期刊
JOURNALISM & MASS COMMUNICATION QUARTERLY
卷 93, 期 2, 页码 332-359出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1077699016639231
关键词
computer-assisted content analysis; unsupervised machine learning; topic modeling; political communication; Twitter
资金
- US AFOSR [FA9550-10-1-0458, A1795]
- US NSF [1218992, 1527618]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1218992, 1527618] Funding Source: National Science Foundation
This article presents an empirical study that investigated and compared two big data text analysis methods: dictionary-based analysis, perhaps the most popular automated analysis approach in social science research, and unsupervised topic modeling (i.e., Latent Dirichlet Allocation [LDA] analysis), one of the most widely used algorithms in the field of computer science and engineering. By applying two big data methods to make sense of the same dataset77 million tweets about the 2012 U.S. presidential electionthe study provides a starting point for scholars to evaluate the efficacy and validity of different computer-assisted methods for conducting journalism and mass communication research, especially in the area of political communication.
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