IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v53y2019i2d10.1007_s11135-018-0787-5.html
   My bibliography  Save this article

Language and interaction: applying sociolinguistics to social network analysis

Author

Listed:
  • David K. Diehl

    (Vanderbilt University)

Abstract

In recent years social network analysis, influenced by relational sociology, has taken a cultural turn. One result has been a growing interest in the cultural, and not just structural, aspects of social networks. And yet, while relational literature conceptualizes network ties as being interactionally constructed through cultural processes, relationalist inspired quantitative network analysts have rarely made face-to-face interaction a focus of study. More often, these scholars have adopted an interpretive approach and examined the network structure of cultural forms and belief systems. This article argues that network analysis is missing an opportunity to study procedural aspects of culture by taking advantage of our growing ability to collect and analyze streaming data of face-to-face interaction. To productively do so, however, network studies of interaction can apply ideas from sociolinguistics related to the context and style of communication in order to capture cultural aspects of interaction.

Suggested Citation

  • David K. Diehl, 2019. "Language and interaction: applying sociolinguistics to social network analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 757-774, March.
    • Handle: RePEc:spr:qualqt:v:53:y:2019:i:2:d:10.1007_s11135-018-0787-5
    DOI: 10.1007/s11135-018-0787-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-018-0787-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-018-0787-5?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. Jan Fuhse & Sophie Mützel, 2011. "Tackling connections, structure, and meaning in networks: quantitative and qualitative methods in sociological network research," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(5), pages 1067-1089, August.
    2. H Andrew Schwartz & Johannes C Eichstaedt & Margaret L Kern & Lukasz Dziurzynski & Stephanie M Ramones & Megha Agrawal & Achal Shah & Michal Kosinski & David Stillwell & Martin E P Seligman & Lyle H U, 2013. "Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David K. Diehl, 2023. "What exactly is “social” about social networks?: Accounting for socio-cultural context in networks of human interaction," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1369-1392, April.
    2. Jan A. Fuhse, 2023. "Analyzing networks in communication: a mixed methods study of a political debate," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1207-1230, April.

    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. Jean M. Twenge & Hannah VanLandingham & W. Keith Campbell, 2017. "The Seven Words You Can Never Say on Television: Increases in the Use of Swear Words in American Books, 1950-2008," SAGE Open, , vol. 7(3), pages 21582440177, August.
    2. Gallus, Jana & Bhatia, Sudeep, 2020. "Gender, power and emotions in the collaborative production of knowledge: A large-scale analysis of Wikipedia editor conversations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 115-130.
    3. Liang Xu & Min Xu & Zehua Jiang & Xin Wen & Yishan Liu & Zaoyi Sun & Hongting Li & Xiuying Qian, 2023. "How have music emotions been described in Google books? Historical trends and corpus differences," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    4. Ronaldo Tavares Souza, 2020. "Box-scheme as alternative food network—the economic integration between consumers and producers," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 8(1), pages 1-25, December.
    5. Feng Wang & Jing Ren & Juan Liu & Mingru Dong & Bin Yan & Hui Zhao, 2021. "Spatial correlation network and population mobility effect of regional haze pollution: empirical evidence from Pearl River Delta urban agglomeration in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 15881-15896, November.
    6. Karol Król & Dariusz Zdonek, 2021. "Most Often Motivated by Social Media: The Who, the What, and the How Much—Experience from Poland," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    7. Jörn H. Block & Walter Diegel & Christian Fisch, 2024. "How venture capital funding changes an entrepreneur’s digital identity: more self-confidence and professionalism but less authenticity!," Review of Managerial Science, Springer, vol. 18(8), pages 2287-2319, August.
    8. Vivek Kulkarni & Margaret L Kern & David Stillwell & Michal Kosinski & Sandra Matz & Lyle Ungar & Steven Skiena & H Andrew Schwartz, 2018. "Latent human traits in the language of social media: An open-vocabulary approach," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
    9. Kirschbaum, Charles, 2013. "A 'Nested Narratives' Project: theoretical grounding and methodological implications," Insper Working Papers wpe_308, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    10. Jong Hwan Suh, 2022. "Machine-Learning-Based Gender Distribution Prediction from Anonymous News Comments: The Case of Korean News Portal," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    11. Ratinen, Mari, 2019. "Social embeddedness of policy actors. The failure of consumer-owned wind energy in Finland," Energy Policy, Elsevier, vol. 128(C), pages 735-743.
    12. Karel Hrazdil & Jiri Novak & Rafael Rogo & Christine Wiedman & Ray Zhang, 2020. "Measuring executive personality using machine‐learning algorithms: A new approach and audit fee‐based validation tests," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 47(3-4), pages 519-544, March.
    13. Luo, Shuli & He, Sylvia Y., 2021. "Understanding gender difference in perceptions toward transit services across space and time: A social media mining approach," Transport Policy, Elsevier, vol. 111(C), pages 63-73.
    14. Gow, Ian D. & Kaplan, Steven N. & Larcker, David F. & Zakolyukina, Anastasia A., 2016. "CEO Personality and Firm Policies," Research Papers 3444, Stanford University, Graduate School of Business.
    15. Mikkel Wallentin, 2018. "Sex differences in post-stroke aphasia rates are caused by age. A meta-analysis and database query," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-18, December.
    16. Hannes Rosenbusch & Maya Aghaei & Anthony M. Evans & Marcel Zeelenberg, 2021. "Psychological trait inferences from women’s clothing: human and machine prediction," Journal of Computational Social Science, Springer, vol. 4(2), pages 479-501, November.
    17. Eszter Hargittai, 2015. "Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 63-76, May.
    18. Rachel Winter & Anna Lavis, 2021. "The Impact of COVID-19 on Young People’s Mental Health in the UK: Key Insights from Social Media Using Online Ethnography," IJERPH, MDPI, vol. 19(1), pages 1-13, December.
    19. Daniel Preoţiuc-Pietro & Svitlana Volkova & Vasileios Lampos & Yoram Bachrach & Nikolaos Aletras, 2015. "Studying User Income through Language, Behaviour and Affect in Social Media," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    20. Luke Sloan & Jeffrey Morgan & Pete Burnap & Matthew Williams, 2015. "Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-20, March.

    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:spr:qualqt:v:53:y:2019:i:2:d:10.1007_s11135-018-0787-5. 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: http://www.springer.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.