology· 2016Q1· Review
Machine Translation: Mining Text for Social Theory
- 345citations
- Q1SCImago
- 2016year
Short summary
Computational methods, including supervised and unsupervised machine learning, can extract social insights from the vast amount of electronic text, analyzing collective attention, social relationships, and social states.
AI-generated from the title and abstract; the full text is not read.
Key points
- Vast amounts of electronic text from diverse sources (web, social media, digitized libraries) are now available for social analysis.
- Computational tools like natural language processing and machine learning are essential for filtering, searching, and translating this text into data.
- Supervised methods extend existing theories to new text data, while unsupervised techniques discover novel regularities for theorizing.
- These tools can provide insights into collective attention, social relationships, and social states by analyzing communication content and signals.
AI-generated from the title and abstract; the full text is not read.
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 the content of 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.
The authors' abstract, as published at the source. Annual Review of Sociology, 2016 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences