Contexts of diffusion: Adoption of research synthesis in Social Work and Women's Studies

Texts reveal the subjects of interest in research fields, and the values, beliefs, and practices of researchers. In this study, texts are examined through bibliometric mapping and topic modeling to provide a birds eye view of the social dynamics associated with the diffusion of research synthesis methods in the contexts of Social Work and Women's Studies. Research synthesis texts are especially revealing because the methods, which include meta-analysis and systematic review, are reliant on the availability of past research and data, sometimes idealized as objective, egalitarian approaches to research evaluation, fundamentally tied to past research practices, and performed with the goal informing future research and practice. This study highlights the co-influence of past and subsequent research within research fields; illustrates dynamics of the diffusion process; and provides insight into the cultural contexts of research in Social Work and Women's Studies. This study suggests the potential to further develop bibliometric mapping and topic modeling techniques to inform research problem selection and resource allocation.Comment: To appear in proceedings of the 2014 International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP2014

Exploratory topic modeling with distributional semantics

As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015

Neural Topic Modeling with Continual Lifelong Learning

Lifelong learning has recently attracted attention in building machine learning systems that continually accumulate and transfer knowledge to help future learning. Unsupervised topic modeling has been popularly used to discover topics from document collections. However, the application of topic modeling is challenging due to data sparsity, e.g., in a small collection of (short) documents and thus, generate incoherent topics and sub-optimal document representations. To address the problem, we propose a lifelong learning framework for neural topic modeling that can continuously process streams of document collections, accumulate topics and guide future topic modeling tasks by knowledge transfer from several sources to better deal with the sparse data. In the lifelong process, we particularly investigate jointly: (1) sharing generative homologies (latent topics) over lifetime to transfer prior knowledge, and (2) minimizing catastrophic forgetting to retain the past learning via novel selective data augmentation, co-training and topic regularization approaches. Given a stream of document collections, we apply the proposed Lifelong Neural Topic Modeling (LNTM) framework in modeling three sparse document collections as future tasks and demonstrate improved performance quantified by perplexity, topic coherence and information retrieval task.Comment: ICML202

Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory

This paper presents results of topic modeling and network models of topics using the International Conference on Computational Science corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of International Conference on Computational Science, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the keywords of authors. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization topic modeling algorithm to discover topics and labeled and grouped results hierarchically.Comment: Accepted by International Conference on Computational Science (ICCS) 2017 which will be held in Zurich, Switzerland from June 11-June 1

PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset

Recommendation systems create personalized list of items that might interest the user by analyzing the user’s history of past purchases and/or consumption. For rating based systems, most of the traditional methods for recommendation focus on the absolute ratings provided by the users to the items. In this paper, we extend the traditional Matrix Factorization approach for recommendation and propose pairwise relation based factor modeling. While modeling the items in the system, the use of pairwise preferences allow information flow between the items through the preference relations as an additional information. Item feedbacks are available in the form of reviews apart from the rating information. The reviews have textual information that can be really helpful to represent the item’s latent feature vector appropriately. We perform topic modeling of the item reviews and use the topic vectors to guide the joint factor modeling of the users and items and learn their final representations. The proposed method shows promising results in comparison to the state-of-the-art methods in our experiments

Trend mining with Orange – using topic modeling in futures research with the example of urban mobility

Today, assumptions about probable future developments (at least as far as they make use of quantifiable scientific methods and are not pure speculation) are generally based on data from the past. An interesting way to analyze the future through this type of data is text mining or individual methods out of the spectrum of text mining, such as topic modeling. Topic Modeling itself is a combination of quantitative and qualitative methodology and is based on the full spectrum of social science methodology. Therefore, the method is an interesting way for futures research to analyze futures. This publication addresses the question of how a combination of different methods can contribute to trend monitoring or trend mining. For this purpose, a set of scientific publications was first generated with the help of a search query in the Web of Science (WoS), which is the basis for all evaluations and statements and topics. In essence, the method considered here should be more fully integrated into the scientific practice of futures research because it can make a valuable contribution to estimating future development based on past development

Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks

Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin's behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin's own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin's consumption more exploratory than the culture's production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.Comment: Cognition pre-print, published February 2017; 22 pages, plus 17 pages supporting information, 7 pages reference

Topic Analysis of Tweets on the European Refugee Crisis Using Non-negative Matrix Factorization

The ongoing European Refugee Crisis has been one of the most popular trending topics on Twitter for the past 8 months. This paper applies topic modeling on bulks of tweets to discover the hidden patterns within these social media discussions. In particular, we perform topic analysis through solving Non-negative Matrix Factorization (NMF) as an Inexact Alternating Least Squares problem. We accelerate the computation using techniques including tweet sampling and augmented NMF, compare NMF results with different ranks and visualize the outputs through topic representation and frequency plots. We observe that supportive sentiments maintained a strong presence while negative sentiments such as safety concerns have emerged over time