IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v12y2022i2p21582440221093327.html
   My bibliography  Save this article

#ShoutYourAbortion on Instagram: Exploring the Visual Representation of Hashtag Movement and the Public’s Responses

Author

Listed:
  • Yunhwan Kim
  • Sunmi Lee

Abstract

The goal of the current study was to explore the visual representation of #ShoutYourAbortion hashtag movement on Instagram. The photos’ content and embedded texts in the photos were examined. And the photos were clustered using k -means clustering algorithm, and the resulting clustered were compared using the same criteria above. Photo features which shows the content- and pixel-level characteristics were extracted and used for comparison between clusters. The photo features were also used to examine their relationships with the public’s responses. It was found that text was the main type of content, and the texts presented in photos were mainly about stories told in first person point of view as a woman. The photos were grouped into two clusters, which differed in terms of content and photo features. And the public’s responses were found to be related to photo features. The results are expected to contribute to the understanding of hashtag movements via photos and making photos in hashtag movements more appealing to the public.

Suggested Citation

  • Yunhwan Kim & Sunmi Lee, 2022. "#ShoutYourAbortion on Instagram: Exploring the Visual Representation of Hashtag Movement and the Public’s Responses," SAGE Open, , vol. 12(2), pages 21582440221, April.
    • Handle: RePEc:sae:sagope:v:12:y:2022:i:2:p:21582440221093327
    DOI: 10.1177/21582440221093327
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440221093327
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440221093327?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. Saeideh Bakhshi & Eric Gilbert, 2015. "Red, Purple and Pink: The Colors of Diffusion on Pinterest," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-20, February.
    2. Yunhwan Kim & Donghwi Song & Yeon Ju Lee, 2020. "#Antivaccination on Instagram: A Computational Analysis of Hashtag Activism through Photos and Public Responses," IJERPH, MDPI, vol. 17(20), pages 1-20, October.
    3. Yu-feng Huang & Feng-yang Kuo & Chia-wen Chen, 2019. "To like or Not to Like in the World of Instagram: An Eye-Tracking Investigation of Instagram Users’ Evaluation Process for Liking an Image," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph (ed.), Information Systems and Neuroscience, pages 203-210, Springer.
    4. Cappellini, Benedetta & Kravets, Olga & Reppel, Alex, 2019. "Shouting on social media? A borderscapes perspective on a contentious hashtag," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 428-437.
    5. Elena Chatzopoulou & Raffaele Filieri & Shannon Arzu Dogruyol, 2020. "Instagram and body image: Motivation to conform to the “Instabod” and consequences on young male wellbeing," Journal of Consumer Affairs, Wiley Blackwell, vol. 54(4), pages 1270-1297, December.
    6. Chunhui Yuan & Haitao Yang, 2019. "Research on K-Value Selection Method of K-Means Clustering Algorithm," J, MDPI, vol. 2(2), pages 1-10, June.
    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. Yunhwan Kim, 2022. "#Nomask on Instagram: Exploring Visual Representations of the Antisocial Norm on Social Media," IJERPH, MDPI, vol. 19(11), pages 1-14, June.
    2. Yunhwan Kim, 2023. "Exploring Organizational Self-(re)presentations on Visual Social Media: Computational Analysis of Startups’ Instagram Photos Based on Unsupervised Learning," SAGE Open, , vol. 13(4), pages 21582440231, December.
    3. Heller, Yuval & Tubul, Itay, 2023. "Strategies in the repeated prisoner’s dilemma: A cluster analysis," MPRA Paper 117444, University Library of Munich, Germany.
    4. Chao Yan & Yao Yan & Zhiyu Wan & Ziqi Zhang & Larsson Omberg & Justin Guinney & Sean D. Mooney & Bradley A. Malin, 2022. "A Multifaceted benchmarking of synthetic electronic health record generation models," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Singh, Pallavi & Bala, Hillol & Dey, Bidit Lal & Filieri, Raffaele, 2022. "Enforced remote working: The impact of digital platform-induced stress and remote working experience on technology exhaustion and subjective wellbeing," Journal of Business Research, Elsevier, vol. 151(C), pages 269-286.
    6. Cuomo, Maria Teresa & Tortora, Debora & Colosimo, Ivan & Ricciardi Celsi, Lorenzo & Genovino, Cinzia & Festa, Giuseppe & La Rocca, Michele, 2023. "Segmenting with big data analytics and Python: A quantitative exploratory analysis of household savings," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    7. Zhang, Yanquan & Chang, Ruidong & Zuo, Jian & Shabunko, Veronika & Zheng, Xian, 2023. "Regional disparity of residential solar panel diffusion in Australia: The roles of socio-economic factors," Renewable Energy, Elsevier, vol. 206(C), pages 808-819.
    8. Şebnem Koltan Yılmaz & Sibel Şener, 2022. "Analysis of The Countries According to The Prosperity Level with Data Mining," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(2), pages 85-104, December.
    9. Nanne, Annemarie J. & Antheunis, Marjolijn L. & van der Lee, Chris G. & Postma, Eric O. & Wubben, Sander & van Noort, Guda, 2020. "The Use of Computer Vision to Analyze Brand-Related User Generated Image Content," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 156-167.
    10. Yan, Min & Filieri, Raffaele & Raguseo, Elisabetta & Gorton, Matthew, 2021. "Mobile apps for healthy living: Factors influencing continuance intention for health apps," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    11. Yen, Barbara T.H. & Li, Jun-Sheng, 2022. "Route-based performance evaluation for airlines – A metafrontier data envelopment analysis approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    12. Onofrei, George & Filieri, Raffaele & Kennedy, Lorraine, 2022. "Social media interactions, purchase intention, and behavioural engagement: The mediating role of source and content factors," Journal of Business Research, Elsevier, vol. 142(C), pages 100-112.
    13. Laor, Tal, 2022. "My social network: Group differences in frequency of use, active use, and interactive use on Facebook, Instagram and Twitter," Technology in Society, Elsevier, vol. 68(C).
    14. Yu, Joanne & Egger, Roman, 2021. "Color and engagement in touristic Instagram pictures: A machine learning approach," Annals of Tourism Research, Elsevier, vol. 89(C).
    15. Jujie Wang & Zhenzhen Zhuang, 2023. "A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6225-6247, July.
    16. Ghaemi, Zahra & Tran, Thomas T.D. & Smith, Amanda D., 2022. "Comparing classical and metaheuristic methods to optimize multi-objective operation planning of district energy systems considering uncertainties," Applied Energy, Elsevier, vol. 321(C).
    17. Chen, Hao, 2022. "Cluster-based ensemble learning for wind power modeling from meteorological wind data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    18. Xinghua Wang & Xixian Liu & Fucheng Zhong & Zilv Li & Kaiguo Xuan & Zhuoli Zhao, 2023. "A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    19. Hazem Noori Abdulrazzak & Goh Chin Hock & Nurul Asyikin Mohamed Radzi & Nadia M. L. Tan & Chiew Foong Kwong, 2022. "Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network," Mathematics, MDPI, vol. 10(24), pages 1-27, December.

    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:sae:sagope:v:12:y:2022:i:2:p:21582440221093327. 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: SAGE Publications (email available below). General contact details of provider: .

    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.