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A Bimodal Network Approach to Model Topic Dynamics

Author

Listed:
  • Luigi Di Caro
  • Marco Guerzoni
  • Massimiliano Nuccio
  • Giovanni Siragusa

Abstract

This paper presents an intertemporal bimodal network to analyze the evolution of the semantic content of a scientific field within the framework of topic modeling, namely using the Latent Dirichlet Allocation (LDA). The main contribution is the conceptualization of the topic dynamics and its formalization and codification into an algorithm. To benchmark the effectiveness of this approach, we propose three indexes which track the transformation of topics over time, their rate of birth and death, and the novelty of their content. Applying the LDA, we test the algorithm both on a controlled experiment and on a corpus of several thousands of scientific papers over a period of more than 100 years which account for the history of the economic thought.

Suggested Citation

  • Luigi Di Caro & Marco Guerzoni & Massimiliano Nuccio & Giovanni Siragusa, 2017. "A Bimodal Network Approach to Model Topic Dynamics," Papers 1709.09373, arXiv.org.
    • Handle: RePEc:arx:papers:1709.09373
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    References listed on IDEAS

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    1. Mario Cedrini & Magda Fontana, 2018. "Just another niche in the wall? How specialization is changing the face of mainstream economics [Multidisciplinarity, interdisciplinarity, transdisciplinarity, and the sciences]," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 42(2), pages 427-451.
    2. Leydesdorff, Loet & Welbers, Kasper, 2011. "The semantic mapping of words and co-words in contexts," Journal of Informetrics, Elsevier, vol. 5(3), pages 469-475.
    3. Arho Suominen & Hannes Toivanen, 2016. "Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(10), pages 2464-2476, October.
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    Cited by:

    1. Nicola Melluso & Andrea Bonaccorsi & Filippo Chiarello & Gualtiero Fantoni, 2021. "Rapid detection of fast innovation under the pressure of COVID-19," Papers 2102.00197, arXiv.org.

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