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1. Using Topic Modeling for Code Discovery in Large Scale Text Data

   https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_2  

   Cai, Zhiqiang ; Siebert-Evenstone, Amanda ; Eagan, Brendan R. ; Shaffer, David W. ( January 2021 , Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings)

   Ruis, Andrew ; Lee, Seung B. (Ed.)

   When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of “topics” in the data, which researchers then use to interpret meaning of the topics. A topic model also gives each document in the dataset a score for each topic, which can be used as a non-binary coding for what proportion of a topic is in the document. Unfortunately, it is often difficult to interpret what the topics mean in a defensible way, or to validate document topic proportion scores as meaningful codes. In this study, we examine how keywords from codes developed by human experts were distributed in topics generated from topic modeling. The results show that (1) top keywords of a single topic often contain words from multiple human-generated codes; and conversely, (2) words from human-generated codes appear as high-probability keywords in multiple topic. These results explain why directly using topics from topic models as codes is problematic. However, they also imply that topic modeling makes it possible for researchers to discover codes from short word lists. 

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2. Evaluating the Coverage and Depth of Latent Dirichlet Allocation Topic Model in Comparison with Human Coding of Qualitative Data: The Case of Education Research

   https://doi.org/10.3390/make5020029  

   Nanda, Gaurav ; Jaiswal, Aparajita ; Castellanos, Hugo ; Zhou, Yuzhe ; Choi, Alex ; Magana, Alejandra J. ( June 2023 , Machine Learning and Knowledge Extraction)

   Fields in the social sciences, such as education research, have started to expand the use of computer-based research methods to supplement traditional research approaches. Natural language processing techniques, such as topic modeling, may support qualitative data analysis by providing early categories that researchers may interpret and refine. This study contributes to this body of research and answers the following research questions: (RQ1) What is the relative coverage of the latent Dirichlet allocation (LDA) topic model and human coding in terms of the breadth of the topics/themes extracted from the text collection? (RQ2) What is the relative depth or level of detail among identified topics using LDA topic models and human coding approaches? A dataset of student reflections was qualitatively analyzed using LDA topic modeling and human coding approaches, and the results were compared. The findings suggest that topic models can provide reliable coverage and depth of themes present in a textual collection comparable to human coding but require manual interpretation of topics. The breadth and depth of human coding output is heavily dependent on the expertise of coders and the size of the collection; these factors are better handled in the topic modeling approach. 

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3. Topical network embedding

   https://doi.org/10.1007/s10618-019-00659-7  

   Shi, Min ; Tang, Yufei ; Zhu, Xingquan ; Liu, Jianxun ; He, Haibo ( October 2019 , Data Mining and Knowledge Discovery)

   Networked data involve complex information from multifaceted channels, including topology structures, node content, and/or node labels etc., where structure and content are often correlated but are not always consistent. A typical scenario is the citation relationships in scholarly publications where a paper is cited by others not because they have the same content, but because they share one or multiple subject matters. To date, while many network embedding methods exist to take the node content into consideration, they all consider node content as simple flat word/attribute set and nodes sharing connections are assumed to have dependency with respect to all words or attributes. In this paper, we argue that considering topic-level semantic interactions between nodes is crucial to learn discriminative node embedding vectors. In order to model pairwise topic relevance between linked text nodes, we propose topical network embedding, where interactions between nodes are built on the shared latent topics. Accordingly, we propose a unified optimization framework to simultaneously learn topic and node representations from the network text contents and structures, respectively. Meanwhile, the structure modeling takes the learned topic representations as conditional context under the principle that two nodes can infer each other contingent on the shared latent topics. Experiments on three real-world datasets demonstrate that our approach can learn significantly better network representations, i.e., 4.1% improvement over the state-of-the-art methods in terms of Micro-F1 on Cora dataset. (The source code of the proposed method is available through the github link: https:// github.com/codeshareabc/TopicalNE.) 

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4. Creative Destruction: The Structural Consequences of Scientific Curation

   https://doi.org/10.1177/0003122421996323  

   McMahan, Peter ; McFarland, Daniel A. ( April 2021 , American Sociological Review)

   null (Ed.)

   Communication of scientific findings is fundamental to scholarly discourse. In this article, we show that academic review articles, a quintessential form of interpretive scholarly output, perform curatorial work that substantially transforms the research communities they aim to summarize. Using a corpus of millions of journal articles, we analyze the consequences of review articles for the publications they cite, focusing on citation and co-citation as indicators of scholarly attention. Our analysis shows that, on the one hand, papers cited by formal review articles generally experience a dramatic loss in future citations. Typically, the review gets cited instead of the specific articles mentioned in the review. On the other hand, reviews curate, synthesize, and simplify the literature concerning a research topic. Most reviews identify distinct clusters of work and highlight exemplary bridges that integrate the topic as a whole. These bridging works, in addition to the review, become a shorthand characterization of the topic going forward and receive disproportionate attention. In this manner, formal reviews perform creative destruction so as to render increasingly expansive and redundant bodies of knowledge distinct and comprehensible. 

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5. Sparseness-constrained nonnegative tensor factorization for detecting topics at different time scales

   https://doi.org/10.3389/fams.2024.1287074  

   Kassab, Lara ; Kryshchenko, Alona ; Lyu, Hanbaek ; Molitor, Denali ; Needell, Deanna ; Rebrova, Elizaveta ; Yuan, Jiahong ( July 2024 , Frontiers in Applied Mathematics and Statistics)

   Temporal text data, such as news articles or Twitter feeds, often comprises a mixture of long-lasting trends and transient topics. Effective topic modeling strategies should detect both types and clearly locate them in time. We first demonstrate that nonnegative CANDECOMP/PARAFAC decomposition (NCPD) can automatically identify topics of variable persistence. We then introduce sparseness-constrained NCPD (S-NCPD) and its online variant to control the duration of the detected topics more effectively and efficiently, along with theoretical analysis of the proposed algorithms. Through an extensive study on both semi-synthetic and real-world datasets, we find that our S-NCPD and its online variant can identify both short- and long-lasting temporal topics in a quantifiable and controlled manner, which traditional topic modeling methods are unable to achieve. Additionally, the online variant of S-NCPD shows a faster reduction in reconstruction error and results in more coherent topics compared to S-NCPD, thus achieving both computational efficiency and quality of the resulting topics. Our findings indicate that S-NCPD and its online variant are effective tools for detecting and controlling the duration of topics in temporal text data, providing valuable insights into both persistent and transient trends.

    

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