IDEAS home Printed from https://ideas.repec.org/a/cog/meanco/v12y2024a7851.html
   My bibliography  Save this article

Audio-as-Data Tools: Replicating Computational Data Processing

Author

Listed:
  • Josephine Lukito

    (School of Journalism and Media, University of Texas at Austin, USA)

  • Jason Greenfield

    (Center for Social Media and Politics, New York University, USA)

  • Yunkang Yang

    (Department of Communication & Journalism, Texas A&M University, USA)

  • Ross Dahlke

    (Department of Communication, Stanford University, USA)

  • Megan A. Brown

    (School of Information, University of Michigan, USA)

  • Rebecca Lewis

    (Department of Communication, Stanford University, USA)

  • Bin Chen

    (School of Journalism and Media, University of Texas at Austin, USA / Journalism and Media Studies Centre, University of Hong Kong)

Abstract

The rise of audio-as-data in social science research accentuates a fundamental challenge: establishing reproducible and reliable methodologies to guide this emerging area of study. In this study, we focus on the reproducibility of audio-as-data preparation methods in computational communication research and evaluate the accuracy of popular audio-as-data tools. We analyze automated transcription and computational phonology tools applied to 200 episodes of conservative talk shows hosted by Rush Limbaugh and Alex Jones. Our findings reveal that the tools we tested are highly accurate. However, despite different transcription and audio signal processing tools yield similar results, subtle yet significant variations could impact the findings’ reproducibility. Specifically, we find that discrepancies in automated transcriptions and auditory features such as pitch and intensity underscore the need for meticulous reproduction of data preparation procedures. These insights into the variability introduced by different tools stress the importance of detailed methodological reporting and consistent processing techniques to ensure the replicability of research outcomes. Our study contributes to the broader discourse on replicability and reproducibility by highlighting the nuances of audio data preparation and advocating for more transparent and standardized practices in this area.

Suggested Citation

  • Josephine Lukito & Jason Greenfield & Yunkang Yang & Ross Dahlke & Megan A. Brown & Rebecca Lewis & Bin Chen, 2024. "Audio-as-Data Tools: Replicating Computational Data Processing," Media and Communication, Cogitatio Press, vol. 12.
    • Handle: RePEc:cog:meanco:v12:y:2024:a:7851
    DOI: 10.17645/mac.7851
    as

    Download full text from publisher

    File URL: https://www.cogitatiopress.com/mediaandcommunication/article/view/7851
    Download Restriction: no
    File URL: https://libkey.io/10.17645/mac.7851?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Denny, Matthew J. & Spirling, Arthur, 2018. "Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It," Political Analysis, Cambridge University Press, vol. 26(2), pages 168-189, April.
    2. Christina Wurst, 2022. "Bread and Plots: Conspiracy Theories and the Rhetorical Style of Political Influencer Communities on YouTube," Media and Communication, Cogitatio Press, vol. 10(4), pages 213-223.
    3. David O. Dowling & Patrick R. Johnson & Brian Ekdale, 2022. "Hijacking Journalism: Legitimacy and Metajournalistic Discourse in Right-Wing Podcasts," Media and Communication, Cogitatio Press, vol. 10(3), pages 17-27.
    4. Knox, Dean & Lucas, Christopher, 2021. "A Dynamic Model of Speech for the Social Sciences," American Political Science Review, Cambridge University Press, vol. 115(2), pages 649-666, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    2. Purwoko Haryadi Santoso & Edi Istiyono & Haryanto & Wahyu Hidayatulloh, 2022. "Thematic Analysis of Indonesian Physics Education Research Literature Using Machine Learning," Data, MDPI, vol. 7(11), pages 1-41, October.
    3. Camilla Salvatore & Silvia Biffignandi & Annamaria Bianchi, 2022. "Corporate Social Responsibility Activities Through Twitter: From Topic Model Analysis to Indexes Measuring Communication Characteristics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1217-1248, December.
    4. Jason Anastasopoulos & George J. Borjas & Gavin G. Cook & Michael Lachanski, 2018. "Job Vacancies, the Beveridge Curve, and Supply Shocks: The Frequency and Content of Help-Wanted Ads in Pre- and Post-Mariel Miami," NBER Working Papers 24580, National Bureau of Economic Research, Inc.
    5. Ashley Hinck, 2022. "Editorial: Populism in and Through Online Communities," Media and Communication, Cogitatio Press, vol. 10(4), pages 105-108.
    6. Lorien Stice-Lawrence, 2022. "Practical issues to consider when working with big data," Review of Accounting Studies, Springer, vol. 27(3), pages 1117-1124, September.
    7. Tyler Andrew Scott & Nicola Ulibarri & Omar Perez Figueroa, 2020. "NEPA and National Trends in Federal Infrastructure Siting in the United States," Review of Policy Research, Policy Studies Organization, vol. 37(5), pages 605-633, September.
    8. Seraphine F. Maerz & Carsten Q. Schneider, 2020. "Comparing public communication in democracies and autocracies: automated text analyses of speeches by heads of government," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(2), pages 517-545, April.
    9. Iasmin Goes, 2023. "Examining the effect of IMF conditionality on natural resource policy," Economics and Politics, Wiley Blackwell, vol. 35(1), pages 227-285, March.
    10. Latifi, Albina & Naboka-Krell, Viktoriia & Tillmann, Peter & Winker, Peter, 2024. "Fiscal policy in the Bundestag: Textual analysis and macroeconomic effects," European Economic Review, Elsevier, vol. 168(C).
    11. Yeomans, Michael, 2021. "A concrete example of construct construction in natural language," Organizational Behavior and Human Decision Processes, Elsevier, vol. 162(C), pages 81-94.
    12. Bastiaan Bruinsma & Moa Johansson, 2024. "Finding the structure of parliamentary motions in the Swedish Riksdag 1971–2015," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3275-3301, August.
    13. Justyna Klejdysz & Robin L. Lumsdaine, 2023. "Shifts in ECB Communication: A Textual Analysis of the Press Conference," International Journal of Central Banking, International Journal of Central Banking, vol. 19(2), pages 473-542, June.
    14. LIM Jaehwan & ITO Asei & ZHANG Hongyong, 2023. "Policy Agenda and Trajectory of the Xi Jinping Administration: Textual Evidence from 2012 to 2022," Policy Discussion Papers 23008, Research Institute of Economy, Trade and Industry (RIETI).
    15. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
    16. Wang, Yan & Luo, Ting, 2023. "Politicizing for the idol: China’s idol fandom nationalism in pandemic," LSE Research Online Documents on Economics 117741, London School of Economics and Political Science, LSE Library.
    17. Camilla Salvatore, 2023. "Inference with non-probability samples and survey data integration: a science mapping study," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 83-107, April.
    18. Jaeho Choi & Anoop Menon & Haris Tabakovic, 2021. "Using machine learning to revisit the diversification–performance relationship," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1632-1661, September.
    19. Miklos Sebők & Zoltán Kacsuk & Ákos Máté, 2022. "The (real) need for a human touch: testing a human–machine hybrid topic classification workflow on a New York Times corpus," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3621-3643, October.
    20. Hung, Shih-Chang & Chang, Shu-Chen, 2023. "Framing the virus: The political, economic, biomedical and social understandings of the COVID-19 in Taiwan," Technological Forecasting and Social Change, Elsevier, vol. 188(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cog:meanco:v12:y:2024:a:7851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: António Vieira or IT Department (email available below). General contact details of provider: https://www.cogitatiopress.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.