IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v7y2020i1d10.1057_s41599-020-0495-3.html
   My bibliography  Save this article

Situated data analysis: a new method for analysing encoded power relationships in social media platforms and apps

Author

Listed:
  • Jill Walker Rettberg

    (University of Bergen)

Abstract

This paper proposes situated data analysis as a new method for analysing social media platforms and digital apps. An analysis of the fitness tracking app Strava is used as a case study to develop and illustrate the method. Building upon Haraway’s concept of situated knowledge and recent research on algorithmic bias, situated data analysis allows researchers to analyse how data is constructed, framed and processed for different audiences and purposes. Situated data analysis recognises that data is always partial and situated, and it gives scholars tools to analyse how it is situated, and what effects this may have. Situated data analysis examines representations of data, like data visualisations, which are meant for humans, and operations with data, which occur when personal or aggregate data is processed algorithmically by machines, for instance to predict behaviour patterns, adjust services or recommend content. The continuum between representational and operational uses of data is connected to different power relationships between platforms, users and society, ranging from normative disciplinary power and technologies of the self to environmental power, a concept that has begun to be developed in analyses of digital media as a power that is embedded in the environment, making certain actions easier or more difficult, and thus remaining external to the subject, in contrast to disciplinary power which is internalised. Situated data analysis can be applied to the aggregation, representation and operationalization of personal data in social media platforms like Facebook or YouTube, or by companies like Google or Amazon, and gives researchers more nuanced tools for analysing power relationships between companies, platforms and users.

Suggested Citation

  • Jill Walker Rettberg, 2020. "Situated data analysis: a new method for analysing encoded power relationships in social media platforms and apps," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.
    • Handle: RePEc:pal:palcom:v:7:y:2020:i:1:d:10.1057_s41599-020-0495-3
    DOI: 10.1057/s41599-020-0495-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-020-0495-3
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-020-0495-3?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sendhil Mullainathan & Ziad Obermeyer, 2017. "Does Machine Learning Automate Moral Hazard and Error?," American Economic Review, American Economic Association, vol. 107(5), pages 476-480, May.
    2. Gustavo Romanillos & Martin Zaltz Austwick & Dick Ettema & Joost De Kruijf, 2016. "Big Data and Cycling," Transport Reviews, Taylor & Francis Journals, vol. 36(1), pages 114-133, January.
    3. Griffin, Greg Phillip & Jiao, Junfeng, 2015. "Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus," SocArXiv 5gy3u, Center for Open Science.
    4. Jestico, Ben & Nelson, Trisalyn & Winters, Meghan, 2016. "Mapping ridership using crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 52(C), pages 90-97.
    5. Siddharth Sareen & Andrea Saltelli & Kjetil Rommetveit, 2020. "Ethics of quantification: illumination, obfuscation and performative legitimation," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-5, December.
    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. McArthur, David Philip & Hong, Jinhyun, 2019. "Visualising where commuting cyclists travel using crowdsourced data," Journal of Transport Geography, Elsevier, vol. 74(C), pages 233-241.
    2. Raturi, Varun & Hong, Jinhyun & McArthur, David Philip & Livingston, Mark, 2021. "The impact of privacy protection measures on the utility of crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 92(C).
    3. Hochmair, Hartwig H. & Bardin, Eric & Ahmouda, Ahmed, 2019. "Estimating bicycle trip volume for Miami-Dade county from Strava tracking data," Journal of Transport Geography, Elsevier, vol. 75(C), pages 58-69.
    4. Md Mintu Miah & Kate Kyung Hyun & Stephen P. Mattingly & Hannan Khan, 2023. "Estimation of daily bicycle traffic using machine and deep learning techniques," Transportation, Springer, vol. 50(5), pages 1631-1684, October.
    5. Nina Cesare & Pallavi Dwivedi & Quynh C. Nguyen & Elaine O. Nsoesie, 2019. "Use of social media, search queries, and demographic data to assess obesity prevalence in the United States," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-9, December.
    6. Stella R. Harden & Nadine Schuurman & Peter Keller & Scott A. Lear, 2022. "Neighborhood Characteristics Associated with Running in Metro Vancouver: A Preliminary Analysis," IJERPH, MDPI, vol. 19(21), pages 1-13, November.
    7. Kyuhyun Lee & Ipek N. Sener, 2019. "Understanding Potential Exposure of Bicyclists on Roadways to Traffic-Related Air Pollution: Findings from El Paso, Texas, Using Strava Metro Data," IJERPH, MDPI, vol. 16(3), pages 1-20, January.
    8. Xie, Xiao-Feng & Wang, Zunjing Jenipher, 2018. "Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area," Journal of Transport Geography, Elsevier, vol. 71(C), pages 84-102.
    9. Mark Livingston & David McArthur & Jinhyun Hong & Kirstie English, 2021. "Predicting cycling volumes using crowdsourced activity data," Environment and Planning B, , vol. 48(5), pages 1228-1244, June.
    10. Mário Meireles & Paulo J. G. Ribeiro, 2020. "Digital Platform/Mobile App to Boost Cycling for the Promotion of Sustainable Mobility in Mid-Sized Starter Cycling Cities," Sustainability, MDPI, vol. 12(5), pages 1-27, March.
    11. Munira, Sirajum & Sener, Ipek N., 2020. "A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin, Texas," Journal of Transport Geography, Elsevier, vol. 88(C).
    12. Radzimski, Adam & Dzięcielski, Michał, 2021. "Exploring the relationship between bike-sharing and public transport in Poznań, Poland," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 189-202.
    13. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    14. Alattar, Mohammad Anwar & Cottrill, Caitlin & Beecroft, Mark, 2021. "Public participation geographic information system (PPGIS) as a method for active travel data acquisition," Journal of Transport Geography, Elsevier, vol. 96(C).
    15. Hong, Jinhyun & Philip McArthur, David & Stewart, Joanna L., 2020. "Can providing safe cycling infrastructure encourage people to cycle more when it rains? The use of crowdsourced cycling data (Strava)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 109-121.
    16. Apanasevic, Tatjana & Rudmark, Daniel, 2021. "Crowdsourcing and Public Transportation: Barriers and Opportunities," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238005, International Telecommunications Society (ITS).
    17. Ali Al-Ramini & Mohammad A Takallou & Daniel P Piatkowski & Fadi Alsaleem, 2022. "Quantifying changes in bicycle volumes using crowdsourced data," Environment and Planning B, , vol. 49(6), pages 1612-1630, July.
    18. Hamsa Bastani, 2021. "Predicting with Proxies: Transfer Learning in High Dimension," Management Science, INFORMS, vol. 67(5), pages 2964-2984, May.
    19. Ziyuan Wang, 2023. "Spatial Differentiation Characteristics of Rural Areas Based on Machine Learning and GIS Statistical Analysis—A Case Study of Yongtai County, Fuzhou City," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
    20. Lindsey Conrow & Siân Mooney & Elizabeth A Wentz, 2021. "The association between residential housing prices, bicycle infrastructure and ridership volumes," Urban Studies, Urban Studies Journal Limited, vol. 58(4), pages 787-808, March.

    More about this item

    Statistics

    Access and download statistics

    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:pal:palcom:v:7:y:2020:i:1:d:10.1057_s41599-020-0495-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.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.