Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation

Syed, Shaheen; Spruit, Marco

doi:10.1109/dsaa.2017.61

Cited by 266 publications

(192 citation statements)

References 28 publications

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“…To uncover latent topics, the topic model method latent Dirichlet allocation (LDA) (Blei, ; Blei, Ng, & Jordan, ) was used. All pre‐processing steps to suitably prepare documents for statistical topical inference (Hoffman, Blei, & Bach, ) are described in our previous work (Syed et al, ), which are highly optimised for the fisheries domain (Syed & Spruit, , , ). With LDA, the number of topics needs to be specified in advance, analogous to most unsupervised methods such as k‐means clustering or Gaussian mixture models.…”

Section: Methodsmentioning

confidence: 99%

Mapping the global network of fisheries science collaboration

et al. 2019

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As socio‐environmental problems have proliferated over the past decades, one narrative which has captured the attention of policymakers and scientists has been the need for collaborative research that spans traditional boundaries. Collaboration, it is argued, is imperative for solving these problems. Understanding how collaboration is occurring in practice is important, however, and may help explain the idea space across a field. In an effort to make sense of the shape of fisheries science, here we construct a co‐authorship network of the field, from a data set comprising 73,240 scientific articles, drawn from 50 journals and published between 2000 and 2017. Using a combination of social network analysis and machine learning, the work first maps the global structure of scientific collaboration amongst fisheries scientists at the author, country and institutional levels. Second, it uncovers the hidden subgroups—here country clusters and communities of authors—within the network, detailing also the topical focus, publication outlets and relative impact of the largest fisheries science communities. We find that whilst the fisheries science network is becoming more geographically extensive, it is simultaneously becoming more intensive. The uncovered network exhibits characteristics suggestive of a thin style of collaboration, and groupings that are more regional than they are global. Although likely shaped by an array of overlapping micro‐ and macro‐level factors, the analysis reveals a number of political–economic patterns that merit reflection by both fisheries scientists and policymakers.

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Section: Methodsmentioning

confidence: 99%

Mapping the global network of fisheries science collaboration

et al. 2019

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“…We downloaded full‐text research articles published in the 21 journals covering fisheries aspects for a time span of 26 years (1990–2016) to allow for enough variation in publication trends. Analysing full‐text articles, compared to just abstract data, results in more detailed and higher quality topics (Syed & Spruit, ). Only research articles were considered, and other types of publications, such as errata, conference reports, forewords, announcements, dedications, letters, comments, and book reviews, were excluded.…”

Section: Methodsmentioning

confidence: 99%

“…Analysing full-text articles, compared to just abstract data, results in more detailed and higher quality topics (Syed & Spruit, 2017).…”

Section: Creating the Data Setmentioning

confidence: 99%

Narrow lenses for capturing the complexity of fisheries: A topic analysis of fisheries science from 1990 to 2016

2018

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Despite increased fisheries science output and publication outlets, the global crisis in fisheries management is as present as ever. Since a narrow research focus may be a contributing factor to this failure, this study uncovers topics in fisheries research and their trends over time. This interdisciplinary research evaluates whether science is diversifying fisheries research topics in an attempt to capture the complexity of the fisheries system, or whether it is multiplying research on similar topics, attempting to achieve an in-depth, but possibly marginal, understanding of a few selected compo- This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…In total, 22,236 full-text research articles from 13 top-tier fisheries journals were downloaded using automated download scripts, as well as by utilizing the available application programming interfaces (APIs) offered by the publishers. The use of full-text articles, in contrast to only using abstracts, has shown to increase topic quality and provide a more detailed overview of the latent topics permeating a document collection (Syed and Spruit, 2017). Table 1 provides an overview of the complete dataset utilized in this study.…”

Section: Creating the Datasetmentioning

confidence: 99%

Using Machine Learning to Uncover Latent Research Topics in Fishery Models

Syed

Weber

2018

Reviews in Fisheries Science & Aquaculture

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Modeling has become the most commonly used method in fisheries science, with numerous types of models and approaches available today. The large variety of models and the overwhelming amount of scientific literature published yearly can make it difficult to effectively access and use the output of fisheries modeling publications. In particular, the underlying topic of an article cannot always be detected using keyword searches. As a consequence, identifying the developments and trends within fisheries modeling research can be challenging and time-consuming. This paper utilizes a machine learning algorithm to uncover hidden topics and subtopics from peer-reviewed fisheries modeling publications and identifies temporal trends using 22,236 full-text articles extracted from 13 top-tier fisheries journals from 1990 to 2016. Two modeling topics were discovered: estimation models (a topic that contains the idea of catch, effort, and abundance estimation) and stock assessment models (a topic on the assessment of the current state of a fishery and future projections of fish stock responses and management effects). The underlying modeling subtopics show a change in the research focus of modeling publications over the last 26 years.

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Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation

Cited by 266 publications

References 28 publications

Mapping the global network of fisheries science collaboration

Mapping the global network of fisheries science collaboration

Narrow lenses for capturing the complexity of fisheries: A topic analysis of fisheries science from 1990 to 2016

Using Machine Learning to Uncover Latent Research Topics in Fishery Models

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