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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. A Guided Topic-Noise Model for Short Texts

   https://doi.org/10.1145/3485447.3512007  

   Churchill, Robert; Singh, Lisa; Ryan, Rebecca; Davis-Kean, Pamela (April 2022, WWW '22: Proceedings of the ACM Web Conference 2022)

   Researchers using social media data want to understand the discussions occurring in and about their respective fields. These domain experts often turn to topic models to help them see the entire landscape of the conversation, but unsupervised topic models often produce topic sets that miss topics experts expect or want to see. To solve this problem, we propose Guided Topic-Noise Model (GTM), a semi-supervised topic model designed with large domain-specific social media data sets in mind. The input to GTM is a set of topics that are of interest to the user and a small number of words or phrases that belong to those topics. These seed topics are used to guide the topic generation process, and can be augmented interactively, expanding the seed word list as the model provides new relevant words for different topics. GTM uses a novel initialization and a new sampling algorithm called Generalized Polya Urn (GPU) seed word sampling to produce a topic set that includes expanded seed topics, as well as new unsupervised topics. We demonstrate the robustness of GTM on open-ended responses from a public opinion survey and four domain-specific Twitter data sets. 

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3. COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution

   https://doi.org/10.1007/s11192-023-04747-w  

   Wu, Mengjia; Zhang, Yi; Markley, Mark; Cassidy, Caitlin; Newman, Nils; Porter, Alan (May 2023, Scientometrics)

   Covid-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood has surged, researchers have been disadvantaged by not having access to a platform that can quickly synthesize rapidly emerging information and link the expertise it contains to established knowledge foundations. Aiming to fill this gap, in this paper we propose a research framework that can assist scientists in identifying, retrieving, and understanding Covid-19 knowledge from the ocean of scholarly articles. Incorporating Principal Component Decomposition (PDC), a knowledge model based on text analytics, and hierarchical topic tree analysis, the proposed framework profiles the research landscape, retrieves topic-specific knowledge and visualizes knowledge structures. Addressing 127,971 Covid-19 research papers from PubMed, our PCD topic analysis identifies 35 research hotspots, along with their correlations and trends. The hierarchical topic tree analysis further segments the knowledge landscape of the whole dataset into clinical and public health branches at a macro level. To supplement this analysis, we also built a knowledge model from research papers on vaccinations and fetched 92,286 pre-Covid publications as the established knowledge foundation for reference. The hierarchical topic tree analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization. 

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4. Domain-specific Topic Model for Knowledge Discovery through Conversational Agents in Data Intensive Scientific Communities

   Zhang, Y.; Calyam, P.; Joshi, T.; Nair, S.S.; Xu, D. (January 2018, IEEE International Workshop on Conversational Agents and Chatbots with Machine Learning (ChatbotML), in conjunction with IEEE Big Data)

   Machine learning techniques underlying Big Data analytics have the potential to benefit data intensive communities in e.g., bioinformatics and neuroscience domain sciences. Today’s innovative advances in these domain communities are increasingly built upon multi-disciplinary knowledge discovery and cross-domain collaborations. Consequently, shortened time to knowledge discovery is a challenge when investigating new methods, developing new tools, or integrating datasets. The challenge for a domain scientist particularly lies in the actions to obtain guidance through query of massive information from diverse text corpus comprising of a wide-ranging set of topics. In this paper, we propose a novel “domain-specific topic model” (DSTM) that can drive conversational agents for users to discover latent knowledge patterns about relationships among research topics, tools and datasets from exemplar scientific domains. The goal of DSTM is to perform data mining to obtain meaningful guidance via a chatbot for domain scientists to choose the relevant tools or datasets pertinent to solving a computational and data intensive research problem at hand. Our DSTM is a Bayesian hierarchical model that extends the Latent Dirichlet Allocation (LDA) model and uses a Markov chain Monte Carlo algorithm to infer latent patterns within a specific domain in an unsupervised manner. We apply our DSTM to large collections of data from bioinformatics and neuroscience domains that include hundreds of papers from reputed journal archives, hundreds of tools and datasets. Through evaluation experiments with a perplexity metric, we show that our model has better generalization performance within a domain for discovering highly specific latent topics. 

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5. Tracking Disaster Footprints with Social Streaming Data

   https://doi.org/10.1609/aaai.v34i01.5372  

   Cheng, Lu; Li, Jundong; Candan, K. Selcuk; Liu, Huan (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Social media has become an indispensable tool in the face of natural disasters due to its broad appeal and ability to quickly disseminate information. For instance, Twitter is an important source for disaster responders to search for (1) topics that have been identified as being of particular interest over time, i.e., common topics such as “disaster rescue”; (2) new emerging themes of disaster-related discussions that are fast gathering in social media streams (Saha and Sindhwani 2012), i.e., distinct topics such as “the latest tsunami destruction”. To understand the status quo and allocate limited resources to most urgent areas, emergency managers need to quickly sift through relevant topics generated over time and investigate their commonness and distinctiveness. A major obstacle to the effective usage of social media, however, is its massive amount of noisy and undesired data. Hence, a naive method, such as set intersection/difference to find common/distinct topics, is often not practical. To address this challenge, this paper studies a new topic tracking problem that seeks to effectively identify the common and distinct topics with social streaming data. The problem is important as it presents a promising new way to efficiently search for accurate information during emergency response. This is achieved by an online Nonnegative Matrix Factorization (NMF) scheme that conducts a faster update of latent factors, and a joint NMF technique that seeks the balance between the reconstruction error of topic identification and the losses induced by discovering common and distinct topics. Extensive experimental results on real-world datasets collected during Hurricane Harvey and Florence reveal the effectiveness of our framework. 

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