IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v54y2020i1d10.1007_s11135-019-00952-z.html
   My bibliography  Save this article

Social roles and structural signatures of top influentials in the #prayforparis Twitter network

Author

Listed:
  • Miyoung Chong

    (University of North Texas)

  • Hae Jung Maria Kim

    (University of North Texas)

Abstract

Scholars have shown much interest in whether diffusion is inflated through planting a piece of information by influential people (influentials). Although a few attempts have been made to discover structural gaps or gap fillers in the Twitter network, these efforts mainly concentrated on applying topological approaches to detect influentials in online networks. Further, though many studies explored diffusion on the Twitter network, they rarely examined the phenomenon with a theoretical framework. Through the #prayforparis Twitter network, this study attempted (1) to identify top influentials by applying multiple centrality measures and word frequency measures and (2) to examine social roles based on structural signatures of the Twitter network through the lens of the Diffusion of Innovation Theory. To fulfill the objectives of this study, the authors employed an innovative multi-method approach combining Social Network Analysis, word frequency analysis via NodeXL and R, and a qualitative approach to examine behavioral and structural relationships of the #prayforparis Twitter network. Top influentials of the network were pop music celebrities who shared condolences to the victims of the 2015 Paris attacks through their tweets. This study identified “celebrity” and “fan” as social roles based on the structural and qualitative analysis of the network as well as metrical examinations, including indegree and outdegree counts of the social roles of the “celebrities” and “fans.” Justin Bieber, the most dominant influential individual in the #prayforparis Twitter network, functioned as a breaking news provider through his tweet about the death of his friend during the Paris attacks. By filling the gap from the past studies, this study utilizes the theoretical improvement in the diffusion research as well as contributes to the methodological approach about influentials and social roles in the Twitter network.

Suggested Citation

  • Miyoung Chong & Hae Jung Maria Kim, 2020. "Social roles and structural signatures of top influentials in the #prayforparis Twitter network," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 315-333, February.
    • Handle: RePEc:spr:qualqt:v:54:y:2020:i:1:d:10.1007_s11135-019-00952-z
    DOI: 10.1007/s11135-019-00952-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-019-00952-z
    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/s11135-019-00952-z?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. Al-garadi, Mohammed Ali & Varathan, Kasturi Dewi & Ravana, Sri Devi, 2017. "Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 278-288.
    2. Shaobin Huang & Tianyang Lv & Xizhe Zhang & Yange Yang & Weimin Zheng & Chao Wen, 2014. "Identifying Node Role in Social Network Based on Multiple Indicators," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-16, August.
    3. Yezheng Liu & Fei Du & Jianshan Sun & Thushari Silva & Yuanchun Jiang & Tingting Zhu, 2019. "Identifying social roles using heterogeneous features in online social networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(7), pages 660-674, July.
    4. Jaeung Lee & Manish Agrawal & H. R. Rao, 2015. "Message diffusion through social network service: The case of rumor and non-rumor related tweets during Boston bombing 2013," Information Systems Frontiers, Springer, vol. 17(5), pages 997-1005, October.
    5. Han Park & Mike Thelwall, 2008. "Link analysis: Hyperlink patterns and social structure on politicians’ Web sites in South Korea," Quality & Quantity: International Journal of Methodology, Springer, vol. 42(5), pages 687-697, October.
    6. Noriko Hara & Madelyn Rose Sanfilippo, 2017. "Analysis of roles in engaging contentious online discussions in science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(8), pages 1953-1966, August.
    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. HaeJung Maria Kim & Kyung Wha Oh & Hye Jung Jung, 2020. "Socialization on Sustainable Networks: The Case of eBay Green’s Facebook," Sustainability, MDPI, vol. 12(8), pages 1-15, April.
    2. Muhammad Riaz & Sherani, 2021. "Investigation of information sharing via multiple social media platforms: a comparison of Facebook and WeChat adoption," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1751-1773, October.

    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. Naveena Prakasam & Louisa Huxtable-Thomas, 2021. "Reddit: Affordances as an Enabler for Shifting Loyalties," Information Systems Frontiers, Springer, vol. 23(3), pages 723-751, June.
    2. Jiang, Syuan-Yi, 2022. "Transition and innovation ecosystem – investigating technologies, focal actors, and institution in eHealth innovations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Seong Cho & Han Park, 2013. "A qualitative analysis of cross-cultural new media research: SNS use in Asia and the West," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2319-2330, June.
    4. Yu‐Ru Lin & Drew Margolin & Xidao Wen, 2017. "Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1580-1605, August.
    5. Marta Poblet & Esteban García-Cuesta & Pompeu Casanovas, 2018. "Crowdsourcing roles, methods and tools for data-intensive disaster management," Information Systems Frontiers, Springer, vol. 20(6), pages 1363-1379, December.
    6. Shakibian, Hadi & Charkari, Nasrollah Moghadam, 2018. "Statistical similarity measures for link prediction in heterogeneous complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 248-263.
    7. Mingxin Gan & Lily Sun & Rui Jiang, 2019. "GLORY: Exploration and integration of global and local correlations to improve personalized online social recommendations," Information Systems Frontiers, Springer, vol. 21(4), pages 925-939, August.
    8. Chung Joo Chung & Han Woo Park, 2012. "Web visibility of scholars in media and communication journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(1), pages 207-215, October.
    9. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
    10. Marta Poblet & Esteban García-Cuesta & Pompeu Casanovas, 0. "Crowdsourcing roles, methods and tools for data-intensive disaster management," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    11. Muhammad Omar & Arif Mehmood & Gyu Sang Choi & Han Woo Park, 2017. "Global mapping of artificial intelligence in Google and Google Scholar," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1269-1305, December.
    12. Jiexun Li & Xiaohui Chang, 2023. "Combating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media," Information Systems Frontiers, Springer, vol. 25(4), pages 1479-1493, August.
    13. Besiki Stvilia & Shuheng Wu & Dong Joon Lee, 2018. "Researchers’ participation in and motivations for engaging with research information management systems," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-24, February.
    14. Jiang, Meiling & Gao, Qingwu & Zhuang, Jun, 2021. "Reciprocal spreading and debunking processes of online misinformation: A new rumor spreading–debunking model with a case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    15. Ma, Ning & Liu, Yijun & Chi, Yuxue, 2018. "Influencer discovery algorithm in a multi-relational network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 415-425.
    16. Jiang, Cheng & Liu, Zhonghua, 2019. "Detecting multiple key players under the positive effect by using a distance-based connectivity approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    17. Han Zheng & Dion Hoe‐Lian Goh & Edmund Wei Jian Lee & Chei Sian Lee & Yin‐Leng Theng, 2022. "Understanding the effects of message cues on COVID‐19 information sharing on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(6), pages 847-862, June.
    18. Jyoti Prakash Singh & Abhinav Kumar & Nripendra P. Rana & Yogesh K. Dwivedi, 2022. "Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets," Information Systems Frontiers, Springer, vol. 24(2), pages 459-474, April.
    19. Li, Jiaman & Dong, Xiucheng & Jiang, Qingzhe & Dong, Kangyin & Liu, Guixian, 2021. "Natural gas trade network of countries and regions along the belt and road: Where to go in the future?," Resources Policy, Elsevier, vol. 71(C).
    20. Chul Woo Yoo, 2020. "An Exploration of the Role of Service Recovery in Negative Electronic Word-of-Mouth Management," Information Systems Frontiers, Springer, vol. 22(3), pages 719-734, June.

    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:54:y:2020:i:1:d:10.1007_s11135-019-00952-z. 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.