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Measuring discursive influence across scholarship

Author

Listed:
  • Aaron Gerow

    (Department of Computing, Goldsmiths, University of London, New Cross, London SE14 6NW, United Kingdom)

  • Yuening Hu

    (Cloud AI, Google, Sunnyvale, CA 94809)

  • Jordan Boyd-Graber

    (Department of Computer Science, University of Maryland, College Park, MD 20742; University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742; iSchool, University of Maryland, College Park, MD 20742; Language Science Center, University of Maryland, College Park, MD 20742)

  • David M. Blei

    (Department of Statistics, Columbia University, New York, NY 10027; Department of Computer Science, Columbia University, New York, NY 10027; Data Science Institute, Columbia University, New York, NY 10027)

  • James A. Evans

    (Department of Sociology, University of Chicago, Chicago, IL 60637; Computation Institute, University of Chicago, Chicago, IL 60637)

Abstract

Assessing scholarly influence is critical for understanding the collective system of scholarship and the history of academic inquiry. Influence is multifaceted, and citations reveal only part of it. Citation counts exhibit preferential attachment and follow a rigid “news cycle” that can miss sustained and indirect forms of influence. Building on dynamic topic models that track distributional shifts in discourse over time, we introduce a variant that incorporates features, such as authorship, affiliation, and publication venue, to assess how these contexts interact with content to shape future scholarship. We perform in-depth analyses on collections of physics research (500,000 abstracts; 102 years) and scholarship generally (JSTOR repository: 2 million full-text articles; 130 years). Our measure of document influence helps predict citations and shows how outcomes, such as winning a Nobel Prize or affiliation with a highly ranked institution, boost influence. Analysis of citations alongside discursive influence reveals that citations tend to credit authors who persist in their fields over time and discount credit for works that are influential over many topics or are “ahead of their time.” In this way, our measures provide a way to acknowledge diverse contributions that take longer and travel farther to achieve scholarly appreciation, enabling us to correct citation biases and enhance sensitivity to the full spectrum of scholarly impact.

Suggested Citation

  • Aaron Gerow & Yuening Hu & Jordan Boyd-Graber & David M. Blei & James A. Evans, 2018. "Measuring discursive influence across scholarship," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(13), pages 3308-3313, March.
    • Handle: RePEc:nas:journl:v:115:y:2018:p:3308-3313
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    Citations

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    Cited by:

    1. Juste Raimbault, 2019. "Exploration of an interdisciplinary scientific landscape," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 617-641, May.
    2. Minchul Lee & Min Song, 2020. "Incorporating citation impact into analysis of research trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1191-1224, August.
    3. Jay Bhattacharya & Mikko Packalen, 2020. "Stagnation and Scientific Incentives," NBER Working Papers 26752, National Bureau of Economic Research, Inc.
    4. Sandeep Soni & Kristina Lerman & Jacob Eisenstein, 2021. "Follow the leader: Documents on the leading edge of semantic change get more citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 478-492, April.
    5. Antonio De Nicola & Gregorio D’Agostino, 2021. "Assessment of gender divide in scientific communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3807-3840, May.
    6. Teplitskiy, Misha & Duede, Eamon & Menietti, Michael & Lakhani, Karim R., 2022. "How status of research papers affects the way they are read and cited," Research Policy, Elsevier, vol. 51(4).
    7. Massimo Franceschet & Giovanni Colavizza, 2020. "Quantifying the higher-order influence of scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 951-963, November.

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