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The existing topic models usually focus the citation relationships when considering the relationships between documents. To make full use of the information contained in scientific documents, this paper proposes a Multi-Relational Neural Topic Model (MRNTM) based on the three relationships networks between documents. This work comprehensively considers the multiple relationships networks between documents: citation relationship, author relationship, and co-citation relationship, and then proposes a model that combines Variational Auto-Encoder (VAE) with neural networks multi-layer perceptron (MLP). This model not only can be used to learn more representative document topics, but also capture the complex interaction between documents according to their latent topics. The interaction between documents can further promote topic learning. Experiments on two real datasets show that our model can effectively utilize latent topics and the relationship between document networks, and superior to existing models in topic learning and citation link prediction. [ABSTRACT FROM AUTHOR]

In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that Topic Flow Networks are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields. [ABSTRACT FROM AUTHOR]

The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods. [ABSTRACT FROM AUTHOR]

The concept of Earth system science denotes a shift in the scientific discourse from disciplinary accounts of isolated components of the global environment towards the holistic and interdisciplinary treatment of their complex, functional interactions. We measure to what extent the environmental scientific literature of the past three decades reflects this system turn. Our initial dataset consists of 133,670 articles published in 95 relevant journals since 1990. We apply a combination of topic modelling and network analysis. Correlated Topic Models identify latent themes ("topics") in the scientific discourse as well as intertopic correlations. This allows the generation of topic networks and thus the application of network-analytic techniques. We generate and analyze 2 topic networks. The first network focuses on climate linkages in a subset of our corpus consisting only of environmental journals without a climate-specific orientation: as the climate system is constitutional for the notion of Earth system science, a system turn should reflect itself in strong linkages between climate- and non-climate environmental topics. The second network, based on the full dataset, applies community detection to identify broader topical clusters. Here, we expect the system turn to manifest itself in communities with high topical diversity regarding the components of the global environment as well as types of human-nature interactions, rather than reflecting the boundaries between broader research fields. Our results show that climate topics are comparatively weakly connected and less integrated into broader thematic packages than other topics; that linkages frequently reflect conceptual debates rather than functional interactions between substantive environmental components; and that the scientific discourse splits into 4 broader domains, two of those being topically homogeneous and the other two comprising only marginally diverse topics. We conclude that the concept of Earth system science is primarily aspirational in nature rather than reflecting an empirical shift in the structure of the scientific discourse. [ABSTRACT FROM AUTHOR]

The exponential growth of social media has brought with it an increasing propagation of hate speech and hate based propaganda. Hate speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristics such as race, colour, ethnicity, gender, sexual orientation, nationality, religion. Online hate diffusion has now developed into a serious problem and this has led to a number of international initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures. The aim of this paper is to analyse the knowledge structure of hate speech literature and the evolution of related topics. We apply co-word analysis methods to identify different topics treated in the field. The analysed database was downloaded from Scopus, focusing on a number of publications during the last thirty years. Topic and network analyses of literature showed that the main research topics can be divided into three areas: "general debate hate speech versus freedom of expression","hate-speech automatic detection and classification by machine-learning strategies", and "gendered hate speech and cyberbullying". The understanding of how research fronts interact led to stress the relevance of machine learning approaches to correctly assess hatred forms of online speech. [ABSTRACT FROM AUTHOR]

This paper investigates how scholars in the digital humanities use Twitter for informal scholarly communication. In particular, the paper observes the hosting of an annual conference over a number of years by one association in order to see whether there was a change in the network configuration structure, the influential scholars in the network, the information sources, and the tweet contents. Annual conferences held by the Association of Internet Researchers over 3 years are used for data collection. According to our result, while the Twitter communication network developed into a bigger network, the basic form of the network configuration remained stable as a Tight Crowd structure and the core influential people were not much changed. Analyses on information source and content found topic changes in each year but consistency in the kind of information source and content. [ABSTRACT FROM AUTHOR]

Many contemporary social and public health problems do not fit neatly into the research fields typically found in universities. With this in mind, researchers and funding agencies have devoted increasing attention to projects that span multiple disciplines. However, comparatively little attention has been paid to how these projects evolve over time. This relative neglect is in part attributable to a lack of theory on the dynamic nature of such projects. In this paper, we describe how research programs can move through various states of integration including disciplinarity, multidisciplinarity, interdisciplinarity and transdisciplinarity. We link this insight to computational techniques-topic models-to explore one of the most vibrant and pressing contemporary research areas-research on HIV/AIDS. Topic models of over 9000 abstracts from two prominent journals illustrate how research on HIV/AIDS has evolved from a high to a lower level of integration. The topic models motivate a more detailed historical analysis of HIV/AIDS research and, together, they highlight the dynamic nature of knowledge production. We conclude by discussing the role of computational social science in dynamic models of interdisciplinarity. [ABSTRACT FROM AUTHOR]

The research streams of transition economies and emerging markets have some common ground, but yet differ. The goal of this study is to provide a better understanding of the commonalities and differences regarding trends and topics of this cross-disciplinary research area. We employ the novel method of topic models on a corpus of nearly 6,000 articles in more than 600 journals from 1995 to 2012 to identify 25 topics and analyze their trends and use across scope (transition or emerging), disciplines (business or economics) and geography (countries or regions). [ABSTRACT FROM AUTHOR]

Within the same research field, different subfields and topics may exhibit varied citation behaviors and scholarly communication patterns. For a more effect scientific evaluation at the topic level, this study proposes a topic-based PageRank approach. This approach aims to evaluate the scientific impact of research entities (e.g., papers, authors, journals, and institutions) at the topic-level. The proposed topic-based PageRank, when applied to a data set on library and information science publications, has effectively detected a variety of research topics and identified authors, papers, and journals of the highest impact from each topic. Evaluation results show that compared with the standard PageRank and a topic modeling technique, the proposed topic-based PageRank has the best performance on relevance and impact. Different perspectives of organizing scientific literature are also discussed and this study recommends the mode of organization that integrates stable research domains and dynamic topics. [ABSTRACT FROM AUTHOR]