IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v26y2020i4d10.1007_s10588-019-09299-0.html
   My bibliography  Save this article

Authentic chatter

Author

Listed:
  • Bruce Forrester

    (Command Control and Intelligence, Defence Research and Development Canada)

Abstract

This operations research aims to derive an easy but meaningful method for practitioners to identify key influencers and uncover suppressed narratives within a Twitter topic group. This research employs a new concept called “authentic chatter” (analogous to a grass-roots discourse) in combination with influence metrics, content analysis, and commercial-off-the-shelf social media analysis software (NexaIntelligence). The mixed-method exploits the power of social network analysis to determine a small but prominent group of influencers that provides a manageable dataset for the qualitative review of the content. This paper reviews research on social influence and identifies two local influence theories, “indegree” and “retweet”, ideal for topical discussion. Next it reviews Twitter content analysis research looking at specific details on methods. Findings from this past research guide development of a new methodology. The research concludes that use of a prominent group and filtering for authentic chatter increased the signal to noise ratio highlighting important underlying themes within the topic.

Suggested Citation

  • Bruce Forrester, 2020. "Authentic chatter," Computational and Mathematical Organization Theory, Springer, vol. 26(4), pages 382-411, December.
    • Handle: RePEc:spr:comaot:v:26:y:2020:i:4:d:10.1007_s10588-019-09299-0
    DOI: 10.1007/s10588-019-09299-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-019-09299-0
    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/s10588-019-09299-0?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. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    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. Nisar, Sobia & Shafiq, Muhammad, 2019. "Framework for efficient utilisation of social media in Pakistan's healthcare sector," Technology in Society, Elsevier, vol. 56(C), pages 31-43.
    2. Luis-Millán González & José Devís-Devís & Maite Pellicer-Chenoll & Miquel Pans & Alberto Pardo-Ibañez & Xavier García-Massó & Fernanda Peset & Fernanda Garzón-Farinós & Víctor Pérez-Samaniego, 2021. "The Impact of COVID-19 on Sport in Twitter: A Quantitative and Qualitative Content Analysis," IJERPH, MDPI, vol. 18(9), pages 1-20, April.
    3. Han, Chunjia & Yang, Mu & Piterou, Athena, 2021. "Do news media and citizens have the same agenda on COVID-19? an empirical comparison of twitter posts," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    4. Boonyanit Mathayomchan & Viriya Taecharungroj & Walanchalee Wattanacharoensil, 2023. "Evolution of COVID-19 tweets about Southeast Asian Countries: topic modelling and sentiment analyses," Place Branding and Public Diplomacy, Palgrave Macmillan, vol. 19(3), pages 317-334, September.
    5. Elanor Colleoni & Nuccio Ludovico & Illia Laura & Ravindran Kiron, 2021. "Does Sharing Economy Have a Moral Capital? Comparing Semantic Networks in Social Media and News Media," Journal of Management and Sustainability, Canadian Center of Science and Education, vol. 11(2), pages 1-1, December.
    6. Nanath, Krishnadas & Balasubramanian, Sreejith & Shukla, Vinaya & Islam, Nazrul & Kaitheri, Supriya, 2022. "Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    7. Cindy Cheng & Joan Barceló & Allison Spencer Hartnett & Robert Kubinec & Luca Messerschmidt, 2020. "COVID-19 Government Response Event Dataset (CoronaNet v.1.0)," Nature Human Behaviour, Nature, vol. 4(7), pages 756-768, July.
    8. Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020. "Happiness-lost: Did Governments make the right decisions to combat Covid-19?," GLO Discussion Paper Series 556, Global Labor Organization (GLO).
    9. Hongzhou Shen & Yue Ju & Zhijing Zhu, 2023. "Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    10. Gaspar, Rui & Yan, Zheng & Domingos, Samuel, 2019. "Extreme natural and man-made events and human adaptive responses mediated by information and communication technologies' use: A systematic literature review," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 125-135.
    11. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    12. Jiayin Pei & Guang Yu & Xianyun Tian & Maureen Renee Donnelley, 2017. "A new method for early detection of mass concern about public health issues," Journal of Risk Research, Taylor & Francis Journals, vol. 20(4), pages 516-532, April.
    13. Zeynep Ertem & Dorrie Raymond & Lauren Ancel Meyers, 2018. "Optimal multi-source forecasting of seasonal influenza," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-16, September.
    14. Jose L Herrera & Ravi Srinivasan & John S Brownstein & Alison P Galvani & Lauren Ancel Meyers, 2016. "Disease Surveillance on Complex Social Networks," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-16, July.
    15. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    16. Turgut Acikara & Bo Xia & Tan Yigitcanlar & Carol Hon, 2023. "Contribution of Social Media Analytics to Disaster Response Effectiveness: A Systematic Review of the Literature," Sustainability, MDPI, vol. 15(11), pages 1-50, May.
    17. Rodrigo Carrillo-Larco, 2012. "Social networks and public health: use of Twitter by ministries of health," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(4), pages 755-756, August.
    18. Esther Oliver & María Carmen Llasat & Montserrat Llasat-Botija & Javier Díez-Palomar, 2021. "Twitter’s Messages about Hydrometeorological Events. A Study on the Social Impact of Climate Change," Sustainability, MDPI, vol. 13(6), pages 1-24, March.
    19. Xiaodong Yang & Lai Wei & Zhiyue Liu, 2022. "Promoting COVID-19 Vaccination Using the Health Belief Model: Does Information Acquisition from Divergent Sources Make a Difference?," IJERPH, MDPI, vol. 19(7), pages 1-15, March.
    20. Kott, Anne & Limaye, Rupali J., 2016. "Delivering risk information in a dynamic information environment: Framing and authoritative voice in Centers for Disease Control (CDC) and primetime broadcast news media communications during the 2014," Social Science & Medicine, Elsevier, vol. 169(C), pages 42-49.

    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:comaot:v:26:y:2020:i:4:d:10.1007_s10588-019-09299-0. 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.