https://github.com/nathan-lindstedt/ssci_study

https://github.com/nathan-lindstedt/ssci_study

quantitative-text-analysis social-movements stm structural-topic-modeling tutorial

Last synced: 7 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/nathan-lindstedt/ssci_study
• Owner: nathan-lindstedt
• License: mit
• Created: 2019-12-12T03:11:50.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-01-07T01:12:39.000Z (about 5 years ago)
• Last Synced: 2024-11-09T00:48:28.016Z (2 months ago)
• Topics: quantitative-text-analysis, social-movements, stm, structural-topic-modeling, tutorial
• Language: R
• Homepage:
• Size: 354 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

README

        
# ssci_study

STRUCTURAL TOPIC MODELING FOR SOCIAL SCIENTISTS: A BRIEF CASE STUDY WITH SOCIAL MOVEMENT STUDIES LITERATURE, 2005–2017

Abstract

Sociologists frequently make use of language as data in their research using methodologies including open-ended surveys, in-depth interviews, and content analyses. Unfortunately, the ability of researchers to analyze the growing amount of these data declines as the costs and time associated with the research process increases. Topic modeling is a computer-assisted technique that can help social scientists to address these data challenges. Despite the central role of language in sociological research, to date the field has largely overlooked the promise of automated text analysis in favor of more familiar and more traditional methods. This article provides an overview of a topic modeling framework especially suited for social scientific research. By way of a case study using abstracts from social movement studies literature, a short tutorial from data preparation through data analysis is given for the method of structural topic modeling. This example demonstrates how text analytics can be applied to research in sociology and encourages academics to consider such methods not merely as novel tools, but as useful supplements that can work beside and enhance existing methodologies.

https://doi.org/10.1177/2329496519846505