IDEAS home Printed from https://ideas.repec.org/a/bpj/soeuro/v70y2022i1p24-46n7.html
   My bibliography  Save this article

The “Digital Turn” in Transitional Justice Research: Evaluating Image and Text as Data in the Western Balkans

Author

Listed:
  • Kostovicova Denisa

    (European Institute, London School of Economics and Political Science, London, UK)

  • Kerr Rachel

    (Department of War Studies, King’s College, London, UK)

  • Sokolić Ivor

    (School of Humanities, University of Hertfordshire, Hatfield, UK)

  • Fairey Tiffany

    (Department of War Studies, King’s College, London, UK)

  • Redwood Henry

    (London South Bank University, London, UK)

  • Subotić Jelena

    (Department of Political Science, Georgia State University, Atlanta, GA, USA)

Abstract

The “digital turn” has transformed the landscape of transitional justice research. A wealth of data has been created through social media channels, and new digitisation tools have made existing data more easily accessible. This article discusses the ethical and methodological dimensions of using digital data and novel technologies in transitional justice research based on innovative research using digital archives, digitised transcripts, social media (Facebook) content and digital images. The authors review and evaluate how, in each of these domains, new digital technologies have enabled scholars to expand empirical evidence to understand the mechanics of transitional justice by analysing how data is produced and curated, to interrogate ethical dilemmas involved in those processes and to shift the focus from the ability of transitional justice to fulfil normative goals to how transitional justice is enacted and articulated as a process.

Suggested Citation

  • Kostovicova Denisa & Kerr Rachel & Sokolić Ivor & Fairey Tiffany & Redwood Henry & Subotić Jelena, 2022. "The “Digital Turn” in Transitional Justice Research: Evaluating Image and Text as Data in the Western Balkans," Comparative Southeast European Studies, De Gruyter, vol. 70(1), pages 24-46, March.
    • Handle: RePEc:bpj:soeuro:v:70:y:2022:i:1:p:24-46:n:7
    DOI: 10.1515/soeu-2021-0055
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/soeu-2021-0055
    Download Restriction: no
    File URL: https://libkey.io/10.1515/soeu-2021-0055?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. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(2), pages 358-375, May.
    3. Benoit, Kenneth & Conway, Drew & Lauderdale, Benjamin E. & Laver, Michael & Mikhaylov, Slava, 2016. "Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data," American Political Science Review, Cambridge University Press, vol. 110(2), pages 278-295, May.
    4. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
    5. Michael Evans & Wayne McIntosh & Jimmy Lin & Cynthia Cates, 2007. "Recounting the Courts? Applying Automated Content Analysis to Enhance Empirical Legal Research," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 4(4), pages 1007-1039, December.
    6. Berinsky, Adam J. & Huber, Gregory A. & Lenz, Gabriel S., 2012. "Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk," Political Analysis, Cambridge University Press, vol. 20(3), pages 351-368, July.
    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. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.
    2. Anton Oleinik, 2024. "A Bayesian index of association: comparison with other measures and performance," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 277-305, February.
    3. H. Andrew Schwartz & Lyle H. Ungar, 2015. "Data-Driven Content Analysis of Social Media," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 78-94, May.
    4. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    5. Weiss, Max & Zoorob, Michael, 2021. "Political frames of public health crises: Discussing the opioid epidemic in the US Congress," Social Science & Medicine, Elsevier, vol. 281(C).
    6. Sarel, Roee & Demirtas, Melanie, 2021. "Delegation in a multi-tier court system: Are remands in the U.S. federal courts driven by moral hazard?," European Journal of Political Economy, Elsevier, vol. 68(C).
    7. Pierre-Marc Daigneault & Dominic Duval & Louis M. Imbeau, 2018. "Supervised scaling of semi-structured interview transcripts to characterize the ideology of a social policy reform," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2151-2162, September.
    8. Diaf, Sami & Döpke, Jörg & Fritsche, Ulrich & Rockenbach, Ida, 2022. "Sharks and minnows in a shoal of words: Measuring latent ideological positions based on text mining techniques," European Journal of Political Economy, Elsevier, vol. 75(C).
    9. Heike Klüver, 2015. "The promises of quantitative text analysis in interest group research: A reply to Bunea and Ibenskas," European Union Politics, , vol. 16(3), pages 456-466, September.
    10. 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.
    11. Keren Weinshall & Lee Epstein, 2020. "Developing High‐Quality Data Infrastructure for Legal Analytics: Introducing the Israeli Supreme Court Database," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(2), pages 416-434, June.
    12. Elio Amicarelli & Jessica Di Salvatore, 2021. "Introducing the PeaceKeeping Operations Corpus (PKOC)," Journal of Peace Research, Peace Research Institute Oslo, vol. 58(5), pages 1137-1148, September.
    13. Rebecca Cordell & Kristian Skrede Gleditsch & Florian G Kern & Laura Saavedra-Lux, 2020. "Measuring institutional variation across American Indian constitutions using automated content analysis," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 777-788, November.
    14. Caroline Le Pennec, 2024. "Strategic Campaign Communication: Evidence from 30,000 Candidate Manifestos," The Economic Journal, Royal Economic Society, vol. 134(658), pages 785-810.
    15. Greene, Zac & Ceron, Andrea & Schumacher, Gijs & Fazekas, Zoltan, 2016. "The Nuts and Bolts of Automated Text Analysis. Comparing Different Document Pre-Processing Techniques in Four Countries," OSF Preprints ghxj8, Center for Open Science.
    16. Delattre, Laurence & Chanel, Olivier & Livenais, Cecile & Napoléone, Claude, 2015. "Combining discourse analyses to enrich theory: The case of local land-use policies in South Eastern France," Ecological Economics, Elsevier, vol. 113(C), pages 60-75.
    17. Born, Andreas & Janssen, Aljoscha, 2020. "Does a District-Vote Matter for the Behavior of Politicians? A Textual Analysis of Parliamentary Speeches," Working Paper Series 1320, Research Institute of Industrial Economics.
    18. Sanders James & Lisi Giulio & Schonhardt-Bailey Cheryl, 2017. "Themes and Topics in Parliamentary Oversight Hearings: A New Direction in Textual Data Analysis," Statistics, Politics and Policy, De Gruyter, vol. 8(2), pages 153-194, December.
    19. Michael Scharkow, 2013. "Thematic content analysis using supervised machine learning: An empirical evaluation using German online news," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 761-773, February.
    20. Weifeng Zhong, 2016. "The candidates in their own words: A textual analysis of 2016 president primary debates," AEI Economic Perspectives, American Enterprise Institute, April.

    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:bpj:soeuro:v:70:y:2022:i:1:p:24-46:n:7. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.