A systematic review of the use of topic models for short text social media analysis

Caitlin Doogan · Wray Buntine · Henry Linger

133 citations0/0 files storedArtificial Intelligence Review

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Computational and Text Analysis Methods Topic Modeling Sentiment Analysis and Opinion Mining

Abstract

Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures do not inform whether the topics produced can yield meaningful insights for those examining social media data. Efforts to address this issue, including gauging the alignment between automated and human evaluation tasks, are hampered by a lack of knowledge about how researchers use topic models. Further problems could arise if researchers do not construct topic models optimally or use them in a way that exceeds the models' limitations. These scenarios threaten the validity of topic model development and the insights produced by researchers employing topic modelling as a methodology. However, there is currently a lack of information about how and why topic models are used in applied research. As such, we performed a systematic literature review of 189 articles where topic modelling was used for social media analysis to understand how and why topic models are used for social media analysis. Our results suggest that the development of topic models is not aligned with the needs of those who use them for social media analysis. We have found that researchers use topic models sub-optimally. There is a lack of methodological support for researchers to build and interpret topics. We offer a set of recommendations for topic model researchers to address these problems and bridge the gap between development and applied research on short text topic models. Supplementary Information: The online version contains supplementary material available at 10.1007/s10462-023-10471-x.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 133 citations exist upstream.

References

2003Towards a Methodology for Developing Evidence‐Informed Management Knowledge by Means of Systematic Review 2010Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement 2009The SAGE handbook of organizational research methods 2014Structural Topic Models for Open‐Ended Survey Responses 2010Empirical study of topic modeling in Twitter 1972Information processing management 2019Topic Modeling in Management Research: Rendering New Theory from Textual Data 2015Quantitative analysis of large amounts of journalistic texts using topic modelling

Cited by

2024Facilitating topic modeling in tourism research:Comprehensive comparison of new AI technologies 2024Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study 2025Topic modelling through the bibliometrics lens and its technique 2025The collision of tradition and fashion: How anthropomorphizing museum exhibits influences cultural inheritance 2024Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.