The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology
Open accessAbstract
Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of topics to be generated; (c) evaluation of the model’s reliability; and (d) the process of validly interpreting the resulting topics. We review the research literature dealing with these questions and propose a methodology that approaches these challenges. Our overall goal is to make LDA topic modeling more accessible to communication researchers and to ensure compliance with disciplinary standards. Consequently, we develop a brief hands-on user guide for applying LDA topic modeling. We demonstrate the value of our approach with empirical data from an ongoing research project.
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Cites
Introduction to information retrieval
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The Content Analysis Guidebook
Neuendorf, K. A. (2017) · doi:10.4135/9781071802878 · Cited by 9,286 · cited 1×
“These challenges particularly affect the approach’s reliability and validity, both of which are core criteria for content analysis in communication research (Neuendorf, 2017), but they have, nevertheless, received little attention thus far.”
BackgroundFinding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344 · cited 1×
“Subsequent semantic community detection is performed by applying the Partitioning-Around-Medoids (PAM) algorithm (see Kaufman & Rousseeuw, 1990).”
MethodGriffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297 · cited 3×
“Compared with other topic-clustering methods, a further advantage of LDA topic modeling is its mixed membership approach (Grimmer & Stewart, 2013, p. 18), i.e., one document can contain several topics, which is a useful assumption.”
BackgroundMethodComparing Twitter and Traditional Media Using Topic Models
Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., & Li, X. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-20161-5_34 · Cited by 1,214
Topics in semantic representation
Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007) · Psychological Review · doi:10.1037/0033-295x.114.2.211 · Cited by 1,122
topicmodels : An R Package for Fitting Topic Models
Grün, B., & Hornik, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v040.i13 · Cited by 1,072
How to analyze political attention with minimal assumptions and costs
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2009.00427.x · Cited by 671
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Russian Twitter accounts and the partisan polarization of vaccine discourse, 2015-2017
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Bright, J., Hale, S., Ganesh, B., Bulovsky, A., Margetts, H., & Howard, P. (2020) · Communication Research · doi:10.1177/0093650219872394 · Cited by 104
Zeng, J., Chan, C.-H., & Schäfer, M. S. (2022) · Information, Communication & Society · doi:10.1080/1369118x.2020.1776372 · Cited by 102
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Metadata
- Authors
- Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H., & Adam, S.
- Venue
- Communication Methods and Measures
- Publisher
- Informa UK Limited
- Published
- 2018-02-16
- Volume
- 12
- Issue
- 2-3
- Pages
- 93-118
- ISSN
- 1931-2458 (ISSN-L)
- 1931-2466
- OpenAlex
- W2790855988
- Cited by
- 832
- References
- 10
- FWCI
- 188.35
- Type
- journal article
- Language
- en
- Open access
- green
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- Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H., & Adam, S. (2018). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Communication Methods and Measures. https://doi.org/10.1080/19312458.2018.1430754
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